A recent Gartner report signals the end of the agentic AI “gold rush.” As supply outstrips demand, a wave of consolidation is beginning. For smart enterprises, this isn’t a crisis—it’s the opportunity they’ve been waiting for.
This imbalance is triggering what Gartner calls an inevitable “market correction.” For many startups and investors, this signals a painful reckoning. But for enterprise customers—the CTOs, data leaders, and strategists tasked with delivering real business value—this shakeout is not a crisis. It is a necessary and beneficial rationalisation.
(Read why this consolidation is good news for enterprises in part one of this story.)
As the market consolidates, the hype is giving way to fundamental economics, clearing the fog and revealing a smaller, stronger, and more reliable set of partners. For enterprises ready to move from experimentation to execution, the real work starts now.
What Enterprises Must Do Now
For enterprise technology leaders, the consolidation represents both risk and opportunity. Those who navigate the correction strategically can gain competitive advantage whilst avoiding costly tech provider failures.
Choose Vertical Over Horizontal
The era of horizontal agent platforms promising universal application is ending. Consolidation will favour domain-specific, vertically integrated solutions with pre-built industry knowledge, compliance frameworks, and clear ROI metrics.
Enterprises should prioritise providers with demonstrated expertise in their specific industry—financial services compliance agents, healthcare clinical documentation systems, legal contract analysis tools—rather than generic platforms requiring extensive customisation.
Assess Provider Viability
With dozens of providers likely to disappear through acquisition or failure, provider selection must include financial stability assessment. Evaluate funding runway, acquisition likelihood, customer retention rates, and whether the provider has a clear path to profitability or strategic value to potential acquirers.
Build Infrastructure First
Successful agentic AI implementations require foundational capabilities most enterprises lack. Before deploying agents, invest in data quality improvement, API standardisation across legacy systems, governance frameworks for autonomous decision-making, and explainability standards for regulatory compliance.
Start Narrow, Prove Value, Then Scale
Resist enterprise-wide rollouts. Instead, identify high-value, well-defined use cases with clear metrics, controlled data environments, and tolerance for supervised autonomy. Prove ROI in production before expanding scope.
The gradient from human-in-the-loop (agent suggests, human approves) to human-on-the-loop (agent acts, human monitors) to fully autonomous operation should be measured in quarters or years, not weeks.
ALSO READ: Agentic AI Supply Exceeds Demands, Says Gartner
Demand Clear ROI Frameworks
Given that most deployments break even only in year two with 29% ROI after two years, business case discipline is essential. Require providers to provide detailed TCO models including ongoing training, monitoring, and integration costs—not just licence fees.
Leverage the Consolidation
The consolidation creates negotiating leverage. Providers facing acquisition pressure or funding challenges may offer better terms, whilst enterprises can increasingly demand outcome-based pricing rather than seat licences.
Additionally, as vertical leaders emerge in each industry, enterprises can access more reliable, better-integrated solutions than were available during the fragmented proliferation phase.
From Hype to Value
The agentic AI market correction represents the technology crossing from hype to maturity. Whilst painful for providers and investors who bet on undifferentiated platforms, the consolidation benefits enterprise technology leaders who need reliable, production-ready solutions rather than aspirational capabilities.
By 2027, the market will look dramatically different: 10-15 major players will dominate, vertical specialists will lead each industry, integration with existing enterprise platforms will be table stakes, and trust and governance will differentiate winners from losers.
The opportunity remains substantial. Enterprise spending is projected to surge from $1.5 billion in 2025 to $41.8 billion by 2030, whilst 88% of enterprises plan to increase AI budgets. The question isn’t whether agentic AI will transform enterprise operations—it’s which providers will survive to deliver that transformation, and which enterprises will position themselves to capture the value.
ALSO READ: The GenAI Paradox: Why Widespread Adoption Hasn’t Led to Widespread Value
Tech Provider Evaluation Checklist
Questions Enterprise Tech Leaders Must Ask Before Committing
Financial Viability
- What is the provider’s funding runway? (Minimum 18-24 months recommended)
- Are they profitable or have a clear path to profitability?
- What’s their customer retention rate?
- Are they likely acquisition targets? (Consider continuity risk)
Technical Capability
- Can they demonstrate true autonomous planning and reasoning, not just API orchestration?
- What’s their approach to handling hallucinations and edge cases?
- How do they ensure explainability and auditability of agent decisions?
- What’s the token multiplication factor versus standard generative AI? (Understand cost implications)
Domain Expertise
- Do they have pre-built workflows for your specific industry?
- Can they demonstrate regulatory compliance capabilities (GDPR, HIPAA, SOX, etc.)?
- Do they understand your industry’s data models and terminology?
- What’s their customer roster in your sector?
Integration & Infrastructure
- What are the data quality requirements? (Be specific about structured vs. unstructured data)
- How do they integrate with your existing tech stack?
- What APIs and data pipelines are required?
- What’s the realistic implementation timeline from POC to production?
Business Model & ROI
- Can they provide detailed TCO models including all ongoing costs?
- What are their success metrics and how do they measure them?
- Do they offer outcome-based pricing or only seat licences?
- What’s the expected breakeven timeline and ROI at 12, 24, and 36 months?
Governance & Risk
- What guardrails and monitoring capabilities do they provide?
- How do they handle agent errors and rollback scenarios?
- What’s their approach to data privacy and security?
- Can they demonstrate compliance with relevant regulations?
Red Flags to Watch For
- Vague promises of “full autonomy” without specifics on guardrails
- Inability to demonstrate production deployments (only POCs)
- Lack of industry-specific case studies
- Resistance to providing detailed cost breakdowns
- No clear explainability framework
- Recent executive departures or organisational instability
ALSO READ: What Is Zombie AI, and Why Should Your C-Suite Care?
