The bill you planned for
- AI models
- Token consumption
- Infrastructure
- AI applications
- AI agents
- Implementation
BILL 1 · PAID
You paid for the AI. Now you’re paying to trust it.
The hidden second bill of enterprise AI — and how trustworthy intelligence brings it down
The invoice
Most organizations have paid the first bill
Enterprise AI has moved beyond isolated pilots and into the processes businesses depend on.
BILL 1 · PAID
AI models✓
Token consumption✓
Infrastructure✓
AI applications✓
AI agents✓
Implementation✓
Where organizations are with AI
80%scaled or embedded in core processes
4.84AI application types in use or piloted, on average
Then came the bill that wasn’t on the invoice
It took the form of validation, reconciliation, data preparation, context gathering, explainability checks, rework — paid in time, labor, and human review.
The bill hidden in the work
- Data gathering and preparation
- Output validation
- Record reconciliation
- Context gathering
- Explainability checks
- Human correction and rework
Paid in time, labor, and human review
The AI Trust Tax
The hidden cost of validating, reconciling, explaining, and reworking AI outputs before they can be used with confidence.
Trustworthy intelligence is how you bring it down: governed data, connected business context, and explainability behind every output.
Four in five lose at least six hours a week to the tax
Time spent preparing, reconciling, and validating data before AI and analytics can deliver usable answers.
Hours per week spent validating, reconciling, and cleaning data
80%spend at least six hours a week
28%say AI outputs require too much manual review
27%say duplicate, incomplete, or inconsistent data created errors
And that’s a more serious problem than just oversight. This is skilled time that’s being spent on preparation and rework that could be avoided.
The paradox
Confident on paper, checking by hand
Leaders report confidence. But their teams tell a more complicated story.
What leaders say
What happens in practice
Confidence isn’t the same as operational trust. AI becomes trustworthy when you can trace, explain, and stand behind the data informing its outputs.
The tax doesn’t stay behind the scenes
Weak data foundations slow initiatives, raise operating costs, and make AI outcomes harder to explain or defend.
Initiative delayed in the past 12 months
What unreliable data input caused
66%report at least one delayed initiative
14%cite risk or compliance exposure as the top consequence
14%cite lower confidence among business users
12%cite higher operating costs
AI autonomy is hitting a trust ceiling
Leaders want strong accuracy, data quality, explainability, and traceability before they grant AI more freedom.
Where organizations permit autonomous action
What leaders need before granting more
Agents need better evidence before they get more freedom.
The gap
Stronger confidence requires stronger data foundations
Organizations with greater confidence in AI also report stronger traceability, availability, governance, and enterprise data foundations.
Highly confidentModerately confident
The difference? Not just confidence in the model, but confidence in the data and context behind the output.
Better data, better AI outcomes
High-confidence organizations are more likely to report production progress, measurable value, ROI, and transformational improvements.
Highly confidentModerately confident
Trust does more than just reduce risk. It helps turn AI activity into outcomes the business can see.
Even the best-performing AI still gets checked
More mature AI programs report stronger value, ROI, and explainability. They also validate their outputs more often.
High maturity (n=317)Low maturity (n=83)
The twist
Often or always validate or rework AI outputs
This isn’t necessarily a contradiction. Mature organizations may use AI more broadly, in more consequential workflows, with more disciplined controls. Instead of zero review, the goal is to reduce preventable rework and improve informed oversight.
The answer
Not just more intelligence. Trustworthy intelligence.
Trustworthy intelligence is what AI success actually looks like: governed data, connected business context, and explainability behind every output you act on.
1
Build the foundation for trust
Unify and govern critical business data across domains, systems, and workflows.
2
Give AI the context behind the record
Connect products, customers, suppliers, locations, assets, and transactions so AI can understand business meaning and relationships.
3
Make trust measurable
Track traceability, explainability, validation time, preparation work, rework, and the outcomes AI delivers.
You already know what your AI costs to run. Now it’s time to find out what it costs to trust.
Methodology
How we gathered the insights
Research partnerTEAM LEWIS
Survey periodAug. 4–10, 2026
Respondents400 U.S. business leaders
Decision authority82% are primary decision-makers
Stibo Systems commissioned TEAM LEWIS to conduct an online survey of 400 U.S. full-time business leaders of manager level and above at organizations with 500 or more employees. Every respondent influences their organization’s enterprise data and AI strategy, and 82% serve as primary decision-makers. More than half (51%) work in IT, including data architecture, data engineering, governance, AI product, AIOps, and related functions. The rest work across finance, operations, business administration, product development, sales, marketing, and customer-facing functions. Organizations span manufacturing, financial services, retail and quick-service restaurants, consumer packaged goods (CPG), life sciences, distribution, insurance, and other industries. Data was collected Aug. 4–10, 2026.
How to read the findings
- Highly confident
- Organizations where business users are very or extremely confident in AI-generated outputs (72% of respondents). The comparison group is moderately confident organizations.
- High AI maturity
- Organizations that have scaled AI across multiple functions or business units, or embedded it deeply in core business processes (n=317).
- Low AI maturity
- Organizations that are not using AI, are experimenting in limited pilots, or have deployed AI in select functions or use cases (n=83). The smaller base makes these findings directional.
- Association, not causation
- Group comparisons show patterns. They do not establish that one factor causes another.
- Rounding and combined answers
- Percentages are rounded and may not sum to 100%. Where a figure combines answer options, such as 6+ hours a week or very and extremely confident, it adds the underlying response bands.
