74% of enterprises run AI in production, but half can't prove it pays off
Who owns what your AI has learned? This MarketScale article examines the distance between fast deployment and slower governance, covering data ownership, audit rights, and exit portability in AI platform agreements. Read the article to prepare for your next vendor review.
Why are so many enterprises struggling to prove AI ROI?
Today, about 74% of enterprises have AI running in production, yet roughly half of them cannot demonstrate clear ROI. The core issue isn’t deployment—it’s measurement and governance.
Common reasons enterprises struggle to prove AI ROI include:
- No clear success metrics: AI projects go live without being tied to specific business KPIs (e.g., cost per ticket, churn rate, cycle time, upsell rate).
- Weak integration with core systems: Models run in isolation instead of being embedded into workflows where impact can be tracked.
- Limited accountability: Ownership for AI performance and value is often unclear across IT, data, and business teams.
To close this “production-to-proof” gap, teams are starting to:
- Attach every AI deployment to a named metric before the next budget cycle (for example, “reduce support handle time by 15%” or “increase lead conversion by 5%”).
- Build governance frameworks that define who owns model performance, data quality, and business outcomes.
- Integrate AI into core business systems (ERP, CRM, ticketing, operations platforms) so impact can be measured in the tools finance and operations already trust.
In short, getting AI into production was the easy part. The next phase is about reimagining AI as a measurable business capability, not just a technical experiment.
What risks come with relying on external AI vendors?
Many enterprises are discovering that they’ve effectively “rented their brains” from AI vendors. Critical knowledge—business logic, customer insights, decision patterns—often ends up locked inside models and platforms they don’t control.
Key risks of heavy AI vendor dependency include:
- Loss of institutional knowledge: When workflows and decisions live inside a vendor’s model, it’s hard to take that learning with you if you switch providers.
- Data ownership ambiguity: It’s not always clear who owns training data, derived insights, or fine-tuned models.
- Limited portability: Migrating away can mean rebuilding models and processes from scratch.
To manage these risks, enterprises are starting to treat AI platforms like other critical infrastructure (ERP, cloud) and are tightening contracts accordingly. Practical steps include:
- Auditing AI vendor contracts for explicit terms on data ownership, model update rights, and exit options.
- Negotiating data portability: Ensure you can export your data, logs, and—where possible—model artifacts in usable formats.
- Securing audit and transparency rights so you understand how your data is used and how models are updated over time.
The goal is to reshape the relationship with AI vendors so you benefit from their capabilities without giving up long-term control of your own knowledge and data.
How are infrastructure and industrial players reshaping enterprise AI strategy?
AI is no longer just a software decision. It’s increasingly embedded in industrial platforms and shaped by where physical infrastructure gets built.
On the industrial side, companies like Schneider Electric and Siemens are making multi-billion-dollar acquisitions to build AI-native capabilities directly into their platforms. For buyers, this means:
- AI will often arrive bundled with automation, building management, or energy systems you already purchase.
- Lock-in risk increases: The AI layer may not be a separable line item later, so today’s OEM contracts can define tomorrow’s AI options.
- Procurement teams need to ask how AI is priced, licensed, and governed inside these platforms before signing long-term deals.
On the infrastructure side, data-center siting is shifting. Due to local resistance in some U.S. markets, hyperscalers are exploring remote industrial land, such as the Permian Basin in West Texas, where there is:
- Lower population density
- Existing power from oil and gas infrastructure
- Lower land costs
For enterprise IT and facilities teams, this can affect:
- Latency profiles for cloud regions serving your workloads.
- Power reliability and redundancy commitments.
- Provisioning timelines for new capacity.
As a result, more organizations are:
- Including hyperscaler siting and regional capacity plans in infrastructure roadmap reviews.
- Reevaluating industrial automation contracts with an eye on how embedded AI will shape future flexibility and cost.
Taken together, these trends are pushing enterprises to rethink AI as part of their long-term infrastructure and OEM strategy, not just a set of isolated software projects.
74% of enterprises run AI in production, but half can't prove it pays off
published by MALA Technology Advisors
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