The Real Problem
More tools aren't the answer. Better infrastructure is.
Most enterprises are not behind on AI.
They are drowning in it. AI tools multiply. Security gaps widen. Promising pilots never reach production. The problem is not a shortage of AI; it is the absence of unified infrastructure to govern it.
This report presents the strategic framework for transitioning from fragmented, individual AI deployments to a coordinated, enterprise-owned intelligence platform, which NMBLR defines as the shift from Solo AI to We AI.
of companies have moved custom AI solutions into production
Gartner / IDC, 2025
of companies abandoned most AI initiatives in 2025 — up from 17% the prior year
S&P Global, 2025
of organizations report no meaningful EBIT impact from AI despite wide adoption
IBM IBV, 2025
Ask any CTO whether their organization uses AI, and the answer is almost certainly yes. Ask whether that AI is governed, auditable, and consistent across teams, and the honest answer is almost certainly no.
Enterprises today are not suffering from an AI deficit. They are suffering from an AI governance crisis.
Adoption alone doesn't deliver value. Governed, unified infrastructure does.
There's a term for what most enterprises are running today: Solo AI. Individually adopted tools, scattered across teams, operating without shared context or common oversight. It looks like momentum. It isn't.
Agentic AI is redefining platforms1. It is transforming how work gets done, how value is created and how humans and machines collaborate.
Strategy alignment unlocks value. Companies that align their AI, platforms and business strategies achieve on average 2.2x revenue growth and a 37% EBITDA lift.
Winning in an AI-first world requires a new platform strategy. We outline five priorities to help companies seize the opportunity.
How do you expect your platform strategy will need to evolve in reponse to agentic AI
Three symptoms of Solo AI
If any of this sounds familiar, you're already affected.
When teams use disconnected AI tools, proprietary knowledge leaks into public models and institutional insights get trapped in departmental silos. 68% of organizations cite data silos as their top data management concern, up 7% year-over-year. Only 12% report having data of sufficient quality to support effective AI.
Shadow AI proliferates when employees fill workflow gaps with personal tools. Only 37% of organizations have policies to manage or detect shadow AI. Shadow AI incidents now account for 20% of all breaches and carry a cost premium of $4.63M versus $3.96M for standard incidents. The average enterprise unknowingly hosts 1,200 unofficial applications.
Most enterprise AI projects never graduate from proof-of-concept to production. 42% of companies abandoned most AI initiatives in 2025 up from just 17% in 2024. The average organization scrapped 46% of AI proof-of-concepts. AI projects fail at twice the rate of non-AI technology projects.
Agentic AI is redefining platforms1. It is transforming how work gets done, how value is created and how humans and machines collaborate.
Strategy alignment unlocks value. Companies that align their AI, platforms and business strategies achieve on average 2.2x revenue growth and a 37% EBITDA lift.
Winning in an AI-first world requires a new platform strategy. We outline five priorities to help companies seize the opportunity.
How do you expect your platform strategy will need to evolve in reponse to agentic AI
A Different Way to Think About It
The Paradigm Shift: From Solo AI to We AI
The antidote to Solo AI is not a single new tool. It is a fundamental architectural shift in how enterprises deploy, govern, and scale AI capability.
NMBLR defines this shift as the move from Solo AI, characterized by scattered, individually-adopted tools, to We AI: unified AI infrastructure that the enterprise owns, governs, and continuously improves.
The urgency is not hypothetical. Worker access to AI rose 50% in 2025, yet only one in five companies has a mature governance model for autonomous AI agents. More striking: over 80% of organizations report no meaningful impact on enterprise-wide EBIT from AI, despite widespread adoption. Adoption alone does not deliver value. Governed, unified infrastructure does.
Agentic AI is redefining platforms1. It is transforming how work gets done, how value is created and how humans and machines collaborate.
Strategy alignment unlocks value. Companies that align their AI, platforms and business strategies achieve on average 2.2x revenue growth and a 37% EBITDA lift.
Winning in an AI-first world requires a new platform strategy. We outline five priorities to help companies seize the opportunity.
How do you expect your platform strategy will need to evolve in reponse to agentic AI
Solo AI
We AI
Infrastructure
Disconnected tools
✅ Unified platform you own
Intelligence
Generic, siloed models
✅ Shared enterprise truth
Governance
Shadow AI, unauditable
✅ Fully governed, secure by design
Risk
Data leaks, drift
✅ Auditable, zero-leak
Time to Value
Pilot purgatory
✅ Weeks to production
We AI means treating AI infrastructure with the same rigor as cloud infrastructure, data architecture, or security policy: governed by design, not as an afterthought.
Integration is foundational, not optional. 95% of IT leaders report that integration hurdles impede AI adoption, with only 28% of enterprise applications actually connected despite organizations averaging 897 apps.
Powering Reinvention with Wind & Intelligence
Building a digital core to orchestrate the next generation of energy.
Wind energy is no longer just a resource; it is the cornerstone of the multi-generational decarbonization era. To meet rising global expectations, we are moving beyond simple manufacturing to build a fit-for-purpose foundation for the wind industry. By embedding Agentic AI into our windmill technology, we are transforming static infrastructure into living systems of intelligence—capable of reacting dynamically to atmospheric shifts and orchestrating energy output in real-time. This alignment of AI, platform, and business strategy is what unlocks 360° value, reducing reliance on fossil fuels while driving 2.2x higher operational growth.
The Business Case
Measurable ROI
Infrastructure decisions in the enterprise are ultimately investment decisions. The We AI model delivers measurable returns that compound over time, both in efficiency gains and in the strategic value of owning an enterprise intelligence layer that improves continuously.
$3.70
returned for every $1 invested
in enterprise AI.
Top-performing organizations achieve $10.30 per dollar. The gap between them and everyone else isn't the AI. It's the foundation it runs on.
faster task completion with unified AI tools
Fullview, 2025
cost reduction achievable by automating workflows with unified agentic AI
Deloitte AI Institute, 2025
of enterprises have already achieved significant operational productivity gains
IBM IBV, 2025
And the strategic advantages compound beyond the line-item savings.
Agentic AI is redefining platforms1. It is transforming how work gets done, how value is created and how humans and machines collaborate.
Strategy alignment unlocks value. Companies that align their AI, platforms and business strategies achieve on average 2.2x revenue growth and a 37% EBITDA lift.
Winning in an AI-first world requires a new platform strategy. We outline five priorities to help companies seize the opportunity.
How do you expect your platform strategy will need to evolve in reponse to agentic AI
When teams use disconnected AI tools, proprietary knowledge escapes into public models while institutional insights get trapped in silos. 68% of organizations cite data fragmentation as their top data management concern — and only 12% report having data of sufficient quality to support effective AI.
Employees fill workflow gaps with personal tools, and most organizations have no idea it's happening. Shadow AI incidents now account for 20% of all data breaches — and carry an average cost premium of $4.63M compared to standard incidents. The average enterprise unknowingly hosts 1,200 unofficial applications.
The ideas are good. The pilots are promising. They just never make it to production. The average organization scrapped 46% of its AI proof-of-concepts in 2025. AI projects fail at twice the rate of non-AI technology projects. That number isn't improving on its own.
01
Intelligence that accumulates
Every interaction, workflow, and decision stays inside the enterprise, building a proprietary data asset over time, not feeding someone else's model.
02
Compliance built in, not retrofitted
Only 1 in 5 organizations has achieved advanced AI governance maturity. Building it into the foundation from day one is far less expensive than adding it later.
03
Time-to-production collapses
What took years in the Solo AI model takes weeks when shared infrastructure already exists. Less than 5% of AI pilots reach production today. Unified infrastructure inverts that ratio.
Agentic AI: The new orchestration layer
Agentic AI is becoming the interface across platforms, spanning systems, reacting dynamically and orchestrating work in real time. It can rebalance supply chains, generate personalized offers and even close financial processes.
Example: Lenovo used Adobe Experience Platform and Microsoft Copilot to orchestrate AI across marketing, customer service and internal workflows. The effort delivered $11 million in efficiency savings and a 12.5% boost in click-through rates—speeding execution and enabling new forms of engagement at scale.
When teams use disconnected AI tools, proprietary knowledge escapes into public models while institutional insights get trapped in silos. 68% of organizations cite data fragmentation as their top data management concern — and only 12% report having data of sufficient quality to support effective AI.
Employees fill workflow gaps with personal tools, and most organizations have no idea it's happening. Shadow AI incidents now account for 20% of all data breaches — and carry an average cost premium of $4.63M compared to standard incidents. The average enterprise unknowingly hosts 1,200 unofficial applications.
The ideas are good. The pilots are promising. They just never make it to production. The average organization scrapped 46% of its AI proof-of-concepts in 2025. AI projects fail at twice the rate of non-AI technology projects. That number isn't improving on its own.
