If it feels like everyone else has figured out AI except your organization, you’re not alone. AI in practice is not as pervasive as media reports may suggest. To truly implement AI, you need trust, which can only begin after enterprisewide alignment.
In conversations with executives across industries, our people hear a similar concern. Leaders understand the potential of AI, see competitors investing in it, and know there is pressure to move. Even so, many organizations are still struggling to make real progress. More often, these organizations are still trying to answer a practical question: where do we start?
A recent webinar by Forvis Mazars revealed where full AI integration may be stalling. When participants were asked about the biggest barriers preventing AI adoption, 25% of respondents chose trust. The results also showed that 50% of respondents did not know how much their organizations planned to invest in AI. Taken together, the responses pointed to a broader issue: organizations are not necessarily resisting AI, as the majority indicate wanting to move forward, but they lack the confidence to take the next step.
Which facet of an agentic enterprise will be the hardest for your organization to get right today or in the near future?
How much of your annual revenue does your organization plan to invest in AI development and implementation in the next year?
What Are The Sources of AI Trust Gaps?
Data Quality
The most important factor may have little to do with the AI tool itself. It often comes down to data. Organizations may find that their biggest AI challenge is not choosing a model or platform. It is preparing the information on which those systems will rely. With only 7% of organizations saying their data is completely ready for AI adoption,1 the data problem is widespread.
Many businesses collect large amounts of data. Fewer have connected, governed, and aligned that data across the organization. Information may sit in separate systems, different teams may use different definitions, and important knowledge may be spread across departments, all of which make it difficult to produce the consistency and quality needed for reliable AI outcomes. The technology is only as strong as the data behind it.
Decision Fatigue
Ironically, the technologies designed to help leaders process information may also contribute to overload. Researchers found a strong relationship between long-term AI interaction and information overload, highlighting why trust requires more than deploying tools. It requires helping people navigate increasing complexity.2
For leadership teams trying to make technology decisions, AI can add to background noise instead of acting as a strategic idea generator. There are many ways AI could help automate processes, improve workflows, enhance customer experiences, or support better use of data. Amid the increased productivity AI presents, human judgment and cognitive function are still required to both evaluate and execute decisions with confidence.
Perception Gap
Many executives worry they’re falling behind in the AI race. The data suggests differently as most organizations are still early in their AI journey, highlighting a significant gap between perception and measurable maturity.
In reality, many organizations are still working through the same foundational questions. They are evaluating use cases, securing funding, strengthening data quality, establishing governance, training deficiencies, upskilling talent, filling knowledge gaps, and figuring out how to turn AI usage into true AI integration. Without a clear view of their current capabilities, leaders may move too quickly, invest in the wrong areas, or attempt to scale before the necessary foundation is in place. A realistic assessment of AI readiness can help organizations focus their efforts, address critical gaps, and move forward with greater confidence.
Investment Uncertainty
AI hesitation is not always driven by fear of the technology itself. For many organizations, it stems from uncertainty about where to invest and how to measure success. While 92% of companies plan to increase AI spending, only 1% consider themselves mature in deployment with security and risk concerns being the greatest barrier.3 Many leaders are left wondering whether they are investing in the right use cases, governance structures, and operating models to achieve meaningful returns.
Leadership Alignment
Trust in AI starts with alignment at the top. Leaders need a shared understanding of the organization’s AI priorities, expected value, decision-making authority, and risk tolerance. Without that foundation, fragmented ownership and competing priorities can slow progress. As one recent study concluded, “AI doesn’t create alignment. It rewards it.”4 Organizations that align strategy, governance, and culture are significantly more likely to realize measurable business value from their AI investments. In this environment, trust depends both on choosing the right investments and defining how success is measured. Organizations that establish clear business outcomes, performance metrics, and return on investment (ROI) expectations are better positioned to validate results, build confidence, and expand AI adoption over time.
The Bottom Line
The conversation about AI readiness needs to shift. Organizations do not need more pressure, fear-driven headlines, or lists of emerging tools. They need a practical path forward.
Leaders can start with three actions:
- Align executives around AI priorities: Establish a shared understanding of where AI can create value, how decisions will be made, and what level of risk the organization is prepared to accept.
- Select one high-value, low-risk use case: Focus on a defined business need that can demonstrate value, produce measurable results, and build confidence before expanding.
- Establish governance and data ownership: Clarify who is accountable for AI oversight, data quality, responsible use, and ongoing performance.
These actions can help organizations move from ambition to execution at a pace that fits their business. Trust makes AI adoption possible, but alignment gives trust a place to start.
How Forvis Mazars Can Help
Trustworthy AI isn’t defined by how many AI tools an organization deploys. It’s defined by how confidently leaders can answer a few fundamental questions:
- Do we know why we’re using AI?
- Do we trust the data behind it?
- Do we understand the risks?
- Do employees know how to use it responsibly?
- Is someone accountable for the outcomes?
Organizations that can answer those questions with confidence are often further ahead than they realize.
Trustworthy AI is built on a foundation of clear strategy, strong governance, quality data, responsible oversight, and workforce adoption. Without those elements, even the most advanced AI tools can create uncertainty. With them, organizations can move faster, make better decisions, and scale AI with confidence.
What Trustworthy AI Looks Like
1. Data Quality & Readiness
- Trusted, accurate, and complete data
- Accessible and integrated data
- Data governance and ownership
- Quality standards and monitoring
2. Use Case Identification & Prioritization
- Business value
- ROI assessment
- Feasibility and risk
- Process integration
3. Workforce Enablement & Change Management
- Adoption and engagement
- Training and upskilling
- Change readiness and support
4. AI Governance, Risk, & Compliance
- Policies and guidelines
- Oversight and monitoring
- Responsible use
- Vendor and third-party risk
5. Strategy & Leadership
- Vision
- Executive sponsorship
- Accountability
Download Our Complimentary Assessment
Take the AI Readiness Self-Assessment to understand where your organization stands and what actions can help strengthen trust, governance, and adoption.