The Battle for the Agentic Enterprise
AIMG® Expert Insight
Recent acquisitions by Nebius, Zendesk, and KPMG provide a revealing cross-section of how companies across the technology stack are repositioning themselves for the agentic AI era. AIMG analysis indicates that these transactions are not isolated events but early signals of a broader structural shift in enterprise technology markets.
Across sectors, the strategic objective is increasingly the same: control over the infrastructure, tools, and workflows that will power autonomous enterprise decision-making.
Three AI Transactions That Signal a Structural Market Shift
| Acquirer | Target | Sector | Estimated Deal Value | Strategic Objective |
|---|---|---|---|---|
| Nebius | Tavily | AI infrastructure / agentic search | $275M upfront (up to $400M incl. earn-outs) | Move up the AI value chain into the enablement layer |
| Zendesk | Forethought | Customer service AI agents | Undisclosed | Transform ticketing platform into an autonomous AI “Resolution Platform” |
| KPMG | PrivateBlok | AI agent development | Undisclosed | Embed AI agents into consulting workflows |
AIMG analysis suggests these transactions illustrate the emergence of three major AI M&A battlegrounds:
- AI infrastructure enablement
- Agentic enterprise applications
- AI-augmented professional services
Together they highlight a profound shift in how organisations are competing for advantage in the emerging AI economy.
AI Profit Pools Are Moving Up the Stack
The economic logic behind these acquisitions can be understood through the shifting distribution of profit pools across the AI technology stack.
AIMG AI Value Chain Profit Pools
| AI Layer | Typical Gross Margin | Strategic Role |
|---|---|---|
| Semiconductor hardware | 30–50% | Compute supply |
| Cloud infrastructure | 25–40% | Training and inference |
| AI platforms | 50–65% | Model orchestration and development |
| AI enablement layer | 65–75% | Agents, orchestration, retrieval |
| Enterprise AI applications | 70–85% | Autonomous workflows |
AIMG analysis indicates that the highest margins increasingly reside in the application and enablement layers, rather than in raw infrastructure.
For example, the AI-as-a-service value chain shows that while GPU infrastructure captures relatively modest margins, AI applications can generate roughly $8 of revenue per H100 compute hour with gross margins approaching 80%.
This migration of economic value explains why infrastructure providers, software companies, and consulting firms are all attempting to move higher in the AI stack through strategic acquisitions.
Transaction Analysis
Nebius Acquires Tavily: Moving from Compute to Cognition
In February 2026, Nebius announced the acquisition of Tavily, a developer platform specialising in agentic web search.
| Metric | Value |
|---|---|
| Upfront consideration | $275M |
| Maximum deal value | $400M |
| Target funding prior to acquisition | ~$25M |
| Developer ecosystem | >1M developers |
| SDK downloads | ~3M per month |
The acquisition integrates Tavily’s real-time web search capabilities into Nebius’s AI cloud platform.
This is strategically important because large language models without access to external information are prone to hallucination and outdated knowledge. By embedding agentic search directly within its platform, Nebius enables developers to build autonomous AI systems capable of retrieving and reasoning over live data.
AIMG analysis suggests the transaction represents a strategic shift for Nebius: moving beyond bare-metal infrastructure into the AI enablement layer, where significantly higher margins are available.
The deal also strengthens Nebius’s ambition to become a full-stack AI cloud provider, combining infrastructure, inference, and developer tooling within a single platform.
Zendesk Acquires Forethought: The Rise of the Resolution Platform
Zendesk’s acquisition of Forethought marks the company’s largest transaction in nearly two decades and reflects a fundamental shift in enterprise customer service technology.
| Metric | Value |
|---|---|
| Target funding | ~$115M |
| Enterprise customers | >100,000 |
| Customer interactions | ~8B daily |
| Expected AI-driven ARR | ~$200M |
Forethought develops autonomous AI agents capable of resolving customer service requests without human intervention.
Zendesk’s strategic objective is to transform its traditional ticketing system into an AI-native “Resolution Platform.”
From Ticketing to Autonomous Resolution
| Legacy Customer Service | AI Resolution Platform |
|---|---|
| Ticket routing | Autonomous problem solving |
| Human agents | AI agents |
| Seat-based pricing | Outcome-based pricing |
| Static workflows | Self-learning systems |
Zendesk expects AI agents to handle more customer interactions than humans by 2026, marking a major inflection point in enterprise service automation.
A particularly important innovation is Zendesk’s move toward outcome-based pricing, where customers pay for successful issue resolution rather than for the number of human agents using the software.
AIMG analysis suggests this shift could fundamentally reshape SaaS economics by linking revenue directly to measurable business outcomes.
KPMG Acquires PrivateBlok: Consulting Becomes Software
The third transaction reflects a transformation taking place within the global consulting industry.
In February 2026, KPMG acquired PrivateBlok, an AI development platform specialising in multi-agent systems.
| Metric | Value |
|---|---|
| Transaction type | Talent + technology acquisition |
| Core capability | Multi-agent AI systems |
| Key use cases | Finance, due diligence, change management |
| Strategic focus | Secure AI copilots for enterprise data |
PrivateBlok develops AI systems capable of combining public information with proprietary corporate data to create secure knowledge bases that power enterprise AI copilots.
For consulting firms operating in highly regulated industries, this architecture is essential. Enterprise clients require AI systems that can operate on sensitive corporate data without exposing information to external models.
The acquisition therefore expands KPMG’s ability to deploy AI-powered advisory platforms directly into client organisations.
The AIMG AI M&A Strategic Stack
Taken together, the three transactions illustrate how companies are acquiring capabilities across the AI stack.
| Layer | Strategic Objective | Example Transaction |
|---|---|---|
| Enterprise applications | Autonomous enterprise workflows | Zendesk – Forethought |
| AI enablement tools | Agent orchestration and retrieval | Nebius – Tavily |
| AI platforms | Model development and orchestration | Databricks ecosystem |
| AI infrastructure | GPU compute and data centres | Hyperscaler investments |
| AI talent and research | Specialist engineering teams | KPMG – PrivateBlok |
AIMG analysis suggests that most AI acquisitions in 2025–2026 represent “stack-climbing” strategies, where companies acquire capabilities higher in the value chain to control enterprise decision workflows.
AI M&A Valuation Benchmarks
The growing strategic importance of AI technologies is reflected in the valuation multiples observed across the sector.
Selected AI Transactions
| Transaction | Year | Estimated Deal Value |
|---|---|---|
| Nebius – Tavily | 2026 | $275M–$400M |
| Zendesk – Forethought | 2026 | Undisclosed |
| KPMG – PrivateBlok | 2026 | Undisclosed |
| Databricks – MosaicML | 2023 | $1.3B |
| IBM – Red Hat | 2019 | $34B |
Typical EV / Revenue Multiples
| Segment | Typical Multiple |
|---|---|
| Traditional enterprise software | 5–8× |
| High-growth SaaS | 8–12× |
| AI-native software platforms | 12–18× |
| Frontier AI infrastructure | 15–25× |
AIMG analysis indicates that companies capable of enabling autonomous enterprise workflows command the highest valuation premiums.
The Agentic AI Opportunity
The rapid acceleration of agentic AI markets is the fundamental catalyst behind this wave of acquisitions.
| Year | Estimated Market Size |
|---|---|
| 2025 | ~$7B |
| Early 2030s | $140B–$200B |
| CAGR | >40% |
As AI systems evolve from generative tools to autonomous agents capable of executing workflows, demand for enabling infrastructure and enterprise platforms is expected to grow rapidly.
Industry forecasts suggest that by the end of 2026 roughly 40% of enterprise applications will embed autonomous AI agents capable of handling end-to-end tasks.
AIMG Strategic Interpretation
Based on analysis and insights from the AIMG Expert Network and Advisory Board, three structural shifts are reshaping the enterprise AI landscape.
Infrastructure Providers Are Moving Up the Stack
Infrastructure providers are increasingly acquiring software capabilities that allow them to capture higher margins and strengthen platform lock-in.
Enterprise Software Is Becoming Autonomous
Enterprise applications are evolving from tools used by employees into systems capable of completing complex tasks independently.
Consulting Firms Are Becoming Software Companies
Professional services firms are increasingly deploying proprietary AI platforms rather than delivering services solely through human labour.
This transformation is giving rise to a new delivery model often described as “services-as-software.”
AIMG Outlook: The Next Phase of AI Consolidation
Drawing on insights from the AIMG Advisory Board and Expert Network, several trends are likely to shape the AI M&A cycle over the coming years.
Agentic AI arms race
Companies will increasingly compete to acquire technologies that enable autonomous enterprise workflows.
Platform consolidation
Enterprise customers are likely to consolidate AI spending around integrated platforms rather than fragmented tools.
Services-software convergence
Consulting firms will continue acquiring AI engineering companies to transform advisory services into scalable software platforms.
Private equity acceleration
Large private equity firms with significant capital reserves are likely to become major participants in AI-focused acquisitions.
Capability polarisation
The market will increasingly differentiate between companies with deployable AI capabilities and those with only experimental AI initiatives.
Strategic Conclusion
The acquisitions of Tavily, Forethought and PrivateBlok illustrate a deeper structural shift in how organisations are positioning themselves within the emerging enterprise AI value chain.
Rather than simply adding incremental AI capabilities, companies are increasingly acquiring technologies that allow them to control critical nodes in the AI supply chain – from compute infrastructure and model orchestration to agent frameworks and autonomous enterprise applications.
From an AIMG perspective, these transactions demonstrate three important dynamics.
First, AI profit pools are migrating up the value chain. While foundational infrastructure such as GPUs and cloud compute remains essential, the largest long-term economic value is likely to accrue to the layers that enable context, reasoning and autonomous workflow execution. This explains why infrastructure providers are seeking to expand into software enablement layers, and why enterprise platforms are racing to embed agentic capabilities.
Second, enterprise technology markets are reorganising around agentic architectures. Systems capable of executing multi-step tasks, interacting with external data sources and operating across enterprise workflows will increasingly become the central organising layer of corporate software.
Third, the emerging AI landscape is becoming increasingly stack-oriented rather than product-oriented. Competitive advantage will be determined less by individual models or tools and more by the ability to integrate capabilities across the AI supply chain: data, models, orchestration, agents and enterprise applications.
This is precisely the type of market structure that the AI LeaderMap framework was designed to analyse. Traditional vendor evaluation approaches that focus primarily on historical market share or revenue scale struggle to capture the competitive dynamics of AI markets, where innovation capacity and architectural positioning within the value chain are increasingly the primary drivers of long-term strategic advantage.
From an AI LeaderMap perspective, the key differentiator will not simply be which firms adopt AI, but which firms successfully position themselves in the most strategic layers of the AI stack.
Those organisations that control the enabling infrastructure for agentic enterprise systems – whether through platform integration, proprietary tooling or strategic acquisitions – are likely to shape the next generation of enterprise technology markets.
In this sense, the recent wave of AI acquisitions should not be viewed merely as opportunistic dealmaking. It represents the early stages of a broader reorganisation of the global enterprise technology supply chain around autonomous AI systems.
And that reorganisation is only just beginning.
Source: AIMG