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Posts from the ‘Analytics & Big Data’ Category

Oct 15

Gartner’s $953B IoT forecast puts 81% of spending in endpoints and the fastest growth in software

Gartner IoT market opportunity forecast 2024 to 2030 rising from $620.9 billion to $953.3 billion, stacked by endpoint electronics, communication services, services, infrastructure software, application software and security, with six KPI tiles for 2026 and 2030 totals, new spending, application software growth, 5G growth and the 2028 revision
IoT market opportunity reaches $953.3B by 2030, with endpoint electronics carrying 81% of it. Click any chart to open it full size. Source: Gartner (G00858825, Sept. 25, 2026), analysis by softwarestrategiesblog.com.

The new Gartner IoT forecast puts four out of every five dollars in devices, and the fastest growth in the software that runs them.

Published September 25, the forecast sizes the worldwide IoT market opportunity at $736.6 billion in 2026, up 9.6%, and $953.3 billion by 2030. That is a 7.2% compound annual growth rate from 2025 through 2030, landing just under $1 trillion.

Endpoint electronics account for $769.2 billion of the 2030 total, or 80.7%. Application software grows at 20.4% a year and infrastructure software at 14.8%. Together, the two software segments compound at 16.3%, 2.3 times the market.

The trillion-dollar mark also moved. Gartner’s previous edition, published in June 2024, put the 2028 market at $990.5 billion. The new edition puts 2028 at $850.3 billion, a $140.2 billion reduction, and does not cross $1 trillion before 2030.

I built this analysis from Gartner’s forecast report (G00858825) and the full detailed dataset behind it: 73,493 rows covering 6 technology segments, 28 subsegments, 12 sectors, 47 use cases, 9 regions and 7 years.

Each bar in the hero chart stacks Gartner’s six technology segments in current U.S. dollars. Navy is endpoint electronics, mid-blue is communication services, sky blue is services, teal is infrastructure software, purple is application software and red is security. Italic figures above each bar give year-over-year growth.

Six tiles underneath carry the 2026 and 2030 totals, the $281.0 billion in new spending between 2025 and 2030, the application software growth rate, the 5G growth rate and the 2028 revision against the 2024 edition.

Gartner IoT forecast puts 2026 at $736.6B and 2030 at $953.3B

Gartner sizes the market across six technology segments. Here is how each one moves from 2025 to 2030.

Segment 2025 2026 2030 2025-2030 CAGR 2030 share
Endpoint electronics $545.8B $600.7B $769.2B 7.1% 80.7%
Communication services $56.7B $58.8B $66.3B 3.2% 7.0%
Services $41.0B $43.9B $57.7B 7.1% 6.1%
Infrastructure software $19.9B $23.0B $39.7B 14.8% 4.2%
Application software $6.9B $8.3B $17.5B 20.4% 1.8%
Security $1.9B $2.1B $2.9B 8.6% 0.3%
Total $672.3B $736.6B $953.3B 7.2% 100%

Current U.S. dollars. CAGR and shares calculated by softwarestrategiesblog.com from Gartner’s dataset.

Growth peaks early. Gartner forecasts 9.6% in 2026 and 8.5% in 2027, then 6.4% in 2028, 6.1% in 2029 and 5.7% in 2030. Endpoint electronics drive the near-term numbers, growing 10.0% in 2026 and 8.7% in 2027 before slowing to 5.1% by 2030.

Software moves the other way. Application software growth rises from 20.2% in 2026 to 21.8% in 2030, and security growth climbs from 8.2% to 9.4%.

Endpoints add $223.4B, application software grows 2.8x faster

The market adds $281.0 billion between 2025 and 2030. Endpoint electronics capture $223.4 billion of it, or 79.5%.

The bubble chart below rebuilds Gartner’s own segment view from the dataset. The horizontal axis is growth rate, the vertical axis is dollars added, and bubble size is 2030 market opportunity.

Bubble chart of Gartner IoT technology segments comparing 2025 to 2030 CAGR with dollars added: endpoint electronics adds $223.4 billion at 7.1%, application software grows 20.4% and infrastructure software 14.8%
Segment CAGR versus dollars added, 2025 to 2030. Bubble size is 2030 market opportunity. Rebuilt from Gartner’s segment data. Source: Gartner (G00858825, Sept. 25, 2026), analysis by softwarestrategiesblog.com.

Application software grows 2.8 times faster than the market but adds only $10.6 billion. Infrastructure software adds $19.8 billion at 14.8%. Software’s share of IoT spending rises from 4.0% in 2025 to 6.0% in 2030.

Gartner ties the software growth to connecting devices with enterprise systems, predictive maintenance, operational optimization and digital twin projects that run on IoT platforms. Software, services and security together climb from 10.4% of spending in 2025 to 12.4% in 2030.

That is the strategic split in this forecast. Hardware sets the size of the market. Software, services and security decide who captures recurring value once the devices are installed.

Gartner cut $140.2B from its 2028 forecast

Gartner’s Table 2 compares this edition with the June 2024 edition, which ran through 2028. The new forecast is $113.8 billion lower for 2025 and $140.2 billion lower for 2028.

Gartner 2028 IoT forecast by segment, June 2024 edition versus September 2026 edition, showing the total cut from $990.5 billion to $850.3 billion, led by endpoint electronics and services
2028 IoT market opportunity, June 2024 edition versus September 2026 edition. Prior-edition values derived from Gartner’s Table 2. Source: Gartner (G00858825, Sept. 25, 2026), analysis by softwarestrategiesblog.com.

Endpoint electronics took the largest dollar cut, $102.6 billion in 2028. Its growth rates went up anyway: 8.9% to 10.0% in 2026, 7.0% to 8.7% in 2027 and 4.8% to 6.1% in 2028. A lower starting base, growing faster.

Services took the deepest percentage cut. The 2028 services forecast fell 37.6%, from $80.5 billion to $50.2 billion, with growth rates lowered 11 to 16 points in every year from 2025 through 2028.

Application software, infrastructure software and services all lost growth in the revision. Gartner says enterprise spending on software, security and services “is expected to develop more cautiously as organizations become more selective in scaling and expanding IoT environments.”

Security is the exception on dollars. Its 2028 forecast rose $665 million, or 37.3%, and Gartner lifted its 2028 growth rate from 6.7% to 8.6%.

Total 2028 growth moved up slightly, from 6.2% to 6.4%. The level is lower; the slope is about the same.

Automotive and manufacturing hold 55% of 2030 spending

Automotive is the largest IoT sector at $322.1 billion in 2030. Manufacturing and natural resources follows at $198.9 billion. Together they account for 54.6% of the 2030 market.

Gartner 2030 IoT market opportunity by sector, led by automotive at $322.1 billion and manufacturing and natural resources at $198.9 billion, with 2025 to 2030 CAGR for each sector
2030 IoT market opportunity by sector, with 2025 to 2030 CAGR and dollars added. Source: Gartner (G00858825, Sept. 25, 2026), analysis by softwarestrategiesblog.com.

Manufacturing is the growth story among the large sectors. It grows from $119.6 billion in 2025 to $198.9 billion in 2030, a 10.7% CAGR, adding $79.3 billion. Gartner expects it to grow 13.4% in 2026 and 15.0% in 2027.

Transportation reaches $109.8 billion at a 10.0% CAGR, with 16.0% growth in 2026 and 14.0% in 2027. Smart buildings, the fourth-largest sector at $107.2 billion, grows just 2.8% a year.

Inside manufacturing, software and services matter more than in the market overall. Application and infrastructure software reach $33.4 billion in the sector by 2030, 16.8% of its spending, compared with 6.0% for IoT overall. Services add another $32.3 billion.

Connected cars and factories add the most, EV charging grows fastest

Consumer connected cars remain the single largest use case, reaching $219.8 billion in 2030 and adding $58.3 billion. Manufacturing process automation adds $34.9 billion and air transport adds $28.8 billion.

Top 10 IoT use cases by dollars added from 2025 to 2030, led by consumer connected cars at $58.3 billion, and top 10 use cases by 2028 to 2030 CAGR, led by EV charging at 18.6%
Left: use cases adding the most dollars, 2025 to 2030. Right: fastest-growing use cases, 2028 to 2030 CAGR. Source: Gartner (G00858825, Sept. 25, 2026), analysis by softwarestrategiesblog.com.

Gartner names EV charging, manufacturing execution and management and connected products as the fastest-growing use cases from 2028 to 2030, at 18.6%, 12.0% and 11.3% CAGRs. The dataset also shows retail customer communication growing 12.9% over the same window, on a much larger $20.4 billion base.

Manufacturing execution and management is the standout among large use cases. It more than doubles, from $15.7 billion in 2025 to $32.8 billion in 2030, a 15.8% CAGR. Gartner attributes the growth to real-time control and optimization requirements on the factory floor.

Four use cases shrink. Connected lighting falls from $15.6 billion in 2025 to $7.5 billion in 2030. Automotive insurance declines from $6.4 billion to $4.3 billion, outdoor surveillance from $7.4 billion to $6.9 billion, and commercial automotive aftermarket from $2.1 billion to $1.5 billion.

5G grows 4.7x while 4G and below shrink

Communication services is the slowest-growing segment at 3.2% a year, but the mix inside it changes fast.

IoT communication services by connectivity type 2024 to 2030: 5G rises from $1.3 billion to $10.1 billion, satellite from $6.9 billion to $13.2 billion, while 4G and below falls to $38.5 billion
IoT communication services by connectivity type, 2024 to 2030. Source: Gartner (G00858825, Sept. 25, 2026), analysis by softwarestrategiesblog.com.

IoT 5G connectivity grows from $2.2 billion in 2025 to $10.1 billion in 2030, a 36.2% CAGR. Satellite grows from $7.8 billion to $13.2 billion at 11.0%. Cellular 4G and below, still the largest connectivity line, falls from $43.0 billion to $38.5 billion.

Gartner links connectivity to digital thread solutions that connect IT, OT and product life cycle data across the industrial value chain, the data foundation digital twins need.

Security is 0.3% of IoT spending

Gartner’s IoT security segment grows from $1.9 billion in 2025 to $2.9 billion in 2030. That is 0.3% of the 2030 market, against $769.2 billion in endpoints.

IoT software subsegments ranked by 2025 to 2030 CAGR, led by analytic platforms at 24.5% and security software at 21.2%, with Gartner's IoT security segment at 0.3% of 2030 spending
IoT software subsegments by 2025 to 2030 CAGR. Security software sits inside Gartner’s infrastructure software segment. Source: Gartner (G00858825, Sept. 25, 2026), analysis by softwarestrategiesblog.com.

Security software, which Gartner counts inside infrastructure software, is the second-fastest-growing software subsegment at 21.2% a year, reaching $7.4 billion. Add it to the security segment and IoT security reaches $10.3 billion by 2030, still 1.1% of the market.

Analytic platforms lead software growth at 24.5%, reaching $9.6 billion. Database management systems grow 19.2% to $10.0 billion. IoT platforms, the middleware layer Gartner credits with connecting devices to enterprise systems, grow 12.5% to $14.0 billion.

For security leaders, the gap is the point. Every connected robot, vehicle, meter and controller extends the OT attack surface, and spending to secure those endpoints grows at about a penny per dollar of IoT spend, even counted broadly. For the broader security market context, see Gartner’s $247.5B security forecast makes post-quantum firewall refreshes the only upgrade through 2030.

North America, China and Europe hold 75% of 2030 spending

North America is the largest region at $260.5 billion in 2030, followed by China at $229.9 billion and Europe at $224.2 billion. Together they hold 74.9% of the market.

Gartner 2030 IoT market opportunity by region: North America $260.5 billion, China $229.9 billion and Europe $224.2 billion, with Emerging Asia Pacific growing fastest at 14.7% CAGR
2030 IoT market opportunity by region, with 2025 to 2030 CAGR and share. Source: Gartner (G00858825, Sept. 25, 2026), analysis by softwarestrategiesblog.com.

North America grows slowest of the three, at 5.8% a year. Emerging Asia Pacific grows fastest, at 14.7%, reaching $53.0 billion. Sub-Saharan Africa grows 12.9% and Latin America 11.9%, both from small bases.

Gartner’s report also flags the war in the Middle East. It expects disruptions to the Strait of Hormuz to continue into 2027, with energy prices more than 50% above pre-war levels. Gartner expects the main near-term impact on IT spending to be a rebalancing of sourcing, vendor relationships and regional exposure rather than a material spending reduction.

A reading note on Gartner’s Table 2

The growth-rate columns in Gartner’s Table 2 are labeled 2025 GR, 2027 GR, 2028 GR and 2029 & 2030 GR. The values line up with 2025, 2026, 2027 and 2028.

The endpoint electronics changes of 1.1, 1.7 and 1.3 points match the revisions Gartner describes in its text for 2026, 2027 and 2028. I read the table that way throughout this analysis.

Gartner’s text also says the highest growth by 2030 comes from communications, media and services and transportation. In the dataset, communications, media and services is a $1.8 billion sector growing 11.0% a year, and transportation’s 10.0% CAGR trails manufacturing’s 10.7%.

What technology leaders should do with this forecast

  • Plan for AI-ready endpoints. Gartner credits endpoint growth to connected products, connected vehicles and AI-ready, edge-intelligent devices, including robots and industrial equipment. Device refresh plans should assume edge inference.
  • Budget for the software layer, not just devices. Software grows 16.3% a year against 7.1% for endpoints. Integration, analytics and IoT platforms are where projects stall or pay off.
  • Close the security gap early. IoT security, even counted broadly, is 1.1% of 2030 spending. Asset discovery, identity, segmentation and secure updates belong in the IoT business case.
  • Retire 4G-dependent fleets on a schedule. 4G and below shrinks every year from 2026 while 5G grows 36.2% a year.
  • Treat manufacturing execution as the growth zone. At 15.8% a year, it is the fastest-growing large use case in the forecast.

Frequently asked questions

How big is the IoT market according to Gartner? Gartner forecasts worldwide IoT market opportunity of $736.6 billion in 2026 and $953.3 billion in 2030, a 7.2% CAGR from 2025.

Which IoT segment is largest? Endpoint electronics, at $769.2 billion in 2030, or 80.7% of the market.

Which IoT segment grows fastest? Application software, at a 20.4% CAGR from 2025 to 2030, reaching $17.5 billion.

Did Gartner lower its IoT forecast? Yes. The 2028 forecast fell from $990.5 billion in the June 2024 edition to $850.3 billion, a $140.2 billion reduction, while 2028 growth edged up from 6.2% to 6.4%.

How fast is IoT 5G growing? IoT 5G connectivity grows at a 36.2% CAGR, from $2.2 billion in 2025 to $10.1 billion in 2030.

How I built this analysis

All market sizes are Gartner IoT market opportunity in current U.S. dollars from the detailed dataset behind G00858825, which reconciles to Gartner’s Table 1 totals. Growth rates for years and segments match Gartner’s published figures.

CAGRs for sectors, regions, subsegments and use cases, dollar additions, shares and the combined IoT security figure are my calculations. June 2024 edition values are derived by subtracting Gartner’s Table 2 dollar changes from the new forecast.

Earlier analysis in this series:

This post is my personal analysis of Gartner’s IoT research and does not represent my employer.

Sources

Oct 8

Gartner’s $812.6B app software forecast sends 38% of new spending to CRM as agentic AI targets $116B of legacy apps

Gartner enterprise application software forecast 2024 to 2030 rising from $384.8 billion to $812.6 billion, stacked by CRM, ERP, other application software, email and authoring, analytic platforms, supply chain management, content services and project and portfolio management, with six KPI tiles
Gartner’s 3Q26 enterprise application software forecast by market, 2024-2030, with six tiles on the numbers that matter most. Click any chart to open it full size. Source: Gartner (G00862072, Sept. 22, 2026), analysis by softwarestrategiesblog.com.

Enterprise application software spending reaches $496.3 billion in 2026 and $812.6 billion by 2030, according to the latest Gartner application software forecast. That is a 12.8% compound annual growth rate in constant currency, revised up from 12.7% last quarter.

The headline growth rate hides a sharper story. CRM alone captures $119.2 billion, or 38%, of the $316.2 billion the market adds between 2026 and 2030. In the same report, Gartner says cross-functional agentic AI will cannibalize $116 billion from traditional application software spending by 2030.

Those two numbers are close in size, and they point to the same conclusion. Most of the new money in application software goes to the markets where agentic AI is proving value first. Legacy suites that do not embed agents compete for a shrinking slice.

I built this analysis from Gartner’s Forecast: Enterprise Application Software, Worldwide, 2024-2030, 3Q26 (G00862072), published September 22, 2026. All market, subsegment and regional figures come from the 7,567-row dataset Gartner publishes with the report. The shares, dollars added and comparisons are my calculations.

Agentic AI is the growth engine and the threat

Gartner says cumulative spending between 2026 and 2030 will exceed $3.2 trillion, “fueled by the transition from Generative AI to Agentic AI.” The dataset totals $3,227 billion over those five years.

Gartner expects business leaders to favor applications with embedded agentic capabilities, and vendors to compete on how much functionality they deliver through agents. It warns that “legacy offerings risk rapid obsolescence.”

Early value is already showing up in three segments, according to Gartner: CRM, analytics and human capital management (HCM). Adoption starts inside functional silos such as marketing, sales and customer service, where use cases are maturing. Cross-functional integration follows once use cases prove out, the technology matures and enterprises build trust.

That second phase is where the $116 billion comes from. Gartner covers it in a separate report, Spending on Cross-Functional Agentic AI Will Cannibalize $116 Billion of Spending From Application Software Markets by 2030.

CRM takes the largest share of new spending

Bubble chart of eight enterprise application software markets comparing 2025 to 2030 CAGR with dollars added from 2026 to 2030, led by CRM adding $119.2 billion and analytic platforms growing 14.5%
Each market’s 2025-2030 CAGR against dollars added from 2026 to 2030. Bubble size shows 2030 spending. Source: Gartner (G00862072), analysis by softwarestrategiesblog.com.

CRM grows from $170.5 billion in 2026 to $289.8 billion in 2030 at a 13.8% CAGR. Its share of the total market rises from 34.4% to 35.7%.

Analytic platforms post the fastest market-level growth at 14.5%, reaching $73.8 billion. Email and authoring grows 13.6% to $77.2 billion, and supply chain management grows 13.2% to $71.5 billion. All three beat the market average.

ERP, other application software, content services and project and portfolio management all grow below 12%. Together they still add $106.5 billion, but each loses share.

Market 2025 2026 2030 CAGR 2025-2030 Added 2026-2030 Share of new spending
CRM $149.1B $170.5B $289.8B 13.8% +$119.2B 37.7%
ERP $73.5B $83.0B $130.9B 11.6% +$47.9B 15.1%
Other application software $74.6B $83.5B $128.6B 10.9% +$45.1B 14.3%
Analytic platforms $36.7B $42.3B $73.8B 14.5% +$31.5B 9.9%
Email and authoring $40.1B $47.0B $77.2B 13.6% +$30.3B 9.6%
Supply chain management $37.6B $42.8B $71.5B 13.2% +$28.7B 9.1%
Content services $18.9B $21.3B $31.9B 10.5% +$10.6B 3.4%
Project and portfolio management $5.3B $5.9B $8.8B 10.2% +$2.9B 0.9%
Total $435.8B $496.3B $812.6B 12.8% +$316.2B 100%

Spending in current U.S. dollars. CAGRs in constant currency. Dollars added and shares calculated by Software Strategies Blog from Gartner’s 3Q26 dataset (G00862072).

Cross-CRM and decision intelligence lead 23 subsegments

All 23 enterprise application software subsegments ranked by 2025 to 2030 CAGR, led by decision intelligence platforms at 20.8% and cross-CRM at 18.9%, with core manufacturing and operations management slowest at 6.7%
All 23 subsegments ranked by 2025-2030 CAGR in constant currency, with 2030 spending. Colors mark the parent market. Source: Gartner (G00862072), analysis by softwarestrategiesblog.com.

Decision intelligence platforms grow fastest, at 20.8%, and more than double from $2.7 billion in 2026 to $5.7 billion in 2030. The subsegment is small, but it sits closest to the agentic pattern Gartner describes: software that recommends or takes decisions rather than reporting on them.

Cross-CRM is the more important signal. It grows 18.9% a year and doubles from $27.5 billion to $55.7 billion. That is $28.1 billion of new spending, or 24% of CRM’s total gain. Cross-CRM is the closest thing in this dataset to the cross-functional integration Gartner expects agentic AI to drive.

Customer service and support remains the largest CRM subsegment, reaching $89.7 billion at 13.1%. Marketing reaches $68.1 billion at 12.8%. Sales grows slowest inside CRM, at 11.8%, to $50.2 billion.

Analytics and BI platforms grow 15.0% to $49.4 billion. Within supply chain management, orchestration platforms (14.3%) and procurement (14.2%) lead.

HCM carries ERP

ERP is the market where the subsegment split matters most. HCM grows 13.5% and reaches $78.5 billion by 2030, 60% of all ERP spending. It accounts for $31.7 billion, or 66%, of ERP’s $47.9 billion gain.

The rest of ERP grows slowly. Financial management systems components grow 9.7% to $41.8 billion. Enterprise asset management grows 8.0% to $4.6 billion. Core manufacturing and operations management grows 6.7% to $6.0 billion, the slowest of all 23 subsegments.

That fits Gartner’s list of where early agentic value is showing up. HCM made the list. Finance, asset management and manufacturing operations did not.

North America takes 57% of new spending

2030 enterprise application software spending by region, led by North America at $461.9 billion and 56.8% share, with Emerging Asia Pacific growing fastest at 19.2% CAGR
2030 spending by region with 2025-2030 CAGR and share of the global market. Source: Gartner (G00862072), analysis by softwarestrategiesblog.com.

North America reaches $461.9 billion in 2030, 56.8% of worldwide spending, and grows 13.1% a year. It captures $180.9 billion, or 57.2%, of the market’s new spending.

Europe grows 10.2%, the slowest of any region, and its share drops from 23.3% in 2026 to 22.0% in 2030.

The fastest growth is in Asia. Emerging Asia/Pacific grows 19.2% to $25.9 billion, and China grows 17.1% to $37.3 billion. Together they add $30.7 billion and lift their combined share from 6.6% to 7.8%.

What changed in the Gartner application software forecast

Change in 2030 enterprise application software spending in Gartner's 3Q26 forecast versus 2Q26 by market, led by content services up $1.734 billion and email and authoring up $1.719 billion
Change in 2030 spending by market vs. Gartner’s 2Q26 forecast. Dollar changes include exchange-rate effects. Source: Gartner (G00862072, Table 2), analysis by softwarestrategiesblog.com.

Gartner revised growth for only two markets. Email and authoring rose 0.5 to 0.6 percentage points a year. Content services rose 0.2 to 2.0 points a year, including 1.4 points in 2026. Gartner says growth trajectories for every other market are unchanged.

In U.S. dollars, the total forecast is $657 million lower for 2026 and $2.5 billion lower for 2027 than in 2Q26, then $3.3 billion higher by 2030. The 2030 gains are concentrated in content services (+$1.73 billion) and email and authoring (+$1.72 billion). Exchange rates explain the remaining dollar movement in markets where growth rates did not change.

Gartner also repeats a note on the war in the Middle East. It expects disruption to the Strait of Hormuz to continue into 2027 and energy prices to stay more than 50% above pre-war levels. Gartner expects the main near-term IT spending impact to be a rebalancing of sourcing strategies, vendor relationships and regional exposure, not a material reduction in spending.

A reading note

Gartner reports growth in constant currency, using 2024 exchange rates as the base. Market sizes are in current U.S. dollars, so 2026 spending rises 13.9% in dollars but 12.7% in constant currency. I use constant-currency CAGRs throughout and dollar values for sizes, dollars added and shares.

The $116 billion cannibalization figure comes from a separate Gartner report cited in this forecast. I compare it with CRM’s dollar gain to show scale, not to suggest that one offsets the other.

What software leaders should do

  • Follow the dollars, not the CAGR. CRM, ERP and other application software account for 67% of new spending. Analytics grows faster but adds less than a third as much as CRM.
  • Treat cross-CRM as the agentic beachhead. It doubles to $55.7 billion and sits where Gartner expects cross-functional agents to spread first.
  • Separate HCM from the rest of ERP. HCM delivers two-thirds of ERP’s growth. Finance, asset management and manufacturing operations vendors need an agentic story to avoid sliding below 10% growth.
  • Plan for cannibalization inside your own portfolio. Gartner’s $116 billion estimate means agentic AI will replace spending as well as add to it.
  • Weight go-to-market toward North America and fast-growing Asia. North America takes 57% of the gain. Emerging Asia/Pacific and China grow fastest. Europe grows slowest.

Frequently asked questions

How large is the enterprise application software market in 2026? Gartner forecasts $496.3 billion in end-user spending in 2026, up 12.7% in constant currency.

How large will it be in 2030? Gartner forecasts $812.6 billion in 2030, a 12.8% CAGR in constant currency from 2025 to 2030.

Which market is largest? CRM, at $289.8 billion in 2030, or 35.7% of the total.

Which segment grows fastest? Decision intelligence platforms, at a 20.8% CAGR. Among the eight markets, analytic platforms grow fastest at 14.5%.

How will agentic AI affect application software spending? Gartner says agentic AI drives growth, with early value in CRM, analytics and HCM. It also says cross-functional agentic AI will cannibalize $116 billion from traditional application software spending by 2030.

How I built this analysis

Market totals, growth revisions and quotes come from Gartner’s 3Q26 enterprise application software forecast (G00862072). Subsegment and regional figures come from the dataset Gartner publishes with the report, which covers eight markets, 23 subsegments, nine regions and 2024 to 2030. I calculated constant-currency CAGRs from 2025 to 2030, dollars added from 2026 to 2030, and market and regional shares. Charts are my own work from that data.

Earlier analysis in this series:

This post is my personal analysis of Gartner’s research and does not represent my employer.

Sources

Apr 19

FinancialForce Unleashes Spring ’23 Release, Strengthening Opportunity-to-Renewal

Finding new ways to improve opportunity-to-renewal is core to any services business’s growth.

FinancialForce has long bet its business on the belief that it could streamline opportunity-to-renewal for people- and software-centered businesses better than any other vendor. In delivering their Spring ’23 release, they’re proving how adept they are at delivering new features on a faster release cadence of three major releases a year. Out of its workforce of 1,000 people, FinancialForce has 400 full-time employees in DevOps, engineering, product management, and quality and nearly 100 outside resources in R&D.

FinancialForce’s overarching goal with the Spring ’23 release is to strengthen the customer’s ability to excel at opportunity-to-renewal. The feature refresh for Spring ’23 includes 18 different areas of their platform, with the most, eight, being in Services CPQ. Dan Brown, Chief Product, and Strategy Officer at FinancialForce, says, “Opportunity-to-renewal is core to companies that deliver services. It’s an area that has been dramatically underserved by classic vendors in this space. Most are fairly product-centric, and that tends to hold companies that are service-oriented back.”

Services-as-a-Business is gaining traction

FinancialForce’s Spring ’23 release shows how Services-as-a-Business is closing gaps and improving the opportunity-to-renewal process. Tight labor markets, spiraling costs and prices due to inflation, and blind spots in opportunity-to-renewal cycles continually jeopardize services revenue. As a result, professional services and software companies relying on service revenue risk losing Annual Recurring Revenue (ARR) and seeing reduced Customer Lifetime Value for every account. The Spring ’23 release provides a more granular, 360-degree view across eight core areas of the opportunity-to-renewal process to help services businesses meet new growth challenges.

“Our new Spring ’23 release is designed to give organizations the kind of certainty they need in these very uncertain economic times,” said Scott Brown, President, and Chief Executive Officer at FinancialForce. “Given the pace at which market and business conditions change, services businesses need confidence in their ability to manage estimates, skills and resources, and solve complex problems. This new release gives organizations a complete, customer-centric view of their business to turn continuous disruption into a competitive edge.”

FinancialForce Spring '23 Release

FinancialForce’s Spring ’23 release doubles down in the areas of Service CPQ and Resource Management, which are the areas where the majority of new features have been added in the Spring ’23 release.

Improving Services CPQ process performance protects margins

FinancialForce is prioritizing Services CPQ, first introduced in the Winter ’22 release, to help customers get more in control of their margins and time management. The number and depth of new features in this area and Dan Brown’s insights into how popular Services CPQ has become with enterprise accounts demonstrate that prioritization. FinancialForce’s enterprise accounts are adopting Services CPQ to save time during sales cycles by providing their prospects with the visibility to identify resources available for quoting work, their billable rate, skills, and previous experience.

Dan Brown said that “in (quote) estimation, you now can reach into your PSA (Professional Services Automation) system and identify the resource that you’re going to quote, what’s their billable rate, what’s their skills, what’s their capabilities. A big issue our customers have is that the As Quoted versus the As Delivered are almost always materially very different.”

He continued, emphasizing, “And that’s where you end up with margin erosion, that’s where you end up with revenue leakage for our customers. Now with Services CPQ, the As Quoted and As Delivered features are tightly linked together. And that has driven enormous improvements.”

Scott Brown added, “When I was a customer, this was a big pain point. For me, the capability to connect your pre-sales activities to your post-sale delivery is a real game changer for us.”

Underscoring how vital Services CPQ is to FinancialForce’s opportunity-to-renewal strategy, the Spring ‘23 Customer Overview notes that “with usability improvements in Services CPQ, support for additional pricing and costing scenarios, and streamlined estimate export for correct Statements of Work, services teams will be able to create accurate and competitive proposals faster, leading to higher win rates on projects, with much lower risk profiles.”

FinancialForce Unleashes Spring '23 Release, Strengthening Opportunity-to-RenewalAmong the many enhancements to Services CPQ are usability enhancements to the Estimate Builder, helping to reduce errors in As Quoted and As Delivered Results.

New features to optimize resources and projects

Additional goals of the spring ’23 release are to provide customers with improved workflows for optimizing resources and streamlining project management. Given how every professional services firm and software company today is under pressure to continually find new ways to optimize resources and be more done with less, the timing of Resource Optimizer Enhancements and introducing Resource Manager Work Planner is excellent. FinancialForce allows assigning multiple resources to project enhancements, integrating with MS Outlook and Google Calendar, as well as mass deletion of pass utilization results. FinancialForce also delivers task-based scheduling of held resource requests.

FinancialForce Unleashes Spring '23 Release, Strengthening Opportunity-to-Renewal

The Spring ’23 release is designed to help enterprises optimize resources from small-scale to multi-location projects by adding Resource Work Planner and Enhanced Skills Maintenance that can scale across multiple global locations.

How FinancialForce’s Spring ’23 Release Strengthens Opportunity-to-Renewal

“This new release gives organizations a complete, customer-centric view of their business to turn continuous disruption into a competitive edge,” remarked Scott Brown during a recent briefing. FinancialForce aims to help services businesses more efficiently monetize their time and resources by concentrating their development efforts across opportunity-to-renewal.

The release shows how services companies are looking to real-time financial analytics, including new risk management features, as guardrails to keep their businesses on track to margin and profit goals. The Spring ’23 release shows FinancialForce’s view of the opportunity-to-renewal process and what strengths it can offer customers, from a new Scheduling Risk Dashboard that provides early intervention and project course corrections in real time, to streamlined estimate exports for accurate Statements of Work (SOWs).

The following table uses the opportunity-to-renewal process as a framework to put the new release into context. It compares each phase of the opportunity-to-order process, how FinancialForce defines their role, how the Spring ’23 release strengthens each area, what the people and software-oriented benefits are, along with their leading customer references. You can also download a copy of the Opportunity-to-Renewal Process comparison here.

FinancialForce Spring '23 Release

Apr 8

Five Ways AI Can Help Create New Smart Manufacturing Startups

smart manufacturing, AI, machine learning

AI and machine learning’s potential to drive greater visibility, control, and insight across shop floors while monitoring machines and processes in real-time continue to attract venture capital. $62 billion is now invested in 5,396 startups concentrating on the intersection of AI, machine learning, manufacturing, and Industry 4.0, according to Crunchbase.

PwC’s broader tech sector analysis shows a 30% year-over-year growth in funding rounds that reached $293.2 billion in 2021. Smart manufacturing startups are financed by seed rounds at 52%, followed by early-stage venture funding at 33%. The median last funding amount was $1.6 million, with the average being $9.93 million.

 Abundant AI startup opportunities in smart manufacturing and industry 4.0 

According to Gartner, “The underlying concept of Industry 4.0 is to connect embedded systems and smart production facilities to generate a digital convergence between industry, business, and internal functions and processes.” As a result, Industry 4.0 is predicted to grow from $84.59 billion in 2020 to $334.18 billion by 2028. AI and machine learning adoption in manufacturing are growing in five core fields: smart production, products and services improvements, business operations and management, supply chain, and business model decision-making. Deloitte’s survey on AI adoption in manufacturing found that 93% of companies believe AI will be a key technology to drive growth and innovation.

Machine intelligence (MI) is one of the primary catalysts driving increased venture capital investment in smart manufacturing. Startup CEOs and their customers want AI and machine learning models based on actual data, and machine intelligence is helping to make that happen. An article by McKinsey & Company provides valuable insights into market gaps for new ventures. McKinsey’s compelling data point is that those leading companies using MI achieve 3X to 4X the impact of their peers. However, 92% of leaders also have a process to track incomplete or inaccurate data – which is another market gap startups need to fill.

AI, Industry 4, smart manufacturing

McKinsey and the Massachusetts Institute of Technology (MIT) collaborated on a survey to identify machine intelligence leaders’ KPI gains relative to their peers. They found that leaders achieve efficiency, cost, revenue, service, and time-to-market advantages. Source: Toward smart production: Machine intelligence in business operations, McKinsey & Company. February 1, 2022.

Based on the uplift MI creates for new smart manufacturing startup funding and the pervasive need manufacturers have to improve visibility & control across shop floors, startups have many potential opportunities. The following are five that AI and machine learning is helping to create:

  1. AI-enabled Configure, Price, and Quote (CPQ) systems that can factor in supply chain volatility on product costs are needed. Several startups are already using AI and machine learning in CPQ workflows, and they compete with the largest enterprise software providers in the industry, including Salesforce, SAP, Microsoft, and others. However, no one has taken on the challenge of using AI to factor in how supply chain volatility changes standard and actual costs in real-time. For example, knowing the impact of pricing changes based on an allocation, how does that impact standard costs per unit on each order? Right now, an analyst needs to spend time doing that. AI and machine learning could take on that task so analysts could get to the larger, more complex, and costly supply chain problems impacting CPQ close rates and revenue.
  2. Using AI-enabled real-time data capture techniques to identify anomalies in throughput as an indicator of machine health. The aggregated data manufacturing operations produced every day holds clues regarding each machine’s health on the shop floor. Automated data capture can identify scrap rates, yield rates and track actual costs. However, none of them can analyze the slight variations in process flow product outputs to warn of possible machine or supply chain issues. Each process manufacturing machine runs at its cadence or speed, and having an AI-based sensor system track and analyze why speeds are off could save thousands of dollars in maintenance costs and keep the line running. In addition, adding insight and intelligence to the machine’s real-time data feeds frees quality engineers to concentrate on more complex problems.
  3. Industrial Internet of Things (IIoT) and edge computing data can be used for fine-tuning finite scheduling in real-time. Finite scheduling is part of the broader manufacturing systems organizations rely on to optimize shop floor schedules, machinery, and staff scheduling. It can be either manually intensive or automated to provide operators with valuable insights. A potential smart manufacturing opportunity is a finite scheduler that relies on AI and machine learning to keep schedules on track and make trade-offs to ensure resources are used efficiently. Finite schedulers also need greater accuracy in factoring in frequent changes to delivery dates. AI and machine learning could drive greater on-time delivery performance when integrated across all the shop floors a manufacturer relies on.
  4. Automated visual inspections and quality analysis to improve yield rates and reduce scrap. Using visual sensors to capture data in real-time and then analyze them for anomalies is in its nascent stages of deployment and growth. However, this is an area where captured data sets can provide machine learning algorithms with enough accuracy to identify potential quality problems on products before they leave the factory. Convolutional neural networks are an effective machine learning technique for identifying patterns and anomalies in images. They’re perfect for the use case of streamlining visual inspection and in-line quality checks in discrete, batch, and process manufacturing.
  5. Coordinated robotics (Cobots) to handle assemble-to-order product assembly. The latest cobots can be programmed to stay in sync with each other and perform pick, pack, ship, and place materials in warehouses. What’s needed are advanced cobots that can handle simple product assembly at a more competitive cost as manufacturers continue to face chronic labor shortages and often run a shift with less than half the teams they need.

Talent remains an area of need 

Manufacturers’ CEOs and COOs say that recruiting and retaining enough talent to run all the production shifts they need is the most persistent issue. In addition, those manufacturers located in remote regions of the world are turning to robotics to fulfill orders, which opens up opportunities for integrating AI and machine learning to enable cobots to complete assemble-to-order tasks. The unknown impact of how fast supply chain conditions change needs work from startups, too, especially in tracking actual cost performance. These are just a few opportunities for startups looking to apply AI and machine learnings‘ innate strengths to solve complex supply chain, manufacturing, quality management, and compliance challenges.

Apr 6

LinkedIn Best Companies To Work For In 2022 Dominated Again By Tech

LinkedIn

Amazon’s Sunnyvale, CA Campus (source: Istockphoto)

  • Tech leaders are six of LinkedIn’s top ten companies to grow your career in 2022.
  • Amazon is the again highest rated company, followed by Alphabet (2nd), IBM (6th), AT&T (7th), Apple (9th), and Comcast (10th).
  • 19 of the 50 top companies in the U.S. are in the tech industry, including Dell, Intel, Oracle, Salesforce, Cisco, and others.
  • LinkedIn identified four key trends in their analysis, with flexible work is becoming table stakes for recruiting and retaining employees.

These and many other insights are from LinkedIn Top Companies 2022: The 50 best workplaces to grow your career in the U.S., published today. All 50 companies are currently hiring and have over 530,000 jobs open across the U.S, with over 70,000 being remote positions. The LinkedIn analysis of the best companies to grow your career spans 35 global markets, including the U.S., Canada, Mexico, Brazil, Argentina, Colombia, Chile, Ireland, France, Switzerland, Austria, Germany, Israel, Italy, Spain, the U.K., Sweden, Belgium, Denmark, the Netherlands, Portugal, India, Japan, Singapore, Philippines, Malaysia, Indonesia, Australia, New Zealand, UAE, Egypt, Saudi Arabia, South Africa, Nigeria, and Kenya.

LinkedIn’s Top Companies 2022 spotlights the organizations investing in employee success and career development. LinkedIn’s methodology and internal analysis ranked companies based on seven pillars that display career progression: ability to advance, skills growth, company stability, external opportunity, company affinity, gender diversity, and educational background.

The 19 Best Tech Companies To Grow Your Career In 2022

The following are profiles of the top 19 tech companies hiring in the U.S. today with links to available positions accessible via LinkedIn:

Amazon

Amazon is the parent company of Whole Foods Market, Zappos, Twitch, PillPack, and others.

Global headcount: 1,600,000 (with 1,100,000 in the U.S.) | Top U.S. locations: Seattle, San Francisco Bay Area, New York City | Most notable skills: Warehouse Operations, Data Entry, AWS Lambda | Most common job titles: Software Engineer, Fulfillment Associate, Warehouse Associate | Largest job functions: Operations, Engineering, Program and Project Management | What you should know: Even as the country’s second-largest private employer, Amazon continues to compete in recruiting and retaining top talent amid a competitive labor market. The company recently announced that it’s doubling its maximum base salary for corporate and tech workers, and it raised average wages for warehouse workers late last year, increasing pay for more than half a million of its employees. But the e-commerce giant is going beyond compensation, too: investing $1.2 billion over the next three years to expand its education and skills training initiatives. Amazon now pays 100% of college tuition for frontline employees as part of its Career Choice program and covers high school diploma programs, GEDs, and English proficiency certifications.

See jobs at Amazon

Alphabet

Alphabet is the parent company of Google, YouTube, Fitbit, Waymo, Verily, and others.

Global headcount: 156,000 | Top U.S. locations: San Francisco Bay Area, New York City, Seattle | Most notable skills: Video Editing and Production, Google Cloud Platform (GCP), C++ | Most common job titles: Software Engineer, Program Manager, Product Manager | Largest job function: Engineering, Information Technology, Program and Project Management | What you should know: It’s been a big year for Alphabet: The company onboarded nearly 6,500 employees last quarter and saw significant growth across Google’s Cloud service and YouTube (whose revenues are now growing at a faster rate than Netflix). For those interested in flexibility, the tech giant has a robust offering. In addition to adopting a hybrid work model, the company told LinkedIn that Alphabet offers four ‘work from anywhere’ weeks per year, sabbaticals for long-term employees, and ‘no meeting’ days. But Alphabet has also worked to maintain a collaborative culture and support career growth while working remotely. Employees can take advantage of resource groups like Women@Google and its Googler-to-Googler training, which lets its workers get first-hand knowledge across different fields from other employees.

See jobs at Alphabet

IBM

IBM is the parent company of Red Hat, SoftLayer Technologies, Truven Health Analytics, and others.

Global headcount: 250,000 | Top U.S. locations: New York City; Raleigh-Durham, N.C.; San Francisco Bay Area | Most special skills: Kubernetes, Openshift, Hybrid Cloud | Most common job titles: Software Engineer, Project Manager, Data Scientist | Largest job functions: Engineering, Information Technology, Sales | What you should know: The perennial IT giant has re-upped its benefits offerings amid the Great Reshuffle, IBM told LinkedIn. The new initiatives are increased paid time off, more promotion and pay reviews, backup dependent care, virtual tutoring, and ‘compassionate leave’ for parents who experience stillbirth or miscarriage. In addition, as the company moves forward with a hybrid working model that allows employees to decide how often they want to be onsite, IBM has also transformed its onboarding process with “a focus on empathy and engagement” to help remote new hires feel more connected.

See jobs at IBM

AT&T

AT&T is the parent company of DIRECTV, WarnerMedia, Cricket Wireless, and others.

Global full-time headcount: 202,600 | Top U.S. locations: Atlanta, Dallas, New York City | Most notable skills: Design Thinking, Customer Experience, Futurism | Most common job titles: Retail Sales Consultant, Client Solutions Executive, Customer Service Representative | Largest job functions: Sales, Information Technology, Engineering | What you should know: Just three years after the acquisition of Time Warner, AT&T is changing course. The company agreed to a deal last year that will combine WarnerMedia’s assets with Discovery’s to create a new, separate global entertainment giant. Once the spinoff is completed (likely mid-2022), the telecom company will be focused on its core business — expanding access to broadband internet. For its employees, AT&T offers several advancement opportunities. For example, it invests $2 million annually in ‘AT&T University,’ an internal training program to help its workers upskill, and has partnered with groups like Udacity and Coursera to offer advanced online courses.

See jobs at AT&T

Apple

Apple is the parent company of AuthenTec, NeXT Software, Shazam, and others.

Global headcount: 154,000 | Top U.S. locations: San Francisco Bay Area; Austin, Texas; New York City | Most notable skills: Apple Software and Hardware, Technical Learning, iOS | Most common job titles: Software Engineer, Technical Specialist, Mac Genius | Largest job functions: Engineering, Information Technology, Sales | What you should know: Apple is increasing benefits and pay for retail workers to attract and retain employees at its 270 retail stores across the U.S. — including doubling sick days for both full-time and part-time employees and granting more vacation days. Its retail employees are also eligible for paid parental leave and can access discounted emergency childcare. In addition, after being one of the first companies to tell its corporate employees to work remotely in March 2020, Apple is now asking that they return to the office three days a week.

See jobs at Apple

Comcast

Comcast is the parent company of NBCUniversal, Sky, DreamWorks Animation, and others.

Global headcount: 189,000 (with 130,000 in the U.S.) | Top U.S. locations: Philadelphia, New York City, Los Angeles | Most notable skills: Media Production, Cable Modems, Broadcast Television | Most common job titles: Software Engineer, Communications Technician, Salesperson | Largest job functions: Engineering, Sales, Information Technology | What you should know: Comcast prioritizes career growth and development among its employees through various benefits — including mentorship programs, department rotations and tuition assistance for continuing education and skills development. As a part of its commitment to wellbeing, it also pays for 78% of its employees’ health care costs. Want an in? Comcast says the #1 skill it looks for in new hires is authenticity. We believe that by being yourself, you are empowered to do your best work,” the company told LinkedIn.

See jobs at Comcast

Meta

Meta is the parent company of Onavo, WhatsApp, Instagram, and others.

Global headcount: 71,900 | Top U.S. locations: San Francisco Bay Area, Seattle, New York City | Most notable skills: PHP, Program Management, Social Media Marketing | Most common job titles: Software Engineer, Technical Recruiter, Data Scientist | Largest job functions: Engineering, Information Technology, Human Resources

See jobs at Meta

Dell Technologies

Dell Technologies is the parent company of Dell EMC, SecureWorks, and others.

Global headcount: 133,000 | Top U.S. locations: Austin, Texas; Boston; San Francisco Bay Area | Most notable skills: Software as a Service (SaaS), Kubernetes, Salesforce | Most common job titles: Account Executive, Software Engineer, Inside Sales Representative | Largest job functions: Sales, Information Technology, Engineering

See jobs at Dell Technologies

 Accenture

Accenture is the parent company of Karmarama, The Monkeys, Fjord, and others.

Global headcount: 674,000 | Top U.S. locations: Washington D.C., New York City, Chicago | Most notable skills: Amazon Web Services (AWS), Management Consulting, Software Development Life Cycle (SDLC) | Most common job titles: Managing Director, Management Consultant, Business Integration Manager | Largest job functions: Information Technology, Business Development, Engineering

See jobs at Accenture

 Verizon

Verizon is the parent company of GTE Corporation, MCI Communications Corporation, and others.

Global headcount: 119,400 (with 105,800 in the U.S.) | Top U.S. locations: New York City, Dallas, Washington D.C. | Most notable skills: Quotas, Wireless Technologies, Solution Selling | Most common job titles: Solutions Specialist, Customer Service Representative, Business Account Manager | Largest job functions: Sales, Engineering, Information Technology

See jobs at Verizon

 Intel

Intel is the parent company of Mobileye, Data Center Group, and others.

Global headcount: 121,000 (with 55,700 in the U.S.) | Top U.S. locations: Portland, Ore.; Phoenix; San Francisco Bay Area | Most notable skills: JMP, System on a Chip (SoC), Statistical Process Control (SPC) | Most common job titles: Software Engineer, Process Engineer, System-on-Chip Design Engineer | Largest job functions: Engineering, Operations, Information Technology

See jobs at Intel

Oracle

Oracle is the parent company of MICROS Systems, NetSuite, Peoplesoft, BEA Systems, and others.

Global headcount: 133,000 (46,600 in the U.S.) | Top U.S. locations: San Francisco Bay Area, Boston, Denver | Most notable skills: Oracle Cloud, NetSuite, OCI | Most common job titles: Software Engineer, Business Development Consultant, Application Sales Manager | Largest job functions: Engineering, Sales, Information Technology

See jobs at Oracle

 Salesforce

Salesforce is the parent company of Slack, Mulesoft, Buddy Media, Tableau, and others.

Global headcount: 74,300 (41,000 in the U.S.) | Top U.S. locations: San Francisco Bay Area, Seattle, New York City | Most notable skills: Salesforce.com Administration, Salesforce Sales Cloud, Slack | Most common job titles: Account Executive, Software Engineer, Solutions Engineer | Largest job functions: Sales, Engineering, Information Technology

See jobs at Salesforce

Cisco

Cisco is the parent company of Duo Security and others.

Global headcount: 81,800 (38,800 in the U.S.) | Top U.S. locations: San Francisco Bay Area; Raleigh-Durham, N.C.; Dallas | Most notable skills: Software as a Service (SaaS), Kubernetes, Network Engineering | Most common job titles: Software Engineer, Account Manager, Program Manager | Largest job functions: Engineering, Information Technology, Sales

See jobs at Cisco

Cognizant

Global headcount: 330,600 (34,680 in the U.S.) | Top U.S. locations: New York City, Dallas, Chicago | Most notable skills: Amazon Web Services (AWS), Software Development Life Cycle (SDLC), Agile & Waterfall Methodologies | Most common job titles: Project Manager, Software Engineer, Technical Lead | Largest job functions: Engineering, Information Technology, Program and Project Management

See jobs at Cognizant | See people you may know at Cognizant

Siemens

Siemens is the parent company of Mendix and others.

Global headcount: 303,000 (with 40,000 in the U.S.) | Top U.S. locations: New York City, Philadelphia, Atlanta | Most notable skills: Building Automation, HVAC Controls, Electrical Troubleshooting | Most common job titles: Project Manager, Software Engineer, Senior Sales Executive | Largest job functions: Engineering, Sales, Operations

See jobs at Siemens

Juniper Networks

Global headcount: 10,400 (with 4,400 in the U.S.) | Top U.S. locations: San Francisco Bay Area, Boston, Washington D.C. | Most notable skills: Junos, Kubernetes, Border Gateway Protocol (BGP) | Most common job titles: Software Engineer, System Engineer, Technical Support Engineer  | Largest job functions: Engineering, Sales, Information Technology

See jobs at Juniper Networks

Viasat

Viasat is the parent company of RigNet and others.

Global headcount: 5,800 | Top U.S. locations: San Diego, Denver, Atlanta | Most notable skills: RF Test, Amazon Web Services (AWS), Satellite Communications (SATCOM) | Most common job titles: Software Engineer, Program Manager, System Engineer | Largest job functions: Engineering, Information Technology, Operations

See jobs at Viasat

MathWorks

Global headcount: 5,000 (with 3,000 in the U.S.) | Top U.S. locations: Boston, Detroit, Los Angeles | Most notable skills: MATLAB, Simulink, Deep Learning | Most common job titles: Software Engineer, Application Support Engineer, Principal Software Engineer | Largest job function: Engineering, Information Technology, Sales

See jobs at MathWorks

 

LinkedIn’s Key Trends Of 2022

  • Flexible work is becoming table stakes for recruiting and retaining employees. With job seekers and employees in the driver’s seat and able to ask for the work-life balance they need, flexible work has become required to attract and retain top talent. Most companies on this year’s list offer some form of work-from-anywhere flexibility, with more than 70,000 remote jobs open now across the top 50 companies. Many companies also allow employees to set their schedules and work custom “on” hours through asynchronous work. Some, like Amazon (#1), Raytheon Technologies (#21), and General Motors (#44), are encouraging work-life balance with company-wide days off, while others offer unlimited paid vacation and sabbaticals. In addition, many companies are testing out new flexible offerings – employees at Cisco (#30) have adopted a four-day workweek through the company’s Interim Reduced Workweek program, IBM (#6) has set mandatory “off” hours, Cognizant (#33) offers the option to work a compressed week through its WorkFlex program, Realogy (#40) has a no meetings policy on “Focus Fridays,” Publicis Groupe (#41) allows employees the freedom to work from anywhere they like for up to six weeks per year and PwC (#32) allows employees to step away from work for up to six months while paid through its new Leave of Absence program.
  • Top companies offer stability in an unstable world. While many companies across the U.S. have faced challenges and disruptions over the last year, the Top Companies offer stability and upskilling opportunities that employees can count on – from tuition assistance and PTO for professional development to mentorship programs and job shadowing. Many organizations instituted new programs to retain employees. For example, Deloitte (#11) introduced a new Talent Experience Office focused on employee sentiments and preferences to help inform company choices, EY (#22) offers a Pathway to Purpose virtual program to help employees discover and live their personal purpose and vision, and Kimley-Horn (#31) offers job rotations, so employees learn from different roles and departments. Amazon (#1) is investing $1.2 billion to expand its education and skills training initiatives, Walmart (#5) gives field-based associates access to a no-cost college degree through its Live Better U program, and Verizon (#18) offers an apprenticeship program for those facing employment loss due to automation in technology to prepare them for the jobs of the future. PwC (#32) invested $3 billion in a “New World. New Skills” commitment to equip employees with digital training and awarded a “thank you” bonus of one-week extra pay. Bank of America (#8) provided an additional $1 billion in compensation stock awards to employees globally, and Northrop Grumman (#38) enhanced their annual bonus plan in addition to their ongoing stay interviews.
  • Mental health care is going mainstream across hiring and talent management. To keep employees healthy and happy at work, almost all of this year’s honorees now provide services that address mental health and well-being. Companies like Intel (#23), Salesforce (#28), and Juniper Networks (#46) provide dedicated mental health days, with many – including FedEx (#47) and Blackstone (#43) – offering company-paid mental health benefits. In addition, EY (#22) has expanded its no-cost counseling and mental health coaching sessions to 25 per year for employees and family. Deloitte (#11) provides a $1,000 well-being subsidy in addition to individualized psychological health resources. Unitedhealth Group (#13) provides complimentary access to wellness apps offering coaching, talk therapy, and more.
  • Authenticity, compassion, and curiosity are must-have skills. Most of the Top Companies do not require college degrees and instead look for soft skills that can translate across departments and roles. For example, the #1 skill Comcast (#10) seeks in new hires is authenticity, HCA Healthcare (#37) wants new hires to possess compassion, and Dell Technologies (#14) looks for people who thrive in an environment with a diversity of people and ideas. Accenture (#17), Oracle (#27), and Lockheed Martin (#29) value candidates with curiosity and eagerness to learn and grow. Alphabet (#2) looks for problem-solving skills and a growth mindset.

LinkedIn’s Top 50 Companies In The U.S., 2022

  1. Amazon
  2. Alphabet
  3. Wells Fargo
  4. JPMorgan Chase & Co.
  5. Walmart
  6. IBM
  7. AT&T
  8. Bank of America
  9. Apple
  10. Comcast
  11. Deloitte
  12. Meta
  13. UnitedHealth Group
  14. Dell Technologies
  15. CVS Health
  16. The Walt Disney Company
  17. Accenture
  18. Verizon
  19. GE
  20. Boeing
  21. Raytheon Technologies
  22. EY
  23. Intel
  24. Keller Williams
  25. Kaiser Permanente
  26. Target
  27. Oracle
  28. Salesforce
  29. Lockheed Martin
  30. Cisco
  31. Kimley-Horn
  32. PwC
  33. Cognizant
  34. Citi
  35. Citadel
  36. Johnson & Johnson
  37. HCA Healthcare
  38. Northrop Grumman
  39. Siemens
  40. Realogy
  41. Publicis Groupe
  42. Whiting-Turner
  43. Blackstone
  44. General Motors
  45. Capital One
  46. Juniper Networks
  47. FedEx
  48. Ford Motor Company
  49. Viasat
  50. MathWorks

 

Jan 11

How Services CPQ Helps Close Revenue Gaps

How Services CPQ Helps Close Revenue Gaps

Bottom Line: Professional services (PS) organizations need to close the gaps in their CPQ selling strategies to win more deals, capture more revenue and protect margins from ongoing price pressure.

Why Services CPQ Is Too Slow Today

When PS organizations compete in sales cycles, the first competitor to have a complete quote with accurate pricing, schedules, and an engagement plan will often win. However, getting a complete quote out fast is a major challenge for most PS organizations today. Many PS organizations manually create their quotes by taking into account a broad base of factors that include the following: talent profiles of employees and the market value of their skills; utilization rates; direct and indirect engagement costs; typical gross margins by type of engagement; and, competitive pricing. The average PS organization takes six weeks to deliver a quote or proposal. John Ragsdale’s excellent recent article Automating Services Quote-to-Cash: Emergence of CPQ for Services provides useful insights into what needs to change for PS quoting and selling to increase its velocity.

Getting Services CPQ Right Is Hard

Gaps that drain revenue and margin grow wider when PS organizations attempt to use product-centric CPQ platforms to sell services. Too often, PS organizations attempt to wedge their quoting, pricing, and revenue management into a product-based CPQ system – and get mediocre results at best. Earlier in my career, I led a product management team that defined, created, and launched a quoting system for professional services inside a large IT organization. The most valuable lessons learned from that experience include the following:

  • PS bundles only work if they have simple, solid direct cost structures. Adding a synthetic SKU that represents a PS bundle only works for the most simple, automated PS engagements. Think of those PS engagements with long-standing direct cost structures that are simple, clear and easy to implement. Attempting to group PS bundles can easily lead to quoting mistakes that drain margin when a product-centric CPQ system is used for PS.
  • The greater the differences in PS revenue management, the more the need for a new CPQ platform. Many PS organizations are making a mistake by attempting to make product-centric CPQ platforms work for their unique costing, pricing, and selling needs. My team and I learned that the more a PS revenue model is unique and one-of-a-kind, the more it requires a unique CPQ platform.
  • Getting product-based CPQ rules and constraint logic right is hard in PS. Our teams’ biggest challenge in recycling IT’s CPQ app for PS was how difficult it was to get the rules-based engine to work for the wide variety of variables in a common service engagement. Rules created for transaction velocity needed to be reworked for greater variety. PS engagements didn’t follow a common logic structure like a product, making the constraint logic code only somewhat usable.
  • Only launch after CRM and Revenue Management integration is complete. Our team was handed a project that had languished in IT for nearly a year because PS selling teams wouldn’t use it. The problem was that the quoting module ran batch updates to a series of databases to get customer records and fetch the latest price tables off of a mainframe. In addition, CPQ wasn’t connected in real-time to CRM or Revenue Management.

Closing Long-Standing Services CPQ Gaps

The more a Services CPQ app can close the gaps between CRM, PSA, Revenue Management, and CPQ apps and their workflows, the more effective it will be stopping margin and revenue leakage. Having APIs that share data in real-time between CRM, PSA, and Revenue Management within each quote creation session has the potential to save thousands of hours a year. FinancialForce’s recently announced Services CPQ shows how a platform-based integration strategy works. The following graphic shows how revenue potential increases as a Services CPQ’s systems become more integrated.

How Services CPQ Helps Close Revenue Gaps

FinancialForce’s approach to taking on the challenge of providing an enterprise-grade Services CPQ is noteworthy for several reasons, including the following:

  • Real-time visibility and control of Services CPQ Effectiveness. Having Services CPQ, PSA and Revenue Management on the same Salesforce platform provides the visibility and control PS sales managers need to track quoting effectiveness by program, geography, customer segment, and rep. The more real-time the data integration across these systems the greater the potential for revenue growth in existing accounts and winning new ones.
  • Changing professional services quotes in real-time without impacting sales cycles is possible. Due to the integrated design of Services CPQ, one change made anywhere on a quote will replicate through the entire system and change all related factors immediately.
  • Getting in control of professional services engagement dates and utilization rates by associates helps reduce time-to-market and assures better time-to-customer performance. Keeping track of the myriad of factors that influence a services quote using a manually-based process is too slow for how quickly engagements are decided. Instead, having a single, unified data model that can track effectiveness and provide updates on how they impact engagement project plans is needed to excel at selling with Services CPQ. Adopting an agile CPQ strategy that relies on an integration thread to unify all systems is the secret to scaling and selling more with an agile approach to services CPQ.
  • Pricing needs to be one of the core strengths in an integrated Services CPQ platform. Realizing how a customers’ requested changes to a professional services engagement will impact costs and margins gives PS teams with an integrated system a formidable pricing advantage. FinancialForce’s approach to solving the Services CPQ challenge shows the potential to take on this challenge and provide its PS customers with the insights they need to upsell engagements – and not lose margin doing it.
  • A must-have for any Services CPQ platform is support for channel partner collaboration and team quoting. For any Services CPQ to scale up and deliver its full potential value, there needs to be support for customizing partner selling experiences while providing for team selling and quoting. FinancialForce solves this by relying on the Salesforce platform. By closing the gaps between the systems Services CPQ relies on, the channel selling teams and partners gain greater flexibility in defining customized products.

Conclusion

Services CPQ needs to scale out on a platform to achieve its full potential by providing the analytical insights to track engagement lifecycles and customer lifetime value by engagement. FinancialForce has proven they can do this in their Spring 2021 release. Taking on the most challenging aspects of a Services CPQ architecture starts by providing insights and guidance on how best to optimize the mix of associates and their utilization and billing rates, locations of each engagement, margin threshold levels, and the expected duration of each engagement. Additionally, the world’s leading professional services organizations could use an automated Services CPQ solution as many of them don’t rely on enough data, letting revenue leakage happen without knowing it.

 

Oct 17

1 Comment

2021 State Of The Machine Learning Market: Enterprise Adoption Is Strong

data science, machine learning, enterprise software, AI, artificial Intelligence
  • 59% of all large enterprises are deploying data science (DS) and machine learning (ML) today.
  • Nearly 50% of all organizations have up to 25 or more ML models in use today.
  • 29% of enterprises are refreshing their data science and machine learning models every day.
  • The higher the data literacy an enterprise can achieve before launching Data Science & Machine Learning initiatives, the higher the probability of success.

These and many other insights defining the state of the data science and machine learning market in 2021 are from Dresner Advisory Services’ 2021 Data Science and Machine Learning Market Study. The 7th annual report is noteworthy for its depth of analysis and insight into how data science and machine learning adoption is growing stronger in enterprises. In addition, the study explains which factors drive adoption and determine the key success factors that matter the most when deploying data science and machine learning techniques. The methodology uses crowdsourcing techniques to recruit respondents from over 6,000 organizations and vendors’ customer communities. As a result, 52% of respondents are from North America and 34% from EMEA, with the balance from Asia-Pacific and Latin America. 

“The perceived importance of data science and machine learning correlates with organizational success with BI, with users that self-report as completely successful with BI almost twice as likely to rate data science as critical,” said Jim Ericson, vice president, and research director at Dresner Advisory. “The perceived level of data literacy also correlates directly and positively with the current or likely future use of data science and machine learning in 2021.” 

Key insights from the study include the following:

  • 59% of large enterprises are deploying data science and machine learning in production today.  Enterprises with 10K employees or more lead all others in adopting and using DS and ML techniques, most often in R&D and Business Intelligence Competency Center (BICC)-related work. Large-scale enterprises often rely on DS and ML to identify how internal processes and workflows can be streamlined and made more cost-efficient. For example, the CEO of a manufacturing company explained on a recent conference call that DS and ML pilots bring much-needed visibility and control across multiple plants and help troubleshoot inventory management and supply chain allocation problems.
machine learning
  • The importance of data science and ML to enterprises has doubled in eight years, jumping from 25% in 2014 to 70% in 2021. The Dresner study notes that a record level of enterprises sees data science and ML as critically important to their business in 2021. Furthermore, 90% of enterprises consider these technologies essential to their operations, rating them critically important or very important. Successful projects in Business Intelligence Competency Centers (BICC) and R&D helped data science and ML gain broad adoption across all organizations. Larger-scale enterprises with over 10K employees are successfully scaling data science and ML to improve visibility, control, and profitability in organizations today.
machine learning
  • Enterprises dominate the recruiting and retention of data science and machine learning talent. Large-scale enterprises with over 10K employees are the most likely to have BI experts and data scientists/statisticians on staff. In addition, large-scale enterprises lead hiring and retention in seven of the nine roles included in the survey. It’s understandable how the Business Intelligence (BI) expertise of professionals in these roles is helping remove the roadblocks to getting more business value from data science and machine learning. Enterprises are learning how to scale data science and ML models to take on problems that were too complex to solve with analytics or BI alone.    
machine learning
  • 80% of DS and ML respondents most want model lifecycle management, model performance monitoring, model version control, and model lineage and history at a minimum. Keeping track of the state of each model, including version control, is a challenge for nearly all organizations adopting ML today. Enterprises reach ML scale when they can manage ML models across their lifecycles using an automated system. The next four most popular features of model rollback, searchable model repository, collaborative, model co-creation tools, and model registration and certification are consistent with the feedback from Data Science teams on what they need most in an ML platform. 
machine learning
  • Financial Services prioritize model lifecycle management and model performance monitoring to achieve greater scale from the tens of thousands of models they’re using today. Consistent with other research that tracks ML adoption by industry, the Dresner study found that Financial Services leads all other industries in their need for the two most valuable features of ML platforms, model lifecycle management and model performance monitoring. Retail and Wholesale are reinventing their business models in real-time to become more virtual while also providing greater real-time visibility across supply chains. ML models in these two industries need automated model version control, model lineage and history, model rollback, collaborative, model co-creation tools, and model registration and certification. In addition, retailers and Wholesalers are doubling down on data science and machine learning to support new digital businesses, improve supply chain performance and increase productivity.
machine learning
  • Enterprises need support for their expanding range of regression models, text analytics functions, and ensemble learning. Over the last seven years, text analytics functions and sentiment analysis’ popularity has continually grown. Martech vendors and the marketing technologists driving the market are increasing sentiment analysis’ practicality and importance. Recommendation engines and geospatial analysis are also experiencing greater adoption due to martech changing the nature of customer- and market-driven analysis and predictive modeling. 
machine learning
  • R, TensorFlow, and PyTorch are considered the three most critical open-source statistical and machine learning frameworks in 2021. Nearly 70% of respondents consider R important to getting work done in data science and ML. The R language has established itself as an industry standard and is well-respected across DevOps, and IT teams in financial services, professional services, consulting, process, and discrete manufacturing. Tensorflow and Pytorch are considered important by the majority of organizations Dresner’s research team interviewed. They’re also among the most in-demand ML frameworks today, with new applicants having experience in all three being recruited actively today.   
machine learning
  • Data literacy predicts DS and ML program success rates. 64% of organizations say they have extremely high literacy rates, implying that DS and ML have reached mainstream adoption thanks partly to BI literacy rates in the past. Enterprises that prioritize data literacy by providing training, certification, and ongoing education increase success odds with ML. A bonus is that employees will have a chance to learn marketable skills they can use in their current and future positions. Investing in training to improve data literacy is a win/win.
machine learning
  • On-database analytics and in-memory analytics (both 91%), and multi-tenant cloud services (88%) are the three most popular technologies enterprises rely on for greater scalability. Dresner’s research team observes that the scalability of data science and machine learning often involves multiple, different requirements to address high data volumes, large numbers of users, data variety while supporting analytic throughput. Apache Spark support continues to grow in enterprises and is the fourth-most relied-on industry support for ML scalability.   
machine learning

Apr 7

The Top 20 Machine Learning Startups To Watch In 2021

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  • There are a record number of 9,977 machine learning startups and companies in Crunchbase today, an 8.2% increase over the 9,216 startups listed in 2020 and a 14.6% increase over the 8,705 listed in 2019.
  • Artificial Intelligence (A.I.) and machine learning (ML)-related companies received a record $27.6 billion in funding in 2020, according to Crunchbase. 
  • Of those A.I. and machine learning startups receiving funding since January 1, 2020, 62% are seed rounds, 31% early-stage venture rounds and 6.7% late-stage venture capital-funded rounds.
  • A.I. and machine learning startups’ median funding round was $4.4 million and the average was $29.8 million in 2020, according to Crunchbase.

Throughout 2020, venture capital firms continued expanding into new global markets, with London, New York, Tel Aviv, Toronto, Boston, Seattle and Singapore startups receiving increased funding. Out of the 79 most popular A.I. & ML startup locations, 15 are in the San Francisco Bay Area, making that region home to 19% of startups who received funding in the last year. Israel’s Tel Aviv region has 37 startups who received venture funding over the last year, including those launched in Herzliya, a region of the city known for its robust startup and entrepreneurial culture.  

The following graphic compares the top 10 most popular locations for A.I. & ML startups globally based on Crunchbase data as of today:

 

Top 20 Machine Learning Startups To Watch In 2021

Augury – Augury combines real-time monitoring data from production machinery with AI and machine learning algorithms to determine machine health, asset performance management (APM) and predictive maintenance (PdM) to provide manufacturing companies with new insights into their operations. The digital machine health technology that the company offers can listen to the machine, analyze the data and catch any malfunctions before they arise. This enables customers to adjust their maintenance and manufacturing processes based on actual machine conditions. The platform is in use with HVAC, industrial factories and commercial facilities.

Alation – Alation is credited with pioneering the data catalog market and is well-respected in the financial services community for its use of A.I. to interpret and present data for analysis. Alation has also set a quick pace to evolving its platform to include data search & discovery, data governance, data stewardship, analytics and digital transformation. With its Behavioral Analysis Engine, inbuilt collaboration capabilities and open interfaces, Alation combines machine learning with human insight to successfully tackle data and metadata management challenges. More than 200 enterprises are using Alation’s platform today, including AbbVie, American Family Insurance, Cisco, Exelon, Finnair, Munich Re, New Balance, Pfizer, Scandinavian Airlines and U.S. Foods. Headquartered in Silicon Valley, Alation is backed by leading venture capitalists including Costanoa, Data Collective, Icon, Sapphire and Salesforce Ventures.

Algorithmia – Algorithmia’s expertise is in machine learning operations (MLOps) and helping customers deliver ML models to production with enterprise-grade security and governance. Algorithmia automates ML deployment, provides tooling flexibility, enables collaboration between operations and development and leverages existing SDLC and CI/CD practices. Over 110,000 engineers and data scientists have used Algorithmia’s platform to date, including the United Nations, government intelligence agencies and Fortune 500 companies.

Avora – Avora is noteworthy for its augmented analytics platform, making in-depth data analysis intuitively as easy as performing web searches. The company’s unique technology hides complexity, empowering non-technical users to run and share their reports easily. By eliminating the limitations of existing analytics, reducing data preparation and discovery time by 50-80% and accelerating time to insight, Avora uses ML to streamline business decision-making. Headquartered in London with offices in New York and Romania, Avora helps accelerate decision making and productivity for customers across various industries and markets, including Retail, Financial Services, Advertising, Supply Chain and Media and Entertainment.

Boast.ai – Focused on helping companies in the U.S. and Canada recover their R&D costs from respective federal governments, Boast.ai enables engineers and accountants to gain tax credits using AI-based tools. Some of the tax programs Boast.ai works with include US R&D Tax Credits, Scientific Research and Experimental Development (SR&ED) and Interactive Digital Media Tax Credits (IDMTC). The startup has offices in San Francisco, Vancouver and Calgary.

ClosedLoop.ai – An Austin, Texas-based startup, ClosedLoop.ai has created one of the healthcare industry’s first data science platforms that streamline patient experiences while improving healthcare providers’ profitability.  Their machine learning automation platform and a catalog of pre-built predictive and prescriptive models can be customized and extended based on a healthcare provider’s unique population or client base needs. Examples of their technology applications include predicting admissions/readmissions, predicting total utilization & total risk, reducing out-of-network utilization, avoiding appointment no-shows, predicting chronic disease onset or progression and improving clinical documentation and reimbursement. The Harvard Business School, through its Kraft Precision Medicine Accelerator, recently named ClosedLoop.ai as one of the fastest accelerating companies in its Real World Data Analytics Landscapes report.

Databand – A Tel Aviv-based startup that provides a software platform for agile machine learning development, Databand was founded in 2018 by Evgeny Shulman, Joshua Benamram and Victor Shafran. Data engineering teams are responsible for managing a wide suite of powerful tools but lack the utilities they need to ensure their ops are running properly. Databand fills this gap with a solution that enables teams to gain a global view of their data flows, make sure pipelines complete successfully and monitor resource consumption and costs. Databand fits natively in the modern data stack, plugging seamlessly into tools like Apache Airflow, Spark, Kubernetes and various ML offerings from the major cloud providers.

DataVisor – DataVisor’s approach to using AI for increasing fraud detection accuracy on a platform level is noteworthy. Using proprietary unsupervised machine learning algorithms, DataVisor enables organizations to detect and act on fast-evolving fraud patterns and prevent future attacks before they happen. Combining advanced analytics and an intelligence network of more than 4.2B global user accounts, DataVisor protects against financial and reputational damage across various industries, including financial services, marketplaces, e-commerce and social platforms. They’re one of the more fascinating cybersecurity startups using AI today.

Exceed.ai – What makes Exceed.ai noteworthy is how their AI-powered sales assistant platform automatically communicates the lead’s context and enables sales and marketing teams to scale their lead engagement and qualification efforts accordingly. Exceed.ai follows up with every lead and qualifies them quickly through two-way, automated conversations with prospects using natural language over chat and email. Sales reps are freed from performing error-prone and repetitive tasks, allowing them to focus on revenue-generating activities such as phone calls and demos with potential customers.

Indico – Indico is a Boston-based startup specializing in solving the formidable challenge of how dependent businesses are on unstructured content yet lack the frameworks, systems and tools to manage it effectively. Indico provides an enterprise-ready A.I. platform that organizes unstructured content while streamlining and automating back-office tasks. Indico is noteworthy given its track record of helping organizations automate manual, labor-intensive, document-based workflows.  Its breakthrough in solving these challenges is an approach known as transfer learning, which allows users to train machine learning models with orders of magnitude fewer data than required by traditional rule-based techniques. Indico enables enterprises to deploy A.I. to unstructured content challenges more effectively while eliminating many common barriers to A.I. & ML adoption.

LeadGenius – LeadGenius is noteworthy for its use of AI to provide personalized and actionable B2B lead information that helps its clients attain their global revenue growth goals. LeadGenius’s worldwide team of researchers uses proprietary technologies, including AI and ML-based techniques, to deliver customized lead generation, lead enrichment and data hygiene services in the format, methods and frequency defined by the customer. Their mission is to enable B2B sales and marketing organizations to connect with their prospects via unique and personalized data sets.

Netra – Netra is a Boston-based startup that began as part of MIT CSAIL research and has multiple issued and pending patents on its technology today. Netra is noteworthy for how advanced its video imagery scanning and text metadata interpretation are, ensuring safety and contextual awareness. Netra’s patented A.I. technology analyzes videos in real-time for contextual references to unsafe content, including deepfakes and potential cybersecurity threats. 

Particle –  Particle is an end-to-end IoT platform that combines software including A.I., hardware and connectivity to provide a wide range of organizations, from startups to enterprises, with the framework they need to launch IoT systems and networks successfully.  Particle customers include Jacuzzi, Continental Tires, Watsco, Shifted Energy, Anderson EV, Opti and others. Particle is venture-backed and has offices in San Francisco, Shenzhen, Las Vegas, Minneapolis and Boston. Particle’s developer community includes over 200,000 developers and engineers in more than 170 countries today.

RideVision – RideVision was founded in 2018 by motorcycle enthusiasts Uri Lavi and Lior Cohen. The company is revolutionizing the motorcycle-safety industry by harnessing the strength of artificial intelligence and image-recognition technology, ultimately providing riders with a much broader awareness of their surroundings, preventing collisions and enabling bikers to ride with full confidence that they are safe. RideVision’s latest round was $7 million in November of last year, bringing their total funding to $10 million in addition to a partnership with Continental AG.

Savvie – Savvie is an Oslo-based startup specializing in translating large volumes of data into concrete actions that bakery and café owners can utilize to improve their bottom line every day.  In doing so, we help food businesses make the right decisions to optimize their operations and increase profitability while reducing waste at its source. What’s noteworthy about this startup is how adept they are at fine-tuning ML algorithms to provide their clients with customized recommendations and real-time insights about their food and catering businesses.  Their ML-driven insights are especially valuable given how bakery and café owners are pivoting their business models in response to the pandemic.

SECURITI.ai – One of the most innovative startups in cybersecurity, combining AI and ML to secure sensitive data in multi-cloud and mixed platform environments, SECURITI.ai is a machine learning company to watch in 2021, especially if you are interested in cybersecurity.  Their AI-powered platform and systems enable organizations to discover potential breach risk areas across multi-cloud, SaaS and on-premise environments, protect it and automate all private systems, networks and infrastructure functions.

SkyHive – SkyHive is an artificial intelligence-based SaaS platform that aims to reskill enterprise workforces and communities. It develops and commercializes a methodology, Quantum Labor Analysis, to deliver real-time, skill-level insights into internal workforces and external labor markets, identify future and emerging skills and facilitate individual-and company-level reskilling. SkyHive is industry-agnostic and supporting enterprise and government customers globally with a mission to reduce unemployment and underemployment. Sean Hinton founded the technology company in Vancouver, British Columbia, in 2017.

Stravito – Stravito is an A.I. startup that’s combining machine learning, Natural Language Processing (NLP) and Search to help organizations find and get more value out of the many market research reports, competitive, industry, market share, financial analysis and market projection analyses they have by making them searchable. Thor Olof Philogène and Sarah Lee founded the company in 2017, who identified an opportunity to help companies be more productive, getting greater value from their market research investments. Thor Olof Philogène and Andreas Lee were co-founders of NORM, a research agency where both worked for 15 years serving multinational brands, eventually selling the company to IPSOS. While at NORM, Anders and Andreas were receiving repeated calls from global clients that had bought research from them but could not find it internally and ended up calling them asking for a copy. Today the startup has Carlsberg, Comcast, Colruyt Group, Danone, Electrolux, Pepsi Lipton and others. Stravito has offices in Stockholm (H.Q.), Malmö and Amsterdam.

Verta.ai – Verta is a startup dedicated to solving the complex problems of managing machine learning model versions and providing a platform to launch models into production. Founded by Dr. Manasi Vartak, Ph.D., a graduate of MIT, who led a team of graduate and undergraduate students at MIT CSAIL to build ModelDB, Verta is based on their work define the first open-source system for managing machine learning models. Her dissertation, Infrastructure for model management and model diagnosis, proposes ModelDB, a system to track ML-based workflows’ provenance and performance. In August of this year, Verta received a $10 million Series A round led by Intel Capital and General Catalyst, who also led its $1.7 million seed round. For additional details on Verta.ai, please see How Startup Verta Helps Enterprises Get Machine Learning Right. The Verta MLOps platform launch webinar provides a comprehensive overview of the platform and how it’s been designed to streamline machine learning models into production:

https://www.youtube.com/watch?v=yO84xPMHZk4

V7 – V7 allows vision-based A.I. systems to learn continuously from training data with minimal human supervision. The London-based startup emerged out of stealth in August 2018 to reveal V7 Darwin, an image labeling platform to create training data for computer vision projects with little or no human involvement necessary. V7 specializes in healthcare, life sciences, manufacturing, autonomous driving, agri-tech, sporting clients like Merck, GE Healthcare and Toyota. V7 Darwin launched at CVPR 2019 in Long Beach, CA. Within its first year, it has semi-automatically annotated over 1,000 image and video segmentation datasets. V7 Neurons is a series of pre-trained image recognition applications for industry use. The following video explains how V7 Darwin works:

https://www.youtube.com/watch?v=iBpgSQk5Qyg

 

Mar 3

10 Ways AI And Machine Learning Are Improving Marketing In 2021

  • AI and Machine Learning are on track to generate between $1.4 Trillion to $2.6 Trillion in value by solving Marketing and Sales problems over the next three years, according to the McKinsey Global Institute. 
  • Marketers’ use of AI soared between 2018 and 2020, jumping from 29% in 2018 to 84% in 2020, according to Salesforce Research’s most recent State of Marketing Study. 
  • AI, Machine Learning, marketing & advertising technologies, voice/chat/digital assistants, and mobile tech & apps are the five technologies that will have the greatest impact on the future of marketing, according to Drift’s 2020 Marketing Leadership Benchmark Report.

Chief Marketing Officers (CMOs) and the marketing teams they lead are expected to excel at creating customer trust, a brand that exudes empathy and data-driven strategies that deliver results. Personalizing channel experiences at scale works when CMOs strike the perfect balance between their jobs’ emotional and logical, data-driven parts. That’s what makes being a CMO today so challenging. They’ve got to have the compassion of a Captain Kirk and the cold, hard logic of a Dr. Spock and know when to use each skill set. CMOs and their teams struggle to keep the emotional and logical parts of their jobs in balance.

Asked how her team keeps them in balance, the CMO of an enterprise software company told me she always leads with empathy, safety and security for customers and results follow. “Throughout the pandemic, our message to our customers is that their health and safety come first and we’ll provide additional services at no charge if they need it.” True to her word, the company offered their latest cybersecurity release update to all customers free in 2020.  AI and machine learning tools help her and her team test, learn and excel iteratively to create an empathic brand that delivers results.

The following are ten ways AI and machine learning are improving marketing in 2021:

1.    70% of high-performance marketing teams claim they have a fully defined AI strategy versus 35% of their under-performing peer marketing team counterparts. CMOs who lead high-performance marketing teams place a high value on continually learning and embracing a growth mindset, as evidenced by 56% of them planning to use AI and machine learning over the next year. Choosing to put in the work needed to develop new AI and machine learning skills pays off with improved social marketing performance and greater precision with marketing analytics. Source: State of Marketing, Sixth Edition. Salesforce Research, 2020.

10 Ways AI And Machine Learning Are Improving Marketing In 2021

2.    36% of marketers predict AI will have a significant impact on marketing performance this year. 32% of marketers and agency professionals were using AI to create ads, including digital banners, social media posts and digital out-of-home ads, according to a recent study by Advertiser Perceptions. Source: Which Emerging Tech Do Marketers Think Will Most Impact Strategy This Year?, Marketing Charts, January 5, 2021.

10 Ways AI And Machine Learning Are Improving Marketing In 2021

3.    High-performing marketing teams are averaging seven different uses of AI and machine learning today and just over half (52%) plan on increasing their adoption this year. High-performing marketing teams and the CMOs lead them to invest in AI and machine learning to improve customer segmentation. They’re also focused on personalizing individual channel experiences. The following graphic underscores how quickly high-performing marketing teams learn then adopt advanced AI and machine learning techniques to their competitive advantage. Source: State of Marketing, Sixth Edition. Salesforce Research, 2020.

10 Ways AI And Machine Learning Are Improving Marketing In 2021

4.    Marketers use AI-based demand sensing to better predict unique buying patterns across geographic regions and alleviate stock-outs and back-orders. Combining all available data sources, including customer sentiment analysis using supervised machine learning algorithms, it’s possible to improve demand sensing and demand forecast accuracy. ML algorithms can correlate location-specific sentiment for a given product or brand and a given product’s regional availability. Having this insight alone can save the retail industry up to $50B a year in obsoleted inventory.  Source: AI can help retailers understand the consumer, Phys.org. January 14, 2019.

10 Ways AI And Machine Learning Are Improving Marketing In 2021

5.    Disney is applying AI modeling techniques, including machine learning algorithms, to fine-tune and optimize its media mix model. Disney’s approach to gaining new insights into its media mix model is to aggregate data from across the organization including partners, prepare the model data and then transform it for use in a model. Next, a variety of models are used to achieve budget and media mix optimization. Then compare scenarios. The result is a series of insights that are presented to senior management. The following dashboard shows the structure of how they analyze AI-based data internally. The data shown is, for example only; this does not reflect Disney’s actual operations.   Source: How Disney uses Tableau to visualize its media mix model (https://www.tableau.com/best-marketing-dashboards)

10 Ways AI And Machine Learning Are Improving Marketing In 2021

6.    41% of marketers say that AI and machine learning make their greatest contributions to accelerating revenue growth and improving performance. Marketers say that getting more actionable insights from marketing data (40%) and creating personalized consumer experiences at scale (38%) round out the top three uses today. The study also found that most marketers, 77%, have less than a quarter of all marketing tasks intelligently automated and 18% say they haven’t intelligently automated any tasks at all. Marketers need to look to AI and machine learning to automated remote, routine tasks to free up more time to create new campaigns. Source: Drift and Marketing Artificial Intelligence Institute, 2021 State of Marketing AI Report.

10 Ways AI And Machine Learning Are Improving Marketing In 2021

7.    Starbucks set the ambitious goal of being the world’s most personalized brand by relying on predictive analytics and machine learning to create a real-time personalization experience. The global coffee chain faced several challenges starting with how difficult it was to target individual customers with their existing IT infrastructure. They were also heavily reliant on manual operations across their thousands of stores, which made personalization at scale a formidable challenge to overcome. Starbucks created a real-time personalization engine that integrated with customers’ account information, the mobile app, customer preferences, 3rd party data and contextual data. They achieved a 150% increase in user interaction using predictive analytics and AI, a 3X improvement in per-customer net incremental revenues. The following is a diagram of how DigitalBCG (Boston Consulting Group) was able to assist them. Source: Becoming The World’s Most Personalized Brand, DigitalBCG.  

10 Ways AI And Machine Learning Are Improving Marketing In 2021

8.    Getting personalization-at-scale right starts with a unified Customer Data Platform (CDP) that can use machine learning algorithms to discover new customer data patterns and “learn” over time.  For high-achieving marketing organizations, achieving personalization-at-scale is their highest and most urgent priority based on Salesforce Research’s most recent State of Marketing survey. And McKinsey predicts personalization-at-scale can create $1.7 trillion to $3 trillion in new value. For marketers to capture a part of this value, changes to the mar-tech stack (shown below) must be supported by clear accountability and ownership of channel and customer results. Combining a modified mar-tech stack with clear accountability delivers results.   Source: McKinsey & Company, A technology blueprint for personalization at scale. May 20, 2019. By Sean Flavin and Jason Heller.

10 Ways AI And Machine Learning Are Improving Marketing In 2021

9.    Campaign management, mobile app technology and testing/optimization are the leading three plans for a B2C company’s personalization technologies. Just 19% of enterprises have adopted AI and machine learning for B2C personalization today. The Forrester Study commissioned by IBM also found that 55% of enterprises believe the technology limitations inhibit their ability to execute personalization strategies. Source: A Forrester Consulting Thought Leadership Paper, Commissioned by IBM, Personalization Demystified: Enchant Your Customers By Going From Good To Great, February 2020.

10 Ways AI And Machine Learning Are Improving Marketing In 2021

10. Successful AI-driven personalization strategies deliver results beyond marketing, delivering strong results enterprise-wide, including lifting sales revenue, Net Promoter Scores and customer retention rates. When personalization-at-scale is done right, enterprises achieve a net 5.63% increase in sales revenue, 10.26% increase in order frequency, uplifts in average order value and an impressive 13.25% improvement in cross-sell/up-sell opportunities. The benefits transcend marketing alone and drive higher customer satisfaction metrics as well.   Source: A Forrester Consulting Thought Leadership Paper, Commissioned by IBM, Personalization Demystified: Enchant Your Customers By Going From Good To Great, February 2020.

10 Ways AI And Machine Learning Are Improving Marketing In 2021

CMOs and their teams rely on AI and machine learning to iteratively test and improve every aspect of their marketing campaigns and strategies. Striking the perfect balance between empathy and data-driven results takes a new level of data quality which isn’t possible to achieve using Microsoft Excel or personal productivity tools today. The most popular use of AI and machine learning in organizations is delivering personalization at scale across all digital channels. There’s also increasing adoption of predictive analytics based on machine learning to fine-tune propensity models to improve up-sell and cross-sell results. 

Bibliography

AI can help retailers understand the consumer, Phys.org. January 14, 2019

Brei, Vinicius. (2020). Machine Learning in Marketing: Overview, Learning Strategies, Applications and Future Developments. Foundations and Trends® in Marketing. 14. 173-236. 10.1561/1700000065.

Conick, H. (2017). The past, present and future of AI in marketing. Marketing News, 51(1), 26-35.

Drift and Marketing Artificial Intelligence Institute, 2021 State of Marketing AI Report.

Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30-50.

Jarek, K., & Mazurek, G. (2019). MARKETING AND ARTIFICIAL INTELLIGENCE. Central European Business Review, 8(2).

Libai, B., Bart, Y., Gensler, S., Hofacker, C. F., Kaplan, A., Kötterheinrich, K., & Kroll, E. B. (2020). Brave new world? On AI and the management of customer relationships. Journal of Interactive Marketing, 51, 44-56.

Ma, L., & Sun, B. (2020). Machine learning and AI in marketing–Connecting computing power to human insights. International Journal of Research in Marketing, 37(3), 481-504.

McKinsey & Company, A technology blueprint for personalization at scale. May 20, 2019

McKinsey Global Institute, Visualizing the uses and potential impact of AI and other analytics, April 17, 2018, | Interactive   

Microsoft Azure AI Gallery (https://gallery.azure.ai/)

Pedersen, C. L. Empathy‐based marketing. Psychology & Marketing.

Sinha, M., Healey, J., & Sengupta, T. (2020, July). Designing with AI for Digital Marketing. In Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization (pp. 65-70).

State of Marketing, Sixth Edition. Salesforce Research, 2020.

Aug 31

1 Comment

Where AIOps Is Delivering Results Today

Where AIOps Is Delivering Results Today

Bottom Line: Capitalizing on AI and machine learning’s inherent strengths to create contextual intelligence in real-time, LogicMonitor’s early warning and failure prevention systems reflect where AIOps is delivering results today.

LogicMonitor’s track record of making solid contributions to their customers’ ability to bring greater accuracy, insight, and precision into monitoring all IT assets is emerging as a de facto industry standard. Recently I was speaking with a startup offering Hosted Managed Services of a variety of manufacturing applications, and the must-have in their services strategy is LogicMonitor LM Intelligence. LogicMonitor’s AIOps platform is powered by LM Intelligence, enabling customers’ businesses to gain early warning into potential trouble spots in IT operations stability and reliability. LogicMonitor does the hard work for you with automated alert thresholds, AI-powered early warning capabilities, customizable escalation chains, workflows, and more.

Engineers who are working at the Hosted Managed Services provider I recently spoke with say LM Intelligence is the best use case of AI and machine learning to provide real-time alerts, contextual insights, discover new patterns in data, and make automation achievable. The following is an example of the LM Intelligence dashboard:

Where AIOps Is Delivering Results Today

How LogicMonitor’s Architecture Supports AIOps

One of the core strengths LogicMonitor continues to build on is integration, which they see as essential to their ability to excel at providing AIOps support for their customers. Their architecture is shown below. By providing real-time integration to public cloud platforms, combined with control over the entire IT infrastructure structure along with over 2,000 integrations from network to cloud, LogicMonitor excels at unifying diverse IT environments into a single, cohesive AIOps-based intelligence system.  The LogicMonitor platform collects cloud data through our cloud collectors. These collectors retrieve metrics such as the cloud provider health and billing information by making API calls to the cloud services. The collector is a Windows Service or background process that is installed in a virtual machine. This collector then pulls metrics from the different devices using a variety of different methods, including SNMP, WMI, perf Mon JMX, APIs, and scripts.

Where AIOps Is Delivering Results Today

Using AIOps To Monitor, Analyze, Automate

LogicMonitor has created an architecture that’s well-suited to support the three dominant dimensions of AIOps, including Monitoring, Analytics (AIOps), and Automating. Their product and services strategies in the past have reflected a strong focus on Monitoring. The logic of prioritizing Monitoring as a product strategy area was to provide the AI and machine learning models with enough data to train on so they could identify anomalies in data patterns faster. Their 2018/2019 major releases in the Monitor area reflect how the unique strength they have of capturing and making use of any IT asset that can deliver a signal is paying off. Key Monitor developers recently include the following:

  • Kubernetes Monitoring
  • Service Insight
  • Topology
  • Remote Sessions
  • Netflow
  • Configuration Monitoring
  • Public Cloud Monitoring
  • Applications Monitoring

LogicMonitor’s core strengths in AIOps are in the Anomaly Detection and Early Warning System areas of their product strategy. Their rapid advances in the Early Warning System development show where AIOps is delivering solid results today. Supporting the Early Warning System, there are Dynamic Thresholds and Root Cause Analysis based on Dependencies as well.

The Automate area of their product strategy shows strong potential for future growth, with the ServiceNow integration having upside potential. Today Alert Chaining and Workflow support integrations to Ansible, Terraform, Slack, Microsoft, Teama, Putter, Terraform, OpsGenie, and others.

Conclusion

LogicMonitor’s platform handles 300B metrics on any given day and up to 10B a month, with over 28K collectors deployed integrated with approximately 1.4M devices being monitored. Putting AI and machine learning to work, interpreting the massive amount of data the platform captures every day to fine-tune their Early Warning and Failure Prevention Systems, is one of the most innovative approaches to AIOps today. Their AIOps Early Warning System is using machine learning Algorithms to fine-tune Root Cause Analysis and Dynamic Thresholds continually. AIOps Log Intelligence is also accessing the data to complete Automatic Log Anomaly Detection, Infrastructure change detection, and Log Volume Reduction to Signal analysis.

 

 

 

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