AI Markets Group

Enterprise AI’s real battleground is not the model. It is the context layer. Whoever controls the governed link between enterprise data, workflows and AI agents may own the next strategic control point. The race is on. Read AIMG’s latest Market Insight: https://lnkd.in/eD2uyX7M

Enterprise AI’s real battleground is not the model. It is the context layer. Whoever controls the governed link between enterprise data, workflows and AI agents may own the next strategic control point. The race is on. Read AIMG’s latest Market Insight: https://lnkd.in/eD2uyX7M

The Context Layer may be one of the most important - and least understood - layers in enterprise AI. AIMG estimates the market for context-layer software will grow from $8.3B in 2026 to $24.5B by 2030 - a 31% CAGR. But the bigger story is not the TAM. It is the strategic value of context: governed ontologies, knowledge graphs, lineage, policies, memory, decision traces, and the organizational knowledge that enables AI agents to operate reliably. As retrieval becomes increasingly commoditized, these accumulated context assets could become a critical source of differentiation - and a significant barrier to replacement. Our latest AIMG Insight explores why context is emerging as a foundational layer of enterprise AI, where the value is concentrating, and how the economics may evolve as AI agents replace traditional software interactions. The question for enterprise leaders is no longer just: "What model should we use?" It is increasingly: "What context will make our AI systems uniquely effective?" See: https://lnkd.in/eaHpRgzW

Everyone in AI is talking about “context.” Few can actually define it. Ask what context is, what it is made of, how its components relate to each other, or how it is actually delivered to AI systems - and the answers quickly become vague. That is becoming a problem. As with so much in the AI market, a useful concept is rapidly becoming a source of hype, noise, and vendor positioning before there is even a common taxonomy for what it means. AIMG has been digging beneath the hype. Our latest research examines the Context Layer: what it is, its underlying components, how it operates, and why it is emerging as a critical layer between enterprise data and AI execution. The bigger point is simple: When everyone is talking about something, the real value is often in defining what they are actually talking about. At AIMG, that is what we do: cut through AI market noise and find the strategic signal. For more information see https://lnkd.in/eaHpRgzW and https://lnkd.in/e4Zaqzva

AI capex is booming. AI returns are not. Global AI spending is heading toward $2.5T in 2026, while 95% of enterprise GenAI pilots still deliver no measurable ROI. That is not necessarily a bubble. It is a timing, concentration and capital-allocation problem. The critical question for boards is no longer “How much are we spending on AI?” It is: “Are we investing in the model - or in the productivity harness that turns AI into economic value?” That distinction could determine who captures the returns from this cycle - and who merely finances it. Read AIMG’s The Economics of AI for the full analysis. https://lnkd.in/ejiZWppT

ISO 42001 could become the new SOC 2 for AI. The bigger shift is economic, not regulatory. If 60-70% of AI value sits outside the model - in data, retrieval, workflows, evaluation and governance - then governance isn't overhead. It is part of the AI asset. For regulated industries, the question is increasingly simple: Can you prove your AI is governed well enough to buy? Our latest AIMG Insight explores why ISO/IEC 42001 may become the procurement gate that separates AI vendors who get shortlisted from those who don't. Governance is moving from compliance requirement to competitive advantage. https://lnkd.in/ePN8NSwt

AI may be creating a talent crisis companies won’t see coming. The biggest workforce impact of AI may not be mass unemployment. It may be the erosion of the pipeline that produces future senior talent. AI is absorbing the routine work that traditionally trained junior professionals - coding, testing, analysis, document review and customer support. The economics are obvious: Hire fewer juniors. Give AI-enabled seniors more leverage. Increase productivity. But if everyone automates the bottom of the pyramid, where do tomorrow’s senior experts come from? AIMG’s research suggests this shift is already emerging: demand is moving towards experienced AI talent while junior roles contract. The result could be a 5-10 year capability gap that won’t appear in today’s productivity metrics. For CEOs and boards, the question is no longer simply: “How much work can AI automate?” - it is:“Are we automating the talent pipeline itself?” The winners won’t just automate fastest. They’ll be the companies that learn how to build talent differently in an AI-native economy. #AI #FutureOfWork #Leadership #Talent #AIMG https://lnkd.in/eGi4kSJs

AI is getting cheaper - but demand is exploding. That is Jevons’ Paradox applied to AI: every major efficiency gain in tokens and inference is creating more use, not less. The bottleneck is shifting from chips → power → grid capacity. For investors and AI leaders, the implication is clear: the opportunity is increasingly in the physical infrastructure behind AI - power, cooling, transmission, interconnection and long-term energy contracts. Read full AIMG Insight Post: https://lnkd.in/eCP4Thty #AI #JevonsParadox #AIEconomics #DataCenters #Energy

Most AI adoption stories skip the middle. The pitch deck shows a straight line up. The reality is a J-curve: costs land immediately, returns arrive later. Retraining, process redesign, integration debt, the productivity hit while people learn new tools - all of that shows up in this quarter's numbers. The compounding gains don't. Which means the most dangerous moment in an AI programme isn't the start. It's the trough - the point where the spend is visible, the payoff isn't, and the pressure to declare it a failure peaks. The organisations that break through are usually the ones that budgeted for the dip in the first place. Our new AIMG Special Report, The Economics of AI, maps the shape of that curve - what drives the depth of the trough, what shortens the path to break-even, and how to tell a slow return from a bad investment. See https://lnkd.in/eGaUY6Rt

In enterprise AI, the real advantage is no longer just model performance. It is category authority – the ability to shape how buyers understand the opportunity, evaluate risk, and make confident decisions. Our latest AIMG Insight post by Judith Peterson, AIMG Advisory Board Member, “How Enterprise AI Companies Build Category Authority”, explains why revenue multiples for AI acquisitions are outpacing public comps and what that means for go-to-market motion and product strategy. Category leaders don’t just go to market with features and demos; they design their GTM, product roadmap, and positioning around how multi‑stakeholder buying committees actually make decisions. For enterprise vendors, that means a pivot: from campaigning for attention to engineering decision confidence across CEO, CTO, Risk, Finance, and Governance stakeholders. The companies that win will be the ones that turn buyer signals, alliances, and usage patterns into evidence – and become the trusted guide for a critical new class of enterprise decisions. Full article available at https://lnkd.in/eHW7VVvR