Trace3 warns disconnected AI projects are raising enterprise costs
Trace3 says enterprises are incurring hidden costs as AI tools spread faster than departments can coordinate purchasing, prepare data and measure returns. The technology solutions provider points to duplicated investments, repeated data preparation and unmonitored model usage as barriers to scaling AI across organizations.
- Only 22% of Gartner surveyed organizations had successfully scaled AI across multiple business units.
- 85% of surveyed functional leaders planned to increase AI spending in 2026.
- Separate departments may duplicate cloud platforms and data preparation work.
- Trace3 recommends measuring baseline costs, usage and post implementation results.
- Data should be available, centralized, governed and secure for AI use.
AI adoption is outpacing organizational readiness
Only 22% of organizations surveyed by Gartner had successfully scaled artificial intelligence across multiple business units or adopted an AI first approach, according to figures cited by Trace3. At the same time, 85% of surveyed functional leaders planned to increase AI spending in 2026.
Trace3 said the gap between spending plans and operational readiness can become costly when departments adopt tools independently. Kevin Rogers, Practice Director of Data and Analytics at Trace3, said companies should establish an operating model and focus on readiness before giving employees access to tools.
"You don't want tool adoption to happen faster than organizational readiness," Rogers said.
Separate projects can duplicate software and data work
Trace3 cited Deloitte's 2026 State of AI in the Enterprise report, which found that workforce access to sanctioned tools grew from fewer than 40% to around 60% in one year. However, only 30% of surveyed organizations were redesigning key processes around AI.
According to Rogers, departments acting independently may buy separate instances of similar cloud data platforms for different purposes. He described one example involving separate platforms for security vulnerability analysis and enterprise data and analytics.
"Two separate purchases, two separate environments, and no idea that they could have had economies of scale," Rogers said.
The duplication can also affect data preparation. Separate teams may prepare the same enterprise data for their own AI use cases, repeating work across platforms and data pipelines. Trace3 recommends creating a shared inventory of tools, planned pilots and data resources to identify overlap. Rogers also said pockets of AI expertise should be connected through a center of excellence.
Measuring value requires data and cost controls
Trace3 recommends evaluating AI projects according to business value and technical feasibility. Projects with high value and manageable technical requirements can provide early evidence before organizations commit additional resources.
Rogers said financial planning should include employee time spent on manual tasks, model usage and ongoing operating expenses. Companies should compare how much a process costs before AI with its cost after implementation. Usage based models can make that comparison more difficult, particularly when token consumption is not monitored.
Data readiness is another challenge. Information may be scattered across platforms, structured inconsistently or governed by permissions that do not match how AI applications will use it. Trace3 said preparing data can require specialized expertise while internal teams continue maintaining existing systems.
For users and enterprise buyers, the message is that wider access to AI tools does not by itself establish enterprise value. Trace3 says organizations need shared visibility into investments, governed and secure data, usage guardrails and measurable results before expanding initiatives.
