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 AVTICA defines as the shift from Solo AI to We AI.
<
5
%
of companies have moved custom AI solutions into production
Gartner / IDC, 2025
42
%
of companies abandoned most AI initiatives in 2025 — up from 17% the prior year
S&P Global, 2025
80
%+
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.
Three symptoms of Solo AI
If any of this sounds familiar, you're already affected.
Intelligence drift
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 exposure
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.
Pilot purgatory
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.
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.
AVTICA 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.
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.
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.
55
%
faster task completion with unified AI tools
Fullview, 2025
70
%
cost reduction achievable by automating workflows with unified agentic AI
Deloitte AI Institute, 2025
66
%
of enterprises have already achieved significant operational productivity gains
IBM IBV, 2025
And the strategic advantages compound beyond the line-item savings.
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.