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Posts from the ‘AI Security’ Category

Feb 27

10 Ways AI & Machine Learning Are Revolutionizing Omnichannel

Disney, Oasis, REI, Starbucks, Virgin Atlantic, and others excel at delivering omnichannel experiences using AI and machine learning to fine-tune their selling and service strategies. Source: iStock

Bottom Line: AI and machine learning are enabling omnichannel strategies to scale by providing insights into the changing needs and preferences of customers, creating customer journeys that scale, delivering consistent experiences.

For any omnichannel strategy to succeed, each customer touchpoint needs to be orchestrated as part of an overarching customer journey. That’s the only way to reduce and eventually eliminate customers’ perceptions of using one channel versus another. What makes omnichannel so challenging to excel at is the need to scale a variety of customer journeys in real-time as customers are also changing.

89% of customers used at least one digital channel to interact with their favorite brands and just 13% found the digital-physical experiences well aligned according to Accenture’s omnichannel study. AI and machine learning are being used to close these gaps with greater intelligence and knowledge. Omnichannel strategists are fine-tuning customer personas, measuring how customer journeys change over time, and more precisely define service strategies using AI and machine learning. Disney, Oasis, REI, Starbucks, Virgin Atlantic, and others excel at delivering omnichannel experiences using AI and machine learning for example.

Omnichannel leaders including Amazon use AI and machine learning to anticipate which customer personas prefer to speak with a live agent versus using self-service for example. McKinsey also found omnichannel customer care expectations fall into the three categories of speed and flexibility, reliability and transparency, and interaction and care. Omnichannel customer journeys designed deliver on each of these three categories excel and scale between automated systems and live agents as the following example from the McKinsey article, How to capture what the customer wants illustrate:

The foundation all great omnichannel strategies are based on precise customer personas, insight into how they are changing, and how supply chains and IT need to flex and change too. AI and machine learning are revolutionizing omnichannel on these three core dimensions with greater insight and contextual intelligence than ever before.

10 Ways AI & Machine Learning Are Revolutionizing Omnichannel

The following are 10 ways AI & machine learning are revolutionizing omnichannel strategies starting with customer personas, their expectations, and how customer care, IT infrastructure and supply chains need to stay responsive to grow.

  1. AI and machine learning are enabling brands, retailers and manufacturers to more precisely define customer personas, their buying preferences, and journeys. Leading omnichannel retailers are successfully using AI and machine learning today to personalize customer experiences to the persona level. They’re combining brand, event and product preferences, location data, content viewed, transaction histories and most of all, channel and communication preferences to create precise personas of each of their key customer segments.
  2. Achieving price optimization by persona is now possible using AI and machine learning, factoring in brand and channel preferences, previous purchase history, and price sensitivity. Brands, retailers, and manufacturers are saying that cloud-based price optimization and management apps are easier to use and more powerful based on rapid advances in AI and machine learning algorithms than ever before. The combination of easier to use, more powerful apps and the need to better manage and optimize omnichannel pricing is fueling rapid innovation in this area. The following example is from Microsoft Azure’s Interactive Pricing Analytics Pre-Configured Solution (PCS). Source: Azure Cortana Interactive Pricing Analytics Pre-Configured Solution.

  1. Capitalizing on insights gained from AI and machine learning, omnichannel leaders are redesigning IT infrastructure and integration so they can scale customer experiences. Succeeding with omnichannel takes an IT infrastructure capable of flexing quickly in response to change in customers’ preferences while providing scale to grow. Every area of a brand, retailer or manufacturer’s supply chain from their supplier onboarding, quality management and strategic sourcing to yard management, dock scheduling, manufacturing, and fulfillment need to be orchestrated around customers. Leaders include C3 Solutions who offers a web-based Yard Management System (YMS) and Dock Scheduling System that can integrate with ERP, Supply Chain Management (SCM), Warehouse Management Systems (WMS) and many others via APIs. The following graphic illustrates how omnichannel leaders orchestrate IT infrastructure to achieve greater growth. Source: Cognizant, The 2020 Customer Experience.

  1. Omnichannel leaders are relying on AI and machine learning to digitize their supply chains, enabling on-time performance, fueling faster revenue growth. For any omnichannel strategy to succeed, supply chains need to be designed to excel at time-to-market and time-to-customer performance at scale. 54% of retailers pursuing omnichannel strategies say that their main goal in digitizing their supply chains was to deliver greater customer experiences. 45% say faster speed to market is their primary goal in digitizing their supply chain by adding in AI and machine learning-driven intelligence. Source: Digitize Today To Future-Proof Tomorrow (PDF, 16 pp., opt-in).

  1. AI and machine learning algorithms are making it possible to create propensity models by persona, and they are invaluable for predicting which customers will act on a bundling or pricing offer. By definition propensity models rely on predictive analytics including machine learning to predict the probability a given customer will act on a bundling or pricing offer, e-mail campaign or other call-to-action leading to a purchase, upsell or cross-sell. Propensity models have proven to be very effective at increasing customer retention and reducing churn. Every business excelling at omnichannel today rely on propensity models to better predict how customers’ preferences and past behavior will lead to future purchases. The following is a dashboard that shows how propensity models work. Source: customer propensities dashboard is from TIBCO.

  1. Combining machine learning-based pattern matching with a product-based recommendation engine is leading to the development of mobile-based apps where shoppers can virtually try on garments they’re interested in buying. Machine learning excels at pattern recognition, and AI is well-suited for creating recommendation engines, which are together leading to a new generation of shopping apps where customers can virtually try on any garment. The app learns what shoppers most prefer and also evaluates image quality in real-time, and then recommends either purchase online or in a store. Source: Capgemini, Building The Retail Superstar: How unleashing AI across functions offers a multi-billion dollar opportunity.

  1. 56% of brands and retailers say that order track-and-traceability strengthened with AI and machine learning is essential to delivering excellent customer experiences. Order tracking across each channel combined with predictions of allocation and out-of-stock conditions using AI and machine learning is reducing operating risks today. AI-driven track-and-trace is invaluable in finding where there are process inefficiencies that slow down time-to-market and time-to-customer. Source: Digitize Today To Future-Proof Tomorrow (PDF, 16 pp., opt-in).
  2. Gartner predicts that by 2025, customer service organizations who embed AI in their customer engagement center platforms will increase operational efficiencies by 25%, revolutionizing customer care in the process. Customer service is often where omnichannel strategies fail due to lack of real-time contextual data and insight. There’s an abundance of use cases in customer service where AI and machine learning can improve overall omnichannel performance. Amazon has taken the lead on using AI and machine learning to decide when a given customer persona needs to speak with a live agent. Comparable strategies can also be created for improving Intelligent Agents, Virtual Personal Assistants, Chatbot and Natural Language (NLP) performance.  There’s also the opportunity to improve knowledge management, content discovery and improve field service routing and support.
  3. AI and machine learning are improving marketing and selling effectiveness by being able to track purchase decisions back to campaigns by channel and understand why specific personas purchased while others didn’t. Marketing is already analytically driven, and with the rapid advances in AI and machine learning, markets will for the first time be able to isolate why and where their omnichannel strategies are succeeding or failing. By using machine learning to qualify the further customer and prospect lists using relevant data from the web, predictive models including machine learning can better predict ideal customer profiles. Each omnichannel sales lead’s predictive score becomes a better predictor of potential new sales, helping sales prioritize time, sales efforts and selling strategies.
  4. Predictive content analytics powered by AI and machine learning are improving sales close rates by predicting which content will lead a customer to buy. Analyzing previous prospect and buyer behavior by persona using machine learning provides insights into which content needs to be personalized and presented when to get a sale. Predictive content analytics is proving to be very effective in B2B selling scenarios, and are scaling into consumer products as well

Dec 12

Using Machine Learning To Find Employees Who Can Scale With Your Business

  • Eightfold’s analysis of hiring data has found the half-life of technical, marketable skills is 5 to 7 years, making the ability to unlearn and learn new concepts essential for career survival.
  • Applicant Tracking Systems (ATS) don’t capture applicants’ drive and intensity to unlearn and learn or their innate capabilities for growth.
  • Artificial Intelligence (AI) and machine learning are proving adept at discovering candidates’ innate capabilities to unlearn, learn and reinvent themselves throughout their careers.

Hiring managers in search of qualified job candidates who can scale with and contribute to their growing businesses are facing a crisis today. They’re not finding the right or in many cases, any candidates at all using resumes alone, Applicant Tracking Systems (ATS) or online job recruitment sites designed for employers’ convenience first and candidates last. These outmoded approaches to recruiting aren’t designed to find those candidates with the strongest capabilities. Add to this dynamic the fact that machine learning is making resumes obsolete by enabling employers to find candidates with precisely the right balance of capabilities needed and its unbiased data-driven approach selecting candidates works. Resumes, job recruitment sites and ATS platforms force hiring managers to bet on the probability they make a great hire instead of being completely certain they are by basing their decisions on solid data.

Playing The Probability Hiring Game Versus Making Data-Driven Decisions

Many hiring managers and HR recruiters are playing the probability hiring game. It’s betting that the new hire chosen using imprecise methods will work out. And like any bet, it gets expensive quickly when a wrong choice is made. There’s a 30% chance the new hire will make it through one year, and if they don’t, it will cost at least 1.5 times their salary to replace them. When the median salary for a cloud computing professional is $146,350, and it takes the best case 46 days to find them, the cost and time loss of losing just one recruited cloud computing professional can derail a project for months. It will cost at least $219,000 or more to replace just that one engineer. The average size of an engineering team is ten people so only three will remain in 12 months. These are the high costs of playing the probability hiring game, fueled by unconscious and conscious biases and systems that game recruiters into believing they are making progress when they’re automating mediocre or worse decisions. Hiring managers will have better luck betting in Las Vegas or playing Powerball than hiring the best possible candidate if they rely on systems that only deliver a marginal probability of success at best.

Betting on solid data and personalization at scale, on the other hand, delivers real results. Real data slices through the probabilities and is the best equalizer there is at eradicating conscious and unconscious biases from hiring decisions. Hiring managers, HR recruiters, directors and Chief Human Resource Officers (CHROs) vow they are strong believers in diversity. Many are abandoning the probability hiring game for AI- and machine learning-based approaches to talent management that strip away any extraneous data that could lead to bias-driven hiring decisions. Now candidates get evaluated on their capabilities and innate strengths and how strong a match they are to ideal candidates for specific roles.

A Data-Driven Approach To Finding Employees Who Can Scale

Personalization at scale is more than just a recruiting strategy; it’s a talent management strategy intended to flex across the longevity of every employees’ tenure. Attaining personalization at scale is essential if any growing business is going to succeed in attracting, acquiring and growing talent that can support their growth goals and strategies. Eightfold’s approach makes it possible to scale personalized responses to specific candidates in a company’s candidate community while defining the ideal candidate for each open position. Personalization at scale has succeeded in helping companies find the right person to the right role at the right time and, for the first time, personalize every phase of recruitment, retention and talent management at scale.

Eightfold is pioneering the use of a self-updating corporate candidate database. Profiles in the system are now continually updated using external data gathering, without applicants reapplying or submitting updated profiles. The taxonomies supported in the corporate candidate database make it possible for hiring managers to define the optimal set of capabilities, innate skills, and strengths they need to fill open positions.

Lessons Learned at PARC
Russell Williams, former Vice President of Human Resources at PARC, says the best strategy he has found is to define the ideal attributes of high performers and look to match those profiles with potential candidates. “We’re finding that there are many more attributes that define a successful employee in our most in-demand positions including data scientist that are evident from just reviewing a resume and with AI, I want to do it at scale,” Russell said. Ashutosh Garg, Eightfold founder, added: “that’s one of the greatest paradoxes that HR departments face, which is the need to know the contextual intelligence of a given candidate far beyond what a resume and existing recruiting systems can provide.”  One of the most valuable lessons learned from PARC is that it’s possible to find the find candidates who excel at unlearning, learning, defining and diligently pursuing their learning roadmaps that lead to reinventing their skills, strengths, and marketability.

Conclusion

Machine learning algorithms capable of completing millions of pattern matching comparisons per second provides valuable new insights, enabling companies to find those who excel at reinventing themselves. The most valuable employees who can scale any business see themselves as learning entrepreneurs and have an inner drive to master new knowledge and skills. And that select group of candidates is the catalyst most often responsible for making the greatest contributions to a company’s growth.

Jul 12

Zero Trust Security Update From The SecurIT Zero Trust Summit

  • Identities, not systems, are the new security perimeter for any digital business, with 81% of breaches involving weak, default or stolen passwords.
  • 53% of enterprises feel they are more susceptible to threats since 2015.
  • 51% of enterprises suffered at least one breach in the past 12 months and malicious insider incidents increased 11% year-over-year.

These and many other fascinating insights are from SecurIT: the Zero Trust Summit for CIOs and CISOs held last month in San Francisco, CA. CIO and CSO produced the event that included informative discussions and panels on how enterprises are adopting Next-Gen Access (NGA) and enabling Zero Trust Security (ZTS). What made the event noteworthy were the insights gained from presentations and panels where senior IT executives from Akamai, Centrify, Cisco, Cylance, EdgeWise, Fortinet, Intel, Live Nation Entertainment and YapStone shared their key insights and lessons learned from implementing Zero Trust Security.

Zero Trust’s creator is John Kindervag, a former Forrester Analyst, and Field CTO at Palo Alto Networks.  Zero Trust Security is predicated on the concept that an organization doesn’t trust anything inside or outside its boundaries and instead verifies anything and everything before granting access. Please see Dr. Chase Cunningham’s excellent recent blog post, What ZTX means for vendors and users, for an overview of the current state of ZTS. Dr. Chase Cunningham is a Principal Analyst at Forrester.

Key takeaways from the Zero Trust Summit include the following:

  • Identities, not systems, are the new security perimeter for any digital business, with 81% of breaches involving weak, default or stolen passwords. Tom Kemp, Co-Founder, and CEO, Centrify, provided key insights into the current state of enterprise IT security and how existing methods aren’t scaling completely enough to protect every application, endpoint, and infrastructure of any digital business. He illustrated how $86B was spent on cybersecurity, yet a stunning 66% of companies were still breached. Companies targeted for breaches averaged five or more separate breaches already. The following graphic underscores how identities are the new enterprise perimeter, making NGA and ZTS a must-have for any digital business.

  • 53% of enterprises feel they are more susceptible to threats since 2015. Chase Cunningham’s presentation, Zero Trust and Why Does It Matter, provided insights into the threat landscape and a thorough definition of ZTX, which is the application of a Zero Trust framework to an enterprise. Dr. Cunningham is a Principal Analyst at Forrester Research serving security and risk professionals. Forrester found the percentage of enterprises who feel they are more susceptible to threats nearly doubled in two years, jumping from 28% in 2015 to 53% in 2017. Dr. Cunningham provided examples of how breaches have immediate financial implications on the market value of any business with specific focus on the Equifax breach.

Presented by Dr. Cunningham during SecurIT: the Zero Trust Summit for CIOs and CISOs

  • 51% of enterprises suffered at least one breach in the past 12 months and malicious insider incidents increased 11% year-over-year. 43% of confirmed breaches in the last 12 months are from an external attack, 24% from internal attacks, 17% are from third-party incidents and 16% from lost or stolen assets. Consistent with Verizon’s 2018 Data Breach Investigations Report use of privileged credential access is a leading cause of breaches today.

Presented by Dr. Cunningham during SecurIT: the Zero Trust Summit for CIOs and CISOs

                       

  • One of Zero Trust Security’s innate strengths is the ability to flex and protect the perimeter of any growing digital business at the individual level, encompassing workforce, customers, distributors, and Akamai, Cisco, EdgeWise, Fortinet, Intel, Live Nation Entertainment and YapStone each provided examples of how their organizations are relying on NGA to enable ZTS enterprise-wide. Every speaker provided examples of how ZTS delivers several key benefits including the following: First, ZTS reduces the time to breach detection and improves visibility throughout a network. Second, organizations provided examples of how ZTS is reducing capital and operational expenses for security, in addition to reducing the scope and cost of compliance initiatives. All companies presenting at the conference provided examples of how ZTS is enabling greater data awareness and insight, eliminating inter-silo finger-pointing over security responsibilities and for several, enabling digital business transformation. Every organization is also seeing ZTS thwart the exfiltration and destruction of their data.

Conclusion

The SecurIT: the Zero Trust Summit for CIOs and CISOs event encapsulated the latest advances in how NGA is enabling ZTS by having enterprises who are adopting the framework share their insights and lessons learned. It’s fascinating to see how Akamai, Cisco, Intel, Live Nation Entertainment, YapStone, and others are tailoring ZTS to their specific customer-driven goals. Each also shared their plans for growth and how security in general and NGA and ZTS specifically are protecting customer and company data to ensure growth continues, uninterrupted.

 

 

Nov 17

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Google Cloud technologies overview, 2026 edition, with a 2027 outlook

Updated 12 November 2026. This post first appeared on 17 November 2010 as a short note on Google App Engine and the Google Labs cloud APIs. It is the most visited page on this site this month. So the text below replaces the original with a Google Cloud technologies overview for 2026 and a look at 2027. The 2010 version is summarized in the first section for anyone who arrived looking for it.

Google Cloud technologies overview 2026 hero graphic: quarterly revenue from $15.2 billion in Q3 2025 to $30.7 billion in Q3 2026, contracted backlog from $155 billion to $561 billion, a table mapping the 2010 Google cloud stack of App Engine, Google Storage, Prediction API, BigQuery and Google Labs to their 2026 names, and key figures of $30.7 billion revenue, $561 billion backlog, 36.2% operating margin and 16% market share
Google Cloud by the numbers in 2026. Quarterly revenue and contracted backlog for the five quarters through Q3 2026, what each piece of the 2010 stack is called today, and the four figures that define the business at the end of 2026. Click any chart to open it full size. Sources linked below, analysis by softwarestrategiesblog.com.

Google Cloud reported $30.7 billion of revenue for the third quarter of 2026, up 103% from $15.2 billion a year earlier. Operating income reached $11.1 billion, a 36.2% margin, compared to $3.6 billion and 23.7% a year earlier. Contracted backlog stood at $561 billion at the end of September, up from $155 billion twelve months before. Alphabet expects to recognize just over 55% of it as revenue within 24 months. The business this post described in 2010 did not disclose revenue at all. In 2026 it is the fastest growing of the three largest clouds and Alphabet’s second largest source of operating income.

This Google Cloud technologies overview covers four things. It starts with what the 2010 stack looked like and what each piece is called today. It then works through the financial record from the first disclosure in 2020 to the Q3 2026 results. The third section compares Google Cloud with AWS and Microsoft Azure using Synergy Research Group’s Q3 2026 market data. After that comes the platform announced at Google Cloud Next in April 2026. The final section sets out what the backlog, capex guidance and competitor commentary already say about 2027.

What the 2010 Google cloud stack became

In November 2010 Google’s cloud effort was a developer evangelism program. The original post pointed readers to the Google Labs catalog and to a slide deck by Google developer advocate Chris Schalk. The deck covered Google Storage for Developers, the Prediction API and BigQuery, all three of them in preview. App Engine, launched in 2008, was the production product at the time. The post contrasted Google’s incremental approach with how often Amazon Web Services changed its platform, pricing and services. Google did not report cloud revenue then, since it had no cloud segment to report.

Almost every element of that stack still exists under a different name. Google Storage for Developers became Cloud Storage. BigQuery left preview in 2011 and now anchors what Google calls the Agentic Data Cloud. The Prediction API was shut down in 2018. Its role passed to Cloud Machine Learning Engine, then to Vertex AI in 2021, and in April 2026 to the Gemini Enterprise Agent Platform. Google Labs closed in 2011, and Google Cloud Next is where new services launch instead. App Engine, in fact, remains available inside Google Cloud. Google itself became a subsidiary of Alphabet in 2015. Alphabet first reported Google Cloud as a segment in February 2020, disclosing $8.9 billion of 2019 revenue. The 2018 figure was $5.8 billion and the 2017 figure $4.1 billion.

Two businesses that did not exist in 2010

The first is security. Google Cloud bought Mandiant in September 2022 for $5.4 billion. It then closed its $32 billion acquisition of Wiz on 11 March 2026, the largest purchase in Alphabet’s history. The second is custom silicon sold to customers rather than used internally. The Tensor Processing Unit did not reach outside developers until 2018. In 2026 Alphabet counts more than 100 significant TPU customers and sells TPU systems for installation in customers’ own data centers.

Google Cloud revenue went from $4 billion a year to $30.7 billion a quarter

Google Cloud annual revenue 2017 to 2026 from Alphabet filings: $4.1 billion in 2017, $8.9 billion in 2019, $13.1 billion in 2020, $26.3 billion in 2022, $43.2 billion in 2024, $58.7 billion in 2025, $75.5 billion in the first nine months of 2026 and a $122.8 billion Q3 2026 run rate, with operating income of $1.7 billion in 2023, $6.1 billion in 2024, $13.9 billion in 2025 and $26.5 billion in the first nine months of 2026
Google Cloud segment revenue by year, from Alphabet’s 10-K and 10-Q filings. The segment lost money every year through 2022, earned $1.7 billion in 2023 and $26.5 billion in the first nine months of 2026. Sources linked below, analysis by softwarestrategiesblog.com.

Google Cloud revenue has grown every year since Alphabet began disclosing it. Segment revenue was $13.1 billion in 2020 and $19.2 billion in 2021. It then reached $26.3 billion in 2022, $33.1 billion in 2023 and $43.2 billion in 2024. Alphabet’s 2025 Form 10-K puts 2025 revenue at $58.7 billion. The first nine months of 2026 added $75.5 billion, more than the whole of 2025. At the Q3 2026 rate the segment is on a $123 billion annual run rate. That is roughly fourteen times the $8.9 billion it reported for 2019, before the segment turned a profit.

Profitability arrived later than scale. The segment reported operating losses in every year through 2022. It then earned $1.7 billion in 2023, $6.1 billion in 2024 and $13.9 billion in 2025. The first nine months of 2026 produced $26.5 billion, almost double the full year before, according to Alphabet’s Q3 2026 Form 10-Q. Operating margin moved from 5.2% in 2023 to 14.1% in 2024, 23.7% in 2025 and 36.2% in the latest quarter.

Growth accelerated for five straight quarters

The acceleration is recent and steep. Google Cloud grew 34% in Q3 2025 and 48% in Q4 2025. It grew 63% in Q1 2026, 82% in Q2 2026 and 103% in Q3 2026. Quarterly revenue went from $15.2 billion to $30.7 billion over those five quarters. Alphabet attributes the growth to Google Cloud Platform, and within it to AI infrastructure and enterprise AI solutions. Core GCP products grew as well, though more slowly. Eighteen Google Cloud product lines each generate more than $1 billion a year. About 90% of the Fortune 100 use Gemini Enterprise, according to the Q3 2026 earnings call.

Backlog grew faster than revenue. Remaining performance obligations are the contracted revenue not yet recognized. They went from $155 billion in Q3 2025 to $240 billion in Q4 2025 and $462 billion in Q1 2026. They then reached $514 billion in Q2 2026 and $561 billion in Q3 2026. At the Q2 call Alphabet said just over 50% of backlog would convert to revenue within 24 months. By the Q3 10-Q that figure was just over 55%. So about $309 billion of contracted revenue sits inside the next two years before any new signings.

Google Cloud gains share while AWS loses it

AWS, Microsoft Azure and Google Cloud compared for Q3 2026: revenue of $46.0 billion, $33.2 billion and $30.7 billion, year over year growth of 39%, 48% and 103%, and Synergy Research market share of 26%, 19% and 16% against 29%, 20% and 13% a year earlier
The three largest cloud providers in Q3 2026. AWS remains the largest, Google Cloud grows more than twice as fast as either rival, and Synergy Research has Google Cloud gaining three points of share in a year while AWS loses three. Sources linked below, analysis by softwarestrategiesblog.com.

Synergy Research Group puts worldwide cloud infrastructure services revenue at $167.7 billion for Q3 2026, up 55% from a year earlier. It was the twelfth consecutive quarter in which the growth rate increased. Trailing twelve-month revenue, as a result, reached $558 billion. Within that market Synergy has Amazon at 26% share, down from 29% a year earlier. Microsoft holds 19% against 20%, and Google 16% against 13%. Oracle, Alibaba, SpaceX and xAI, CoreWeave, Salesforce and Anthropic each hold between 2% and 4%.

The company results line up with the shares. AWS reported $46.0 billion of Q3 2026 revenue, up 39%. Its operating income was $17.8 billion, so its run rate is $184 billion at a 38.7% margin. Microsoft disclosed Azure revenue in dollars for the first time at $33.2 billion, up 48%, a $133 billion run rate. Google Cloud’s $30.7 billion, up 103%, sits $2.5 billion behind Azure and $15.3 billion behind AWS for the quarter. A year earlier the gaps were larger on every measure. Google Cloud was growing 34% then, while AWS grew 20% and Azure 40%.

Google Cloud’s margin now sits between its two rivals. AWS earned a 38.7% operating margin in Q3 2026 while Google Cloud earned 36.2%. Microsoft, by comparison, does not disclose Azure operating income. For comparison, Synergy’s Q3 2022 shares were 34% for Amazon, 21% for Microsoft and 11% for Google. Google has gained five points of share in four years while Amazon has lost eight.

The 2026 platform is built around agents and TPUs

Google Cloud Next 2026 ran from 22 to 24 April in Las Vegas. It drew more than 32,000 attendees and produced 260 announcements. The center of them was the Gemini Enterprise Agent Platform, the renamed and extended Vertex AI. It adds Agent Studio for building agents and an Agent Registry for cataloging them. Agent Identity gives each agent its own credentials, Agent Gateway enforces policy on every call, and Agent Observability traces what agents do. The point of the five components is that an enterprise can build, authorize, route and audit agents in one place rather than stitching controls together from separate products.

Infrastructure announcements followed the same theme. TPU 8t is the eighth generation training chip. It scales to 9,600 chips and 2 petabytes of high bandwidth memory in a single superpod. Google rates it at three times the compute of Ironwood, twice the performance per watt and 2.7 times the training performance per dollar. TPU 8i is the inference part, with 1,152 chips per pod and an 80% improvement in inference performance per dollar compared to the prior generation. Google also described Virgo, a custom network for connecting TPU supercomputers into larger clusters. The Agentic Data Cloud rounds it out. Its Cross-Cloud Lakehouse is standardized on Apache Iceberg, so BigQuery can query data where it sits, including data left in AWS.

The money behind the platform is unusually visible. Alphabet spent $51.4 billion on capital expenditure in Q3 2026. It held its 2026 guidance at $195 billion to $205 billion. Total Alphabet revenue reached $127.6 billion in the quarter, up 25%, with $40.6 billion of operating income. The Gemini app also passed one billion monthly users during the quarter.

Security is Google Cloud’s $37 billion acquisition story

Google Cloud entered security by acquisition. Mandiant, bought for $5.4 billion in 2022, brought incident response and threat intelligence. Wiz was announced in March 2025 for $32 billion and closed on 11 March 2026 for $29.5 billion after adjustments. It brought a multicloud security platform that Google has kept as a separate brand since the deal closed. Wiz still works across AWS, Azure and Oracle as well as Google Cloud. The purchase added $22.7 billion of goodwill to the Google Cloud segment. That is what Alphabet paid for the customer base, the engineers and the market position rather than the code.

The combined products now ship under Google AI Threat Defense. It pairs Gemini models with Wiz, Mandiant intelligence and the CodeMender code repair agent. Wiz, for its part, introduced Red, Blue and Green agents for offensive testing, defensive response and remediation. Google’s security blog describes the goal as an agentic security operations center. In it, agents triage alerts, investigate and recommend responses while analysts approve the steps. For readers of this site, that matters for one reason. The controls Google ships for its own agents, Agent Identity and Agent Gateway in particular, are the same controls enterprises need for agents built on any platform.

What 2027 already looks like

Three numbers already shape 2027, starting with the backlog. Just over 55% of $561 billion converts within 24 months, so roughly $309 billion of revenue is contracted before Google Cloud sells anything new. Spread evenly, that alone is about $155 billion a year, above the current $123 billion run rate. The second is capital spending, which is still rising. Alphabet told investors on the Q3 2026 call to expect capex to increase significantly in 2027. The 2026 range is $195 billion to $205 billion, and the Cloud segment carries much of the depreciation. Operating margin rose from 23.7% to 36.2% in a year while that spending ramped. The question for 2027 is whether margin keeps rising as the depreciation catches up.

The third is the competition’s own guidance. Amazon CEO Andy Jassy told analysts he expects AWS to grow more than 40% in 2027 and 2028. To keep gaining share, Google Cloud has to grow faster than that from a base now two thirds the size of AWS. Synergy’s data has the entire market compounding at over 50%. The test of 2027 is whether the three leaders can all grow above 40% at the same time. That has never happened at this scale.

Two other threads run into 2027. TPU systems are now sold into customers’ own data centers, with Oracle and Crux AI named as early buyers. That turns Google’s silicon into a product line rather than an internal advantage. Wiz plus Mandiant gives Google Cloud a security business that competes with CrowdStrike and Palo Alto Networks as much as with AWS. Neither chips for sale nor security acquisitions were part of the plan when App Engine was the product. The 2010 version of this post did not see either one coming.

Key takeaways

  • Google Cloud doubled in a year. Q3 2026 revenue of $30.7 billion was up 103% from $15.2 billion. Growth also accelerated in each of the last five quarters.
  • The backlog is the forecast. Contracted backlog reached $561 billion, with just over 55% converting within 24 months. That puts about $309 billion of revenue under contract for the next two years.
  • Margins moved from 5% to 36% in three years. Operating margin went from 5.2% in 2023 to 36.2% in Q3 2026, on $11.1 billion of quarterly operating income.
  • Google Cloud is the share gainer. Synergy Research puts it at 16% of a $168 billion quarterly market, up from 13%. AWS, in contrast, fell from 29% to 26%.
  • The 2010 stack survived under new names. Google Storage is Cloud Storage and BigQuery anchors the Agentic Data Cloud. The Prediction API’s role, in turn, now belongs to the Gemini Enterprise Agent Platform.
  • Security is a $37 billion acquisition story. Mandiant at $5.4 billion in 2022 and Wiz at $32 billion in 2026 gave Google Cloud a security business that sells across every major cloud.
  • 2027 is a capex and margin test. Alphabet expects capex to rise significantly from the 2026 range of $195 billion to $205 billion. AWS guides to more than 40% growth, so share gains require Google Cloud to grow faster than that.

Sources

This analysis is my own and reflects public filings, earnings calls and vendor announcements. It does not represent the views of my employer.

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