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Posts tagged ‘IDC’

Nov 9

Why Gartner and IDC are $86B apart on 2027 cybersecurity spending

Chart comparing Gartner's and IDC's cybersecurity spending forecasts for 2027: $276.1 billion versus $361.9 billion, an $85.8 billion difference explained by scope, while 2027 growth rates sit within 1.2 points at 11.2 to 12.4 percent
Cybersecurity spending forecasts for 2027 compared. Gartner and IDC are $85.8B apart on market size, yet their 2027 growth rates sit within 1.2 points of each other. Click any chart to open it full size. Sources linked below, analysis by softwarestrategiesblog.com.

Four research firms have now published cybersecurity spending forecasts for 2027. Their numbers do not agree, and the disagreement itself is more instructive than any one total. Gartner’s 3Q26 forecast puts worldwide information security end-user spending at $276.1 billion in 2027. IDC’s latest published figure is $361.9 billion. Omdia has 2026 at $315 billion but keeps its 2027 figure behind a paywall. Forrester surveys the people who hold the budgets instead of sizing the market, and 60% of them expect at least a 5% increase next year.

Put side by side, the spread looks like a dispute about demand. Growth rates say otherwise. Every firm’s 2026 rate lands between 12.7% and 13.6%, while each 2027 rate sits between 11.2% and 12.4%. What separates a $276 billion market from a $362 billion one is what each firm decides to count. This post reconciles the four cybersecurity spending forecasts for 2027, shows where the scope differences come from and identifies the one place where the firms genuinely disagree. That place is the pace of deceleration after 2027.

Five takeaways from the cybersecurity spending forecasts for 2027

  • The 2027 gap is $86 billion, and it is almost entirely definitional. IDC’s $361.9 billion includes security hardware and spans 38 technology markets across 28 industries. Gartner’s $276.1 billion covers 41 categories of security software, security services and network security equipment but no other hardware. Neither firm is wrong. Each draws the market boundary in a different place.
  • Growth is the point of agreement. Gartner has 2026 at 12.7% in constant currency (13.6% in current dollars) and 2027 at 11.2%. IDC’s V2 release has 2026 at 13.2% and its event page has 2027 at 12.4%. Omdia also has 2026 at 13%. Any of the four cybersecurity spending forecasts for 2027 would describe the same healthy, low-teens market.
  • Omdia’s services share is the biggest single difference. Omdia puts services at roughly two-thirds of cybersecurity spending, against a 37.8% services share in Gartner’s forecast. In dollar terms, Omdia’s June 2026 services figure of $204.8 billion is $111 billion above Gartner’s $93.5 billion. That alone is more than the entire gap between the two firms’ totals.
  • The real disagreement is after 2027. Gartner’s constant-currency growth path declines every year, falling from 12.7% in 2026 until it reaches 9.6% in 2030. That is a 10.8% compound rate for 2025 to 2030. IDC describes a “12.5% five-year path to $512.9B by 2030.” By 2030 the two long-range numbers are $140 billion apart.
  • Forrester’s budget signal improved year over year. In its 2027 planning survey, 16% of security decision-makers expect a budget increase above 10%, while 44% expect 5% to 10%. A year earlier the figures were 15% and 40%. Budgets are growing faster in the minds of the people who set them than a year ago.

What each firm’s cybersecurity spending forecasts say for 2026 and 2027

The table below holds the figures each firm has made public, together with the scope that goes with them. Read the scope column first, because the totals mean little without it. Gartner’s number is end-user spending in current dollars from the detailed forecast file. IDC’s figures come from two places. Its V2 Worldwide Security Spending Guide release gives $310.42 billion for 2026. A separate IT Security event page then gives $361.9 billion for 2027 and $512.9 billion for 2030. Omdia’s 2026 figure appears in its October 1, 2026 channel analysis. Forrester’s numbers are survey percentages rather than dollars.

The published figures side by side

Table comparing Gartner, IDC, Omdia and Forrester cybersecurity spending forecasts for 2026 and 2027 with long-range figures, 2026 total bars and scope notes: Gartner $247.5bn and $276.1bn, IDC $310.4bn and $361.9bn, Omdia $315bn for 2026, Forrester 55 percent and 60 percent expecting at least a 5 percent budget increase
What each firm has published for 2026 and 2027, with the scope behind each number. Bars compare the firms’ 2026 totals on their own scopes. Gartner growth rates and CAGR are calculations from the detailed forecast file. Analysis by Louis Columbus, softwarestrategiesblog.com.

One caution on IDC’s published figures

IDC’s own public figures do not fully reconcile. A 2027 total of $361.9 billion that is “up 12.4% year on year” implies a 2026 base of about $322 billion. That is about $11.6 billion above the $310.42 billion in the V2 release. A 12.5% five-year path to $512.9 billion implies a 2025 base near $285 billion, while the V2 growth rate implies $274 billion. The likeliest explanation is that the event page draws on a later version of the guide. I show both as published and treat them as two snapshots rather than one series.

Line chart of Gartner's 2024 to 2030 worldwide security spending forecast against IDC's published points for 2026, 2027 and 2030 and Omdia's 2026 figure, showing gaps of $86 billion in 2027 and $140 billion in 2030
Gartner’s full 2024-2030 series against the points IDC and Omdia have published. The IDC-Gartner gap grows from $86B in 2027 to $140B in 2030. Analysis by Louis Columbus, softwarestrategiesblog.com.

Why IDC’s cybersecurity spending forecasts run $63 billion above Gartner’s in 2026 and $86 billion above in 2027

Start with what each firm counts. Gartner’s forecast is end-user spending across three markets. Security software is $126.9 billion in 2026, security services $93.5 billion and network security equipment $27.0 billion. That equipment is also the only hardware in a forecast Gartner publishes as 41 categories across 47 country and sub-regional rows. The 2026 total still includes $9.0 billion of consumer security software. Once the consumer line is stripped out, the business-only figure is $238.4 billion for 2026 and $266.8 billion for 2027.

IDC built its Worldwide Security Spending Guide on a wider frame. It covers 38 technology markets, including security hardware beyond network appliances, and then slices them by 28 industries and 48 countries. IDC said in March that software would be more than half of its $308 billion 2026 total and grow fastest, at 14%. The October V2 release lifted 2026 to $310.42 billion and the growth rate to 13.2%. That combination means IDC revised 2025 down and 2026 up at the same time.

Both firms tell roughly the same software-led growth story, although about differently sized markets. A $63 billion gap in 2026 that holds its proportion in 2027 is what a fixed scope difference looks like. Demand disagreements leave a different trail. If IDC saw stronger demand than Gartner, the gap would widen quickly. It does widen, but slowly, and almost entirely because of the long-range growth assumption discussed below.

Stacked bar chart showing how Gartner, IDC, Omdia and Forrester each divide cybersecurity spending in 2026: Gartner 51 percent software, 38 percent services, 11 percent network equipment. IDC more than half software. Omdia one-third technology and two-thirds services. Forrester 40 percent software and about 29 percent personnel
Each firm’s 2026 split of cybersecurity spending, in its own categories. Omdia’s two-thirds services share and Forrester’s personnel line have no equivalent in Gartner’s forecast. Analysis by Louis Columbus, softwarestrategiesblog.com.

Omdia’s two-thirds services share is the largest definitional split

Omdia’s $315 billion looks close to IDC’s $310 billion, but it gets there by a different route. Omdia says technology, meaning licenses, subscriptions and appliances, is roughly one-third of cybersecurity spending. Services spanning advisory, design and managed security are the other two-thirds. Its June 2026 figures were $106.4 billion for technology and $204.8 billion for services, out of $311 billion.

Gartner’s forecast inverts that ratio. Software plus network security equipment is $153.9 billion in 2026, $47.5 billion more than Omdia’s technology figure. Gartner’s security services total of $93.5 billion sits $111.3 billion below Omdia’s services figure, and that one difference is larger than the $63.5 billion gap between Omdia’s June total and Gartner’s.

Managed security shows how far apart the definitions sit. Gartner’s Managed Security Services subsegment is $32.3 billion in 2026, with managed detection and response at $11.5 billion of that and security operations at $15.6 billion. Omdia’s managed security services figure for 2026 is $106 billion, growing 14%, as cited by WithSecure’s MSP Cybersecurity Buyer’s Guide from Omdia’s MSSP Trends and Predictions. That one number is larger than Gartner’s entire security services market. Omdia is counting partner-delivered services that Gartner either books as product revenue or leaves outside the forecast.

For anyone selling security services, the implication is simple. Omdia’s figures describe the partner economy, and Gartner’s describe the services revenue that vendors recognize on their own books. Both are real answers, but to different questions.

Paired horizontal bar chart comparing Gartner and Omdia 2026 cybersecurity spending by type: technology $153.9 billion versus $106.4 billion, services $93.5 billion versus $204.8 billion, managed security $32.3 billion versus $106 billion
Gartner and Omdia 2026 figures by type. Omdia counts $111B more in services than Gartner, and its managed security figure alone is larger than Gartner’s total services market. Analysis by Louis Columbus, softwarestrategiesblog.com.

The one real disagreement is how fast growth slows after 2027

Once scope is set aside, the four cybersecurity spending forecasts agree on 2027 and differ on what follows. Gartner’s constant-currency growth falls every year in the 3Q26 file, from 12.7% in 2026 to 11.2% in 2027, 10.4% in 2028, 10.0% in 2029 and 9.6% in 2030, so the compound rate for 2025 to 2030 works out to 10.8%. The market adds $28.7 billion in 2027 and then $96.7 billion over the following three years, which is strong growth on a decelerating path.

IDC’s published path barely decelerates. After 13.2% in 2026 and 12.4% in 2027, the firm describes a 12.5% five-year compound rate to $512.9 billion by 2030. Getting from $361.9 billion in 2027 to $512.9 billion in 2030 requires 12.3% a year for three more years, which is flat growth rather than a slide toward single digits. By 2030 the gap between IDC’s compound rate and Gartner’s year-on-year rate reaches 2.9 percentage points.

Why the 2030 numbers are $140 billion apart

That difference compounds, so the dollar gap widens every year. In 2027 the two totals are $86 billion apart, and by 2030 the distance reaches $140 billion, or about 38% of Gartner’s own 2030 total. Had IDC’s figure grown at Gartner’s rates from 2027 instead, it would reach about $489 billion in 2030. The gap would then be $116 billion, leaving $24 billion that comes from the growth assumption alone.

That leaves the 2028 to 2030 call as a call about AI. Either AI-driven security demand sustains low-teens growth, or platform consolidation and budget pressure pull it toward 10%. Consolidation is the Gartner case, while sustained AI demand is IDC’s. Gartner’s own forecast assumptions also back that reading. By 2029 it expects 52% of organizations to use fewer than 10 cybersecurity tools, up from 21% in 2025, according to Forecast Analysis: Information Security, Worldwide, 2026 (G00838442).

Line chart of year-on-year growth in worldwide security spending showing Gartner's constant-currency path falling from 12.7 percent in 2026 to 9.6 percent in 2030 against IDC's 13.2 percent, 12.4 percent and 12.5 percent five-year compound rate
Growth rates in the cybersecurity spending forecasts for 2027 and beyond. Gartner’s path declines every year through 2030, while IDC’s published points and its 12.5% five-year rate stay above 12%. Analysis by Louis Columbus, softwarestrategiesblog.com.

Forrester measures budgets, not markets, and the mood improved

Forrester built its Budget Planning Guide 2027: Security And Risk Leaders Must Confront The AI Cost Explosion from a survey of more than 2,600 business and technology decision-makers. The guide produces no market size, and that is useful here. Budgets include headcount, training, insurance and now what Forrester calls “token economics,” the inference costs of running AI inside security operations, none of which any vendor forecast counts.

The headline is a modest, consistent improvement. For 2027, 60% of security decision-makers expect their budgets to rise by at least 5%, with 16% expecting more than 10% and 44% expecting 5% to 10%. A year earlier the same questions for 2026 returned 55%, split 15% and 40%. Across all technology decision-makers, 82% expect budgets to rise in 2027, although the regional detail for 2026 showed how uneven that optimism was. Only 9% of North American security leaders expected more than 10% growth, against 14% in Europe and 22% in Asia Pacific.

Forrester’s budget composition data explains why its figures do not map onto Gartner’s or IDC’s cybersecurity spending forecasts for 2027. Citing Information Services Group data for 2025, Forrester says software is about 40% of security budgets. That outpaces hardware and outsourcing combined and, in Forrester’s wording, exceeds personnel-related costs by 11%. Read as percentage points, that puts personnel near 29% of the budget, the second-largest line a CISO carries and one that no market forecast includes. A security budget growing 5% to 10% is consistent with a vendor market growing 11% to 13%, provided staff costs grow more slowly than software and services. Two years of consolidation and automation point that way.

Stacked bar chart of Forrester survey results showing 55 percent of security decision-makers expected at least a 5 percent budget increase for 2026 and 60 percent expect it for 2027, with a side panel showing 9 percent of North American, 14 percent of European and 22 percent of Asia Pacific leaders expecting more than 10 percent growth for 2026
Forrester’s security budget surveys for 2026 and 2027. The share expecting at least a 5% increase rose from 55% to 60%. Analysis by Louis Columbus, softwarestrategiesblog.com.

What Gartner’s CISO research adds to the demand picture

Gartner’s own planning research for the same cycle points the same way, and it adds two cost drivers that none of the market forecasts isolates. CISO Agenda Priority: Align Cybersecurity Investment With Board Risk Appetite (G00862663) states that the cybersecurity challenge “is no longer primarily a budget problem” and that boards are deliberately increasing risk appetite to accelerate AI adoption. The money is there, so the argument is about where it goes.

On staffing, the 2027 Planning Guide for Cybersecurity (G00856222) says the SOC workforce will transform rather than shrink as AI agents arrive. Gartner projects that by 2028 security operations teams will employ 50% more engineers than traditional analysts, which keeps Forrester’s personnel line intact even as vendor spending grows. The Cybersecurity Executive Outlook, 4Q26 (G00861818) reports that 51% of CISOs have not begun any post-quantum cryptography activity and predicts that organizations that do not begin piloting PQC by 2027 will pay 200% higher costs for full migration. Those are reasons for 2027 budgets to keep rising regardless of how a market is scoped.

The securing-AI gap is wider than the market gap

The sharpest divergence between the cybersecurity spending forecasts for 2027 is in the newest category. Gartner’s securing-AI forecast appears in Forecasting the $16.4 Billion Opportunity in Securing AI (G00853861) and carries into the 2Q26 and 3Q26 information security forecasts. It has spending on securing AI at about $3 billion in 2026 before rising to $16 billion in 2030. That implies growth of roughly 52% a year, although from a small base. The category sits inside Gartner’s security software total rather than beside it.

IDC’s IT Security event page states that spending on securing AI will reach $58.3 billion in 2027, “compounding at 37.8% a year.” It calls this “the fastest-moving segment in security.” The source is IDC’s Worldwide Artificial Intelligence Security Spending Forecast, 2025-2029. That 2027 figure is 3.6 times Gartner’s 2030 figure, three years earlier.

A gap of that size comes from definitions. IDC’s event page does not say what the $58.3 billion counts, and its scale points to a far broader set of spending than Gartner’s, plausibly including AI-enabled security tooling and services. Gartner’s securing-AI line covers only the spend on protecting AI models, agents and data. A third number, Gartner’s AI-amplified security forecast, reaches $204 billion by 2030 and is a different construct again. The firm’s February analysis says so itself, stating that AI-amplified security “is not additive spending and will not change security spending” (G00838442). I covered it in an earlier post linked below. Anyone quoting an “AI security market size” needs to say which of these three definitions they mean.

Bar chart comparing Gartner's securing AI forecast of $3 billion in 2026 and $16 billion in 2030 with IDC's $58.3 billion securing AI figure for 2027
Gartner’s securing-AI forecast against IDC’s. IDC’s 2027 figure is 3.6 times Gartner’s 2030 figure, a difference of definition rather than demand. Analysis by Louis Columbus, softwarestrategiesblog.com.

Nearly half of the gap sits in the United States

Regional detail shows where IDC’s extra scope lands. IDC’s March release put the United States at $150 billion for 2026. Gartner’s 3Q26 file has the United States at $119.5 billion, so the $30.5 billion difference is nearly half of the $63 billion gap between the two totals. Gartner then has the US rising to $133.6 billion in 2027 and $181.1 billion in 2030.

Europe is closer than the worldwide totals imply. IDC has Western Europe at $69 billion, while Gartner’s Europe region, which includes Central and Eastern European countries, sums to $64.0 billion. Even a broader Gartner region lands $5 billion below a narrower IDC one. In Asia/Pacific excluding Japan and China, IDC’s $26 billion sits $8.3 billion above the $17.7 billion in Gartner’s Mature and Emerging Asia/Pacific regions combined. As a ratio, that is the widest regional gap of the three. IDC runs at 1.47 times Gartner’s figure there, against 1.26 in the United States and 1.08 in Europe.

The US-heavy gap fits the scope story, because the United States has the deepest market for security hardware, managed services and industry-specific security spending. Those are also the three areas where IDC’s definitions extend furthest beyond Gartner’s.

Dumbbell chart comparing Gartner and IDC 2026 security spending by region: United States $119.5 billion versus $150 billion, Europe $64 billion versus $69 billion for Western Europe, Asia/Pacific excluding Japan and China $17.7 billion versus $26 billion
Gartner and IDC 2026 regional figures. The United States accounts for $30.5B of the $63B gap between the two firms’ worldwide totals. Analysis by Louis Columbus, softwarestrategiesblog.com.

How to use four cybersecurity spending forecasts for 2027 that do not agree

  • Pick the forecast that matches your question, then stay with it. Vendor revenue planning and competitive share analysis belong on Gartner’s end-user spending basis, because that is how most public security vendors are benchmarked. Channel and services strategy belongs on Omdia’s partner-centric total, while hardware-inclusive or industry-vertical sizing belongs on IDC. Switching bases mid-analysis is how a 12% growth market gets misreported as a 30% one.
  • Quote growth rates before you quote totals. The four cybersecurity spending forecasts agree that 2026 grows 12.7% to 13.6% and 2027 grows 11.2% to 12.4%. Those are the defensible numbers, while totals carry scope assumptions that most audiences will not check.
  • Treat 2028 to 2030 as the open question. Gartner’s path heads for 9.6% growth by 2030 while IDC’s stays near 12.5%. Plans built on the IDC trajectory have about $140 billion more market to divide by 2030 than plans built on Gartner. Watch Gartner’s 4Q26 update and IDC’s next guide version for which way the 2028 numbers move.
  • Use Forrester as the demand-side check. The share of security leaders expecting at least a 5% budget increase rose from 55% to 60%. If it keeps rising, the vendor forecasts’ low-teens growth has budget support. A decline would instead make Gartner’s deceleration the base case.
  • Never cite an AI security market size without its definition. $3 billion, $16 billion, $58.3 billion and $204 billion are all current analyst figures for some version of “AI security.” They describe four different things.

Frequently asked questions

What do the cybersecurity spending forecasts say the market is worth in 2027?

Among the published cybersecurity spending forecasts for 2027, Gartner’s is the lowest at $276.1 billion in worldwide information security end-user spending. That is up 11.6% in current dollars and 11.2% in constant currency. IDC’s published figure is $361.9 billion, up 12.4%. The $86 billion difference reflects IDC’s broader scope, which includes security hardware across 38 technology markets, and the two firms’ 2027 growth rates sit within 1.2 points of each other.

Why do IDC’s cybersecurity spending forecasts run higher than Gartner’s for 2027?

IDC’s Worldwide Security Spending Guide counts hardware, software and services across 38 markets, 28 industries and 48 countries. Gartner counts security software, security services and network security equipment in 41 categories. Nearly half of the 2026 gap, $30.5 billion of $63 billion, is in the United States, because hardware, managed services and industry-specific spending are largest there.

What does Omdia forecast for cybersecurity spending?

Omdia’s October 2026 figure is $315 billion for 2026, up 13%, with technology about one-third and services about two-thirds. Its earlier June 2026 breakdown was $106.4 billion for technology and $204.8 billion for services on a $311 billion total. Omdia’s 2027 and five-year figures remain available only to subscribers, unlike its 2026 headline.

What does Forrester say about 2027 security budgets?

In Forrester’s Budget Planning Guide 2027 survey, 60% of security decision-makers expect at least a 5% budget increase for 2027. Of those, 16% expect more than 10% and 44% expect 5% to 10%. The previous year’s survey for 2026 returned 55%, 15% and 40%.

Are the cybersecurity spending forecasts for 2026 and 2027 rising or falling?

For 2026, every firm’s number is higher than when the year began. Gartner’s went from $244.2 billion in 4Q25 to $248.2 billion in 2Q26, then eased to $247.5 billion in 3Q26. IDC’s rose from $308 billion to $310.42 billion, while Omdia’s rose from $311 billion to $315 billion. For the longer range, Gartner forecasts growth slowing to 9.6% by 2030, while IDC’s published five-year rate holds at 12.5%.

How I built this analysis

I computed the Gartner figures from the detailed data file behind Forecast: Information Security, Worldwide, 2024-2030, 3Q26 (G00862059). That file contains 13,489 rows of end-user spending by year, region, country, market, subsegment and category, in both current and constant currency. Its accompanying report confirms the totals and the 2029 and 2030 growth rates, and Forecast Analysis: Information Security, Worldwide, 2026 (G00838442) supplies the forecast assumptions quoted above. CISO and planning-guide statements come from CISO Agenda Priority: Align Cybersecurity Investment With Board Risk Appetite (G00862663), 2027 Planning Guide for Cybersecurity (G00856222) and The Cybersecurity Executive Outlook, 4Q26 (G00861818). Totals, growth rates, segment shares, regional sums and the 2025-2030 CAGR are my calculations from that file. Securing-AI figures come from Forecasting the $16.4 Billion Opportunity in Securing AI (G00853861), as carried into the 2Q26 and 3Q26 forecasts.

Where the IDC, Omdia and Forrester figures come from

IDC figures are as published in its March 3, 2026 press release, its October 28, 2026 blog on the V2 release of the 2026 Worldwide Security Spending Guide, and its IT Security event page. That page cites the spending guide and the Worldwide Artificial Intelligence Security Spending Forecast, 2025-2029. The implied 2026 base of about $322 billion in the event-page series is my calculation rather than an IDC figure.

Omdia figures come from its October 1, 2026 channel analysis as well as the June 2026 figures published by Omdia’s Jay McBain. The managed security figure is as cited by WithSecure from Omdia’s MSSP Trends and Predictions. Forrester figures come from its Budget Planning Guide 2027 press release and coverage, together with its July 2025 Security Planning 2026 blog and its Predictions 2027 release. The Forrester personnel share of about 29% reads Forrester’s statement that software exceeds personnel-related costs by 11% as a percentage-point gap below the 40% software share. Read as a relative 11%, personnel would be about 36%, and either reading leaves personnel as the second-largest budget line.

I built all charts comparing the cybersecurity spending forecasts for 2027 from these figures and labeled each with its sources. Where a figure is an estimate or a derived value, the chart and text say so.

Related comparisons

Sources

This post is my personal analysis and does not represent my employer.

Oct 22

AI infrastructure spending forecasts compared: every major firm raised its 2030 number in 2026

AI infrastructure spending forecasts compared, 2026 revisions: Dell'Oro raised 2030 data center capex from $1.7T to more than $3T, Gartner raised 2030 data center systems 58% to $2.16T, hyperscaler capex consensus rose 52% to $800B, IDC raised 2029 AI infrastructure 42% to $1.08T; Citi's $3.8T 2030 AI capex is 3.1x IDC's $1.21T
Four firms raised the same AI infrastructure forecast between editions, with 2030 figures from Dell’Oro, Gartner and IDC. Click any chart to open it full size. Sources linked below, analysis by softwarestrategiesblog.com.

Every major AI infrastructure spending forecast went up in 2026, and the biggest increases landed on 2029 and 2030. Dell’Oro Group nearly doubled its 2030 data center capex outlook in six months. Gartner raised its 2030 data center systems forecast 58% in a single quarter. IDC raised its 2029 AI infrastructure forecast 42% in nine months.

The firms still disagree on how big the build-out gets. For 2030, the published annual figures run from IDC’s $1.21 trillion to Citi’s $3.8 trillion, a 3.1x gap. Most of that gap comes from what each firm counts, not from different views of demand.

This post puts the public numbers from 12 firms side by side. They are Gartner, IDC, Dell’Oro, Goldman Sachs, Citi, Morgan Stanley, McKinsey, Bain, Forrester, JLL, the IEA and BloombergNEF. It shows what each one measures, how far each has moved, what the five largest buyers are actually spending, what the power grid can deliver and what the build-out has to earn back. Where a firm published only a range or a scenario, I say so.
Read more

Feb 26

Roundup of agentic AI forecasts and market estimates, 2026

Roundup of agentic AI forecasts and market estimates, 2026

Agentic AI spending is projected to reach $201.9 billion in 2026 (Gartner), overtaking chatbot spending by 2027.  Four independent firms size the standalone market at $7–8 billion with 40%+ CAGRs. But adoption lags the money: only 23% of organizations have scaled agent deployments (McKinsey), and 40% of projects face cancellation by 2027 (Gartner).

Fortune Business Insights projects $7.29 billion in 2025, reaching $139.19 billion by 2034 at 40.5% CAGR. Precedence Research sizes it at $7.55 billion in 2025, growing to $199.05 billion by 2034 at 43.84% CAGR. MarketsandMarkets puts the figure at $7.06 billion in 2025, reaching $93.20 billion by 2032 at 44.6% CAGR. Deloitte’s TMT Predictions 2025 estimates $8.5 billion in 2026, growing to $35 to $45 billion by 2030.

Every major forecast agrees on direction. None agrees on scale. The standalone agentic AI market lands between $7 billion and $8.5 billion. Gartner’s broader view, counting agentic capabilities embedded across enterprise software, reaches $201.9 billion in 2026. That 25x gap is not a contradiction. It is a measurement problem, and the takeaways below reflect both realities. The following are the key takeaways from agentic AI forecasts published in 2026 so far:

Two later comparisons extend this roundup: AI infrastructure spending forecasts compared (October 2026), which tracks the 2030 build-out numbers, and AI revenue gap forecasts compared, which follows the returns on that spending.

Key takeaways

Worldwide AI spending will reach $2.52 trillion in 2026, growing 44% year-over-year. That number jumped roughly $500 billion from the September forecast, which had pegged the market just above $2 trillion. Infrastructure takes $1.37 trillion, 54% of total spend. AI software follows at $452.5 billion, up 60%. AI services add $588.6 billion. AI-optimized servers alone account for $421.6 billion, growing to 49%. Gartner expects spending to grow by another 30% in 2027 and surpass $3 trillion. I have tracked these forecasts through multiple iterations. The revisions keep going in one direction. Source: Gartner press release, January 15, 2026

Table 1: Gartner worldwide AI spending by market, 2025 to 2027, in millions of dollars, rising from $1,757,152M to $3,336,690M, January 2026

 

Gartner projects $4.71 trillion in global AI spending by 2029. The fastest growth isn’t in infrastructure. Synthetic data generation leads all categories at 178% CAGR, followed by the broader AI Data market at 155%. Agentic AI compounds at 119%, expanding from $15 billion to $753 billion by 2029. AI Infrastructure, the largest category by dollars, grows at just 29%. The money is following the bottlenecks. Source:  Gartner 4Q25: $4.71T AI Market Proves Agentic AI and Data Readiness Are the Only Race That Matters, Software Strategies Blog, January 22, 2026 Link: https://softwarestrategiesblog.com/2026/01/22/gartner-4q25-agentic-ai-data-readiness-4-71t-market/

Bar chart of Gartner AI spending categories by 2024 to 2029 growth rate, led by synthetic data generation at 178.29% CAGR, AI data at 155.35% and agentic AI at 118.73%

 

The AI cybersecurity market is predicted to hit $51.3 billion in 2026, nearly doubling from $25.9 billion in 2025. But the category masks a structural imbalance. AI-amplified security, where AI defends the enterprise, captures 94.5% of spending at $48.5 billion. Securing AI, where the enterprise defends its own AI systems, gets $2.8 billion. Enterprises are investing 17x more in using AI as a security tool than in protecting the AI itself. Both sub-segments grow at similar CAGRs (74% vs. 72%), which means the dollar gap widens every year. By 2029, AI-amplified security reaches $160.4 billion, while securing AI hits just $11.6 billion. One is a tool. The other is the thing that needs protecting. Source: Gartner Forecasts Agentic AI Will Overtake Chatbot Spending by 2027, Software Strategies Blog, February 16, 2026 Link: https://softwarestrategiesblog.com/2026/02/16/gartner-forecasts-agentic-ai-overtakes-chatbot-spending-2027/

Table: Gartner AI cybersecurity spending 2024 to 2029, securing AI growing from $777M to $11,571M and AI-amplified security from $10,039M to $160,437M, 74% combined CAGR

 

AI Data sits alone in the upper-right quadrant of Gartner’s spending map, compounding at 155% CAGR with 277% growth in 2026. AI Cybersecurity and AI Models cluster above 67% CAGR. AI Infrastructure anchors the chart as the largest bubble, but grows at just 29%. Global AI spending reaches $1.8 trillion in 2025 and $4.7 trillion by 2029. The acceleration is not in compute. It is in data readiness, security architecture, and agentic capabilities. By 2028, software with agentic capabilities crosses 50% of total application software spend, up from 2% in 2024. Non-agentic software spending starts declining in 2027. Source:Data Readiness and Security Are Driving AI’s $4.7 Trillion Run, Software Strategies Blog, December 22, 2025 Link: https://softwarestrategiesblog.com/2025/12/22/data-readiness-security-driving-ai-4-7-trillion/

Gartner bubble chart of worldwide AI spending by market plotting 2026 growth against 2024 to 2029 CAGR, with AI data the fastest-growing segment and AI infrastructure the largest

Gartner’s AI spending forecast reaches $2.53 trillion in 2026 and $4.71 trillion by 2029. Eight markets. One pattern. AI Infrastructure dominates absolute dollars at $1.37 trillion in 2026 but grows at just 29% CAGR. AI Data, the smallest segment at $3.1 billion, compounds at 155%. AI Cybersecurity nearly doubles to $51.3 billion. AI Software hits $452.5 billion, growing 60% year-over-year as agentic capabilities reshape the category. The growth rates tell you where the bottlenecks are breaking. Source: Data Readiness and Security Are Driving AI’s $4.7 Trillion Run, Software Strategies Blog, December 22, 2025 Link: https://softwarestrategiesblog.com/2025/12/22/data-readiness-security-driving-ai-4-7-trillion/

Table: Gartner AI spending forecast by market, 2024 to 2029, in billions of dollars, total rising from $1,143.5B to $4,709.2B at 32.7% CAGR, 4Q25 update December 2025

Nearly nine in ten organizations now use AI in at least one business function, up from 78% a year ago, but nearly two-thirds have not begun scaling it across the enterprise. Only 6% qualify as high performers where AI contributes more than 5% to EBIT. Sixty-two percent of organizations are at least experimenting with AI agents, yet in no individual business function are more than 10% scaling them. High performers are three times more likely than peers to fundamentally redesign workflows and three times more likely to have senior leaders demonstrating ownership of AI initiatives. More than one-third of high performers commit over 20% of their digital budgets to AI, and about three-quarters have reached the scaling phase, versus one-third of other organizations. Source: McKinsey / QuantumBlack, The state of AI in 2025: Agents, innovation, and transformation, November 2025

McKinsey chart: share of organizations using AI in at least one business function rose from 20% in 2017 to 88% in 2025, with generative AI use at 79%; 7% report fully scaled AI

Valued at $638.23 billion in 2024, the global AI market is projected to reach $3,680.47 billion by 2034, expanding to a CAGR of 19.20%. North America holds 31.80% market share. The software segment dominates at 51.40%, while machine learning leads by technology at 36.70%. Healthcare is expected to record the highest CAGR of 36.50% across end-use segments. Among regions, Asia-Pacific is expected to grow at 19.8% CAGR from 2025 to 2034, with AI projected to add up to $3 trillion to the region’s GDP by 2030, driven by national AI strategies in China, India, and Japan. Source: Precedence Research, AI Market Size, Growth & Trends, September 2025

Precedence Research bar chart: artificial intelligence market size growing from $638.23B in 2024 to $3,680.47B in 2034

Nearly $7 trillion. That’s the capital outlay data centers will require by 2030 to keep pace with demand for compute power. Of that, $5.2 trillion goes toward AI-ready facilities and $1.5 trillion toward traditional IT workloads. Global demand for data center capacity could almost triple by 2030, with about 70% of new demand coming from AI workloads. Three investment scenarios range from $3.7 trillion (constrained demand) to $7.9 trillion (accelerated demand, adding 205 incremental GW). The 60% majority of investment—$3.1 trillion—flows to technology developers and designers producing chips and computing hardware. Source: McKinsey, The cost of compute: A $7 trillion race to scale data centers, April 2025

McKinsey chart of estimated global data center capacity demand in gigawatts, continued momentum scenario, rising from 82 GW in 2025 to 219 GW in 2030, a 3.5x increase driven by AI workloads

Inference already consumed half of all AI compute in 2025. That number will grow to two-thirds in 2026 and reach 75% of all AI compute needs by 2030. Global data center capacity is projected to nearly double from 103 gigawatts to 200 GW by 2030, yet U.S. data centers already face a capacity shortfall exceeding 11 GW, with the cumulative gap expected to exceed 40 GW by 2028. North American data center capacity alone will increase eightfold, from 5.6 GW in 2024 to 44 GW by 2030. Operators are increasingly deploying edge facilities closer to end users to reduce latency as inference-dominated workloads drive a fundamental redesign of data center architectures. Source: Avid Solutions, 13 Data Center Growth Projections, January 2026

Grand View Research bar chart: AI inference market growing from $113.5B in 2025 to $253.7B in 2030

 

Generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually to the global economy, increasing the projected impact of all AI by 15 to 40%. About 75% of the value falls across four areas: customer operations, marketing and sales, software engineering, and R&D. Half of today’s work activities could be automated between 2030 and 2060, with a midpoint in 2045—roughly a decade earlier than previously estimated. When embedding effects in existing software are included, the total economic benefit rises to $6.1 trillion to $7.9 trillion annually. Source: McKinsey, The economic potential of generative AI, June 2023

McKinsey chart of AI's potential impact on the global economy in trillions of dollars, with generative AI adding $2.6T to $4.4T on top of $11.0T to $17.7T from advanced analytics and machine learning

The global AI market hit $294.16 billion in 2025 and is projected to grow to $2,480.05 billion by 2034, at a CAGR of 26.60%. The Banking, financial services and insurance (BFSI) segment holds 18.90% market share, while healthcare is expected to record the highest CAGR of 36.50%. In the U.S. alone, the AI market was estimated at $146.09 billion in 2024 and is predicted to reach $851.46 billion by 2034. The number of AI companies funded globally in 2024 totaled 2,049, with U.S.-funded companies accounting for 1,143, signaling strong investor confidence in the sector’s expansion potential. Source: Fortune Business Insights, AI Market Size, Growth & Trends by 2034

Fortune Business Insights infographic: artificial intelligence market valued at $294.16B in 2025 and $375.93B in 2026, projected at $2,480.05B by 2034 at a 26.60% CAGR

Big Tech’s AI capex hit $405 billion in 2025, up from a $250 billion estimate at the start of the year. Sell-side analysts have underestimated AI spending every quarter for two years running. A decade ago, Big Tech’s trailing-twelve-month capex was $24 billion—15x less than today. AI data center costs are projected at $3 trillion to $8 trillion, with gigawatt capacity expected to grow 3.5x by 2030. Source: IO Fund, Big Tech’s $405B Bet, November 2025

I/O Fund bar chart of Big Tech capex revisions, January 2025 versus September 2025: Microsoft $89.9B to $117.5B, Meta $52.3B to $71.0B, Alphabet $62.6B to $92.0B, Amazon $96.4B to $125.0B

The global AI market was valued at $371.71 billion in 2025 and is projected to reach $2,407.02 billion by 2032, growing at a CAGR of 30.6%. Hyperscalers accounted for 53% of chip purchases in 2023, spurring 156% market growth from 2023 to 2024. While demand from hyperscalers is expected to moderate, growth of 41% is still forecast from 2025 to 2026. Enterprises are moving from cloud reliance to in-house AI infrastructure investments, particularly for cost-effective inference solutions, as edge AI gains traction through AI-enabled PCs and mobile devices. Source: Markets and Markets, AI Market Report 2025-2032

MarketsandMarkets snapshot: artificial intelligence market size $371.71B in 2025 growing to $2,407.02B by 2032 at 30.6% CAGR, Asia Pacific the fastest-growing region

At $602 billion projected for 2026, hyperscaler capex has entered uncharted territory. Amazon, Microsoft, Google, and Meta will each exceed $100 billion individually, pushing capital intensity to 45-57% of revenue. Total hyperscaler capex from 2025-2027 is projected at $1.15 trillion, more than double the $477 billion spent from 2022-2024. Morgan Stanley and JP Morgan suggest the technology sector may need to issue $1.5 trillion in new debt over the next few years to finance AI infrastructure construction. The sheer scale of debt issuance mirrors patterns seen during the fiber-optic buildout of the late 1990s. Source: Multiple sources compiled by Introl, January 2026

Table of 2025 capex and 2026 projections for Amazon ($125B), Microsoft (about $95B), Google (about $75B), Meta ($66B to $72B) and Oracle (about $15B), most of it for AI infrastructure

The number of software companies using consumption-based pricing more than doubled between 2015 and 2024, as AI introduces new variable costs that make traditional perpetual licenses obsolete. SaaS remains dominant, but the next wave is outcome-aligned pricing that scales with actual AI usage. Software businesses that successfully adopt consumption-based pricing aligned with usage and outcomes may be better positioned to capture AI-driven value and differentiate themselves in a rapidly evolving market where the cost of each AI inference adds a new variable to the P&L. Source: McKinsey, AI adjusts the software bill, January 27, 2026

McKinsey chart: enterprise software pricing model by type, 2016 to 2028, showing consumption-based pricing taking share from perpetual and on-premises subscription licenses

Data center capacity needs for AI and non-AI workloads could almost triple by 2030, with AI capacity increasing 3.5 times and making up roughly 70% of the total. Under a continued-momentum scenario, total capacity demand rises from 82 GW in 2025 to 219 GW by 2030, with incremental AI capacity ranging from 13 GW in 2025 to 31 GW in 2030, totaling 124 GW of new AI capacity. Non-AI workloads grow from 38 GW to 64 GW over the same period. Average power densities in AI-ready data centers have more than doubled in just two years and are expected to rise nearly four times by 2027. Source: McKinsey, Data center demands (Week in Charts), May 2025

McKinsey chart of estimated global data center capacity demand in gigawatts, 2025 to 2030, split between AI and non-AI workloads, with AI workloads reaching 156 GW by 2030

U.S. data-center spending exceeded half a trillion dollars in 2025. The U.S. and China drove a massive expansion in AI-related computing capacity through 2024, with the U.S. pulling further ahead in the first half of 2025. AI-related trade accounted for nearly half of all merchandise trade growth in that period, despite representing only 15% of total trade volume. The infrastructure boom is reshaping international commerce, with surging demand for servers, graphics cards, and related components essential to AI training and inference now a dominant force in global supply chains. Source: Federal Reserve Board, FEDS Notes: The Global Trade Effects of the AI Infrastructure Boom, February 2026

Federal Reserve charts of existing AI supercomputer capacity through H1 2025 and planned capacity through 2030 in quintillion calculations per second for the U.S., China, EU and rest of world, Epoch AI data

The generative AI market is expanding from $71.36 billion in 2025 to $890.59 billion by 2032, at a CAGR of 43.4%. North America accounted for 43.05% of global revenue in 2025. Text remains the dominant data modality due to its foundational role in enterprise workflows, while the services segment is gaining traction for scalability and cost-effectiveness. Foundation model delivery platforms verticalized adoption across industries, and the rapid scaling of AI-native infrastructure are the three key forces driving the market as of 2025. The 43.4% CAGR makes this one of the fastest-expanding technology subsegments in history. Source: MarketsandMarkets, Generative AI Market Report, Global Forecast to 2032

MarketsandMarkets snapshot: generative AI market size $71.36B in 2025 growing to $890.59B by 2032 at 43.4% CAGR, Asia Pacific the fastest-growing region

The generative AI market reached $37.89 billion in 2025 and is projected to hit $1.2 trillion by 2035, a 37% compound annual growth rate. Transformer architectures account for more than 42% of technology revenue, driven by text-to-image and text-to-video applications. Software captures over 65% of total revenue. North America holds 41% of the market. Asia-Pacific is the fastest-growing region at a 27.6% CAGR through 2035. Financial services is expected to lead sector growth at 36.4%, fueled by fraud detection, risk management, and regulatory compliance demands. Source: Precedence Research, Generative AI Market Size, January 2026

Precedence Research bar chart: generative AI market size growing from $37.89B in 2025 to $1,206.24B in 2035

GPUs captured 89% of AI processor revenue in 2025, but FPGA and ASIC alternatives are growing at a 17% CAGR through 2031. Hardware accounted for 68% of all AI infrastructure spending last year. North America held 40% of the market, backed by $52.7 billion in CHIPS Act grants and hyperscalers operating roughly 60% of global AI compute capacity. Liquid cooling reached 18% of AI server racks as power densities crossed 100 kilowatts per rack, the threshold where air cooling fails. Asia-Pacific is projected to grow fastest at 16.4% CAGR through 2031, driven by China’s $50 billion semiconductor fund and $15 billion in hyperscaler commitments across India. Source: Mordor Intelligence, AI Infrastructure Market Size, Trends & Growth Drivers 2031

Mordor Intelligence snapshot: AI infrastructure market growing from $101.17B in 2026 to $202.48B in 2031 at 14.89% CAGR, with AMD, Intel, Nvidia, Microsoft Azure and AWS as major players

Nearly one in four Americans has already made a purchase through AI. Morgan Stanley Research estimates agentic shoppers will drive $190 billion to $385 billion in U.S. e-commerce spending by 2030, capturing 10% to 20% of market share. Grocery and consumer packaged goods lead adoption, with 49% of AI-assisted buyers transacting in those categories. AI shopping agent users are projected to reach 126 million by 2030, up from near zero today, while traditional e-commerce users decline from 264 million to 149 million over the same period. Source: Morgan Stanley Research, Agentic Commerce Market Impact Outlook, December 2025 Link: https://www.morganstanley.com/insights/articles/agentic-commerce-market-impact-outlook

Morgan Stanley Research charts of agentic share of e-commerce spend, 2026 to 2030: base case reaches 10% agentic by 2030, bull case reaches 20%

Mar 17

How LogicMonitor Buying Airbrake Unleashes DevOps To Do What They Do Best

Bottom Line:  LogicMonitor knows first-hand how much pressure DevOps teams are under to produce high-quality code in record time during the pandemic. Acquiring Airbrake proves they get it: DevOps has a high need for speed right now.

LogicMonitor Aims To Solve Today’s DevOps Paradox

The pandemic is forcing every business to make DevOps a core part of their DNA faster than any of them expected. The competitive strengths many banked on in a pre-pandemic world aren’t as relevant as having a steady pipeline of new apps, platforms, and digital channels are. It’s creating a paradox for DevOps: on the one hand, they’re expected to deliver perfect code, and on the other, it needs to be delivered in record time. Pre-pandemic, a typical DevOps team in a $500M+ enterprise has over 200 concurrent projects in progress, with over 70% dedicated to safeguarding and improving customer experiences according to IDC. Today, there are up to 2X more projects, and up to 80% are focused on cybersecurity.

No organization is perfect at DevOps today. Everyone is at various stages of maturity and growth. The pandemic puts a lot of pressure on DevOps teams to get their code right quickly and into a released app in record time. LogicMonitor must see it in their customer base every day. The trade-offs DevOps teams have to make for speed versus quality – and even security – when pushing out a release are real and often tend to overlook diagnostics. That’s why the Airbrake acquisition makes so much sense today. LogicMonitor bought Airbrake to help DevOps teams do what they do best.

The often-quoted Boston Consulting Group (BCG) article, Going All In With DevOps, illustrates the typical pressure DevOps is under to perform, including catching bugs early, solving them, and getting code into test and deployment. According to Airbrake, 73% of their DevOps customers are pushing code multiple times per week – and many said they were deploying code “multiple times per day.”  What makes Airbrake a perfect fit for LogicMonitor is how their developer-centric application error and performance monitoring service provides detailed diagnostics beyond the first layer of a bug or problem. In the context of the BCG graphic below, LogicMonitor buying Airbrake gives DevOps teams the diagnostics they need to move faster through error detection and into the test, deploy and release phases.

How LogicMonitor Buying Airbrake Unleashes DevOps To Do What They Do Best

Competing In Real-Time Is DevOps’ New Reality

DevOps teams are struggling to keep up with their workloads today. LogicMonitor believes that by automating more monitoring processes and providing deeper contextual data and insight, DevOps teams can improve their response times and quality.

Automation pays off with more efficient continuous integration and deployment (CI/CD) cycles across DevOps teams, speeding up time-to-market and improving software quality in the process. Buying Airbrake extends LogicMonitor into developer environments and enables their shared customers to gain visibility into CI/CD workflows while reducing risk and ensuring every code release meets customer expectations. The following graphic illustrates how the CI/CD pipelines support DevOps. The more efficient continuous integration, testing, delivery, and operations, the more code releases DevOps can deliver at a higher quality, on time, and to customers’ expectations.

How LogicMonitor Buying Airbrake Unleashes DevOps To Do What They Do Best

Source: Deloitte, DevOps Point of View, An Enterprise Architecture perspective, Amsterdam, 2020

Conclusion

The best aspect of LogicMonitor acquiring Airbrake is how practical, pragmatic, and immediately useful their vision of providing unified observability is in supporting DevOps teams under pressure to perform today. Airbrake is LogicMonitor’s second acquisition in just over a year, having also acquired Stockholm-based log analytics company Unomaly in January 2020. LogicMonitor’s Airbrake page provides additional information.

Feb 11

Machine Learning’s Greatest Potential Is Driving Revenue In The Enterprise

  • Enterprise investments in machine learning will nearly double over the next three years, reaching 64% adoption by 2020.
  • International Data Corporation (IDC) is forecasting spending on artificial intelligence (AI) and machine learning will grow from $8B in 2016 to $47B by 2020.
  • 89% of CIOs are either planning to use or are using machine learning in their organizations today.
  • 53% of CIOs say machine learning is one of their core priorities as their role expands from traditional IT operations management to business strategists.
  • CIOs are struggling to find the skills they need to build their machine learning models today, especially in financial services.

These and many other insights are from the recently published study, Global CIO Point of View. The entire report is downloadable here (PDF, 24 pp., no opt-in). ServiceNow and Oxford Economics collaborated on this survey of 500 CIOs in 11 countries on three continents, spanning 25 industries. In addition to the CIO interviews, leading experts in machine learning and its impact on enterprise performance contributed to the study. For additional details on the methodology, please see page 4 of the study and an online description of the CIO Survey Methodology here.

Digital transformation is a cornerstone of machine learning adoption. 72% of CIOs have responsibility for digital transformation initiatives that drive machine learning adoption. The survey found that the greater the level of digital transformation success, the more likely machine learning-based programs and strategies would succeed. IDC predicts that 40% of digital transformation initiatives will be supported by machine learning and artificial intelligence by 2019.

Key takeaways from the study include the following:

  • 90% of CIOs championing machine learning in their organizations today expect improved decision support that drives greater topline revenue growth. CIOs who are early adopters are most likely to pilot, evaluate and integrate machine learning into their enterprises when there is a clear connection to driving business results. Many CIO compensation plans now include business growth and revenue goals, making the revenue potential of new technologies a high priority.
  • 89% of CIOs are either planning to use or using machine learning in their organizations today. The majority, 40%, are in the research and planning phases of deployment, with an additional 26% piloting machine learning. 20% are using machine learning in some areas of their business, and 3% have successfully deployed enterprise-wide. The following graphic shows the percentage of respondents by stage of their machine learning journey.

  • Machine learning is a key supporting technology leading the majority Finance, Sales & Marketing, and Operations Management decisions today. Human intervention is still required across the spectrum of decision-making areas including Security Operations, Customer Management, Call Center Management, Operations Management, Finance and Sales & Marketing. The study predicts that by 2020, machine learning apps will have automated 70% of Security Operations queries and 30% of Customer Management ones.

  • Automation of repetitive tasks (68%), making complex decisions (54%) and recognizing data patterns (40%) are the top three most important capabilities CIOs of machine learning CIOs are most interested in.  Establishing links between events and supervised learning (both 32%), making predictions (31%) and assisting in making basic decisions (18%) are additional capabilities CIOs are looking for machine learning to accelerate. In financial services, machine learning apps are reviewing loan documents, sorting applications to broad parameters, and approving loans faster than had been possible before.

  • Machine learning adoption and confidence by CIOs varies by region, with North America in the lead (72%) followed by Asia-Pacific (61%). Just over half of European CIOs (58%) expect value from machine learning and decision automation to their company’s overall strategy. North American CIOs are more likely than others to expect value from machine learning and decision automation across a range of business areas, including overall strategy (72%, vs. 61% in Asia Pacific and 58% in Europe). North American CIOs also expect greater results from sales and marketing (63%, vs. 47% Asia-Pacific and 38% in Europe); procurement (50%, vs. 34% in Asia-Pacific and 34% in Europe); and product development (48%, vs. 29% in Asia-Pacific and 29% in Europe).
  • CIOs challenging the status quo of their organization’s analytics direction are more likely to rely on roadmaps for defining and selling their vision of machine learning’s revenue contributions. More than 70% of early adopter CIOs have developed a roadmap for future business process changes compared with just 33% of average CIOs. Of the CIOs and senior management teams in financial services, the majority are looking at how machine learning can increase customer satisfaction, lifetime customer value, improving revenue growth. 53% of CIOs from our survey say machine learning is one of their core priorities as their role expands from traditional IT operations to business-wide strategy.

Sources: CIOs Cutting Through the Hype and Delivering Real Value from Machine Learning, Survey Shows

Jul 3

4 Comments

Roundup Of Analytics, Big Data & Business Intelligence Forecasts And Market Estimates, 2014

NYC SkylineFrom manufacturers looking to gain greater insights into streamlining production, reducing time-to-market and increasing product quality to financial services firms seeking to upsell clients, analytics is now essential for any business looking to stay competitive.  Marketing is going through its own transformation, away from traditional tactics to analytics- and data-driven strategies that deliver measurable results.

Analytics and the insights they deliver are changing competitive dynamics daily by delivering greater acuity and focus.  The high level of interest and hype surrounding analytics, Big Data and business intelligence (BI) is leading to a proliferation of market projections and forecasts, each providing a different perspective of these markets.

Presented below is a roundup of recent forecasts and market estimates:

  • The Advanced and Predictive Analytics (APA) software market is projected from grow from $2.2B in 2013 to $3.4B in 2018, attaining a 9.9% CAGR in the forecast period.  The top 3 vendors in 2013 based on worldwide revenue were SAS ($768.3M, 35.4% market share), IBM ($370.3M, 17.1% market share) and Microsoft ($64.9M, 3% market share).  IDC commented that simplified APA tools that provide less flexibility than standalone statistical models tools yet have more intuitive graphical user interfaces and easier-to-use features are fueling business analysts’ adoption.  Source: http://www.idc.com/getdoc.jsp?containerId=249054
  • Cloud-based Business Intelligence (BI) is projected to grow from $.75B in 2013 to $2.94B in 2018, attaining a CAGR of 31%.  Redwood Capital’s recent Sector Report on Business Intelligence  (free, no opt in) provides a thorough analysis of the current and future direction of BI.  Redwood Capital segments the BI market into traditional, mobile, cloud and social business intelligence.   The following two charts from the Sector Report on Business Intelligence  illustrate how Redwood Capital sees the progression of the BI market through 2018.

redwood capital global intelligence market size

  • Enterprises getting the most value out of analytics and BI have leaders that concentrate more on collaboration, instilling confidence in their teams, and creating an active analytics community, while laggards focus on technology alone.  A.T. Kearney and Carnegie Mellon University recently surveyed 430 companies around the world, representing a wide range of geographies and industries, for the inaugural Leadership Excellence in Analytic Practices (LEAP) study.  You can find the study here.  The following is a graphic from the study comparing the characteristics of leaders and laggards’ strategies for building a culture of analytics excellence.

leaders and laggards2

  • Big Data is projected to be a $28.5B market in 2014, growing to $50.1B in 2015 according to Wikkbon.  Their report, Big Data Vendor Revenue and Market Forecast 2013-2017 is outstanding in its accuracy and depth of analysis.  The following is a graphic from the study, illustrating Wikibon’s Big Data market forecast broken down by market component through 2017.

Big Data Wikibon

  • SAP, IBM, SAS, Microsoft, Oracle, Information Builders, MicroStrategy, and Actuate are market leaders in BI according to Forrester’s latest Wave analysis of BI platforms.  Their report, The Forrester Wave™: Enterprise Business Intelligence Platforms, Q4 2013 (free PDF, no opt in, courtesy of SAS) provides a thorough analysis of 11 different BI software providers using the research firm’s 72-criteria evaluation methodology.
  • Amazon Web Services, Cloudera, Hortonworks, IBM, MapR Technologies, Pivotal Software, and Teradata are Big Data Hadoop market leaders according to Forrester’s latest Wave analysis of Hadoop Solutions.  Their report, The Forrester Wave™: Big Data Hadoop Solutions, Q1 2014 (free PDF, no opt in, courtesy of MapR Technologies) provides a thorough analysis of nine different Big Data Hadoop software providers using the research firm’s 32-criteria evaluation methodology.
  • IDC forecasts the server market for high performance data analysis (HPDA) will grow at a 23.5% compound annual growth rate (CAGR) reaching $2.7B by 2018.  In the same series of studies IDC forecasts the related storage market will expand to $1.6B also in 2018. HPDA is the term IDC created to describe the formative market for big data workloads using HPC. Source: http://www.idc.com/getdoc.jsp?containerId=prUS24938714
  • Big Data technology and services will grow from $1.95B in 2013 to $9.83B in 2020, attaining a CAGR of 26%.  These and other market forecasts and projections are in a report Huawei sponsored titled Big Data & Advanced Analytics in Telecom: A Multi-Billion-Dollar Revenue Opportunity.  A chart from the study is shown below that shows Big Data analytics market size by business category.

big data analytics by market size

  • Financial Services firms are projected to spend $6.4B in Big Data-related hardware, software and services in 2015, growing at a CAGR of 22% through 2020.  Software and internet-related companies are projected to spend $2.8B in 2015, growing at a CAGR of 26% through 2020.  These and other market forecasts and projections can be found in Bain & Company’s Insights Analysis, Big Data: The Organizational Challenge.  An infographic of their research results are shown below.

Big-Data-infographic-Bain & Company

potential payback of big data initiatives

Jul 19

5 Comments

Sizing the Cloud Computing Market

The pace of cloud computing market forecasts produced and announced is quickening, with several new projections announced this month. In every one of these forecasts, the benefits of operating expense (OPEX) versus capital expense (CAPEX) financing play a role, as does the emerging trust in cloud computing as a viable platform.

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