Executive summary
Technology companies have embedded AI capabilities across functions, often with solutions developed by in-house teams. But these solutions can have disconnects that ultimately weaken controls, fragment data, strain workforce alignment, increase vendor risk and obscure returns. To sustain value, tech firms need proactive AI coherence built on integrated governance, standardized use cases and enterprise value. Cross-functional accountability, rationalized tools and continuous monitoring can help firms connect their innovations to performance. This enables scalable growth, stronger risk management and clearer, enterprise-wide decision-making.
When solutions are the problem
Technology companies have applied AI everywhere. AI capabilities have become essential tools in product development, customer operations, finance, security and risk functions.
This expansive AI integration has fostered significant potential in particular functions, along with significant complexity.
“When your teams are releasing feral AI solutions and processes across the enterprise, initial results might be impressive — but they can ultimately become contradictory and incoherent,” said Grant Thornton Technology Industry Head Andrea Schulz.
Disconnected AI solutions often foster problems with:
- Controls: Lacking governance and policies, uncontrolled model usage, unsecured data and missing risk management
- Data: Inconsistent data structures, data access gaps, incomplete analysis, poorly informed AI models and misleading conclusions
- Workforce: Fragmented AI adoption, skill development gaps, disconnected workflows, duplicated efforts and inconsistent perspectives
- Vendors: AI vendor proliferation, excess costs, conflicting standards, security complications and poor integration
- Goals: Inconsistent solution goals, incompatible metrics and an inability to capture a clear return on investment
Together, these problems ultimately limit a company’s short-term agility, along with its ability to plan, implement, measure and achieve long-term AI value.
In fact, technology industry respondents in Grant Thornton’s 2026 AI Impact Survey indicated that regulatory and compliance uncertainty is the top barrier to scaling AI solutions. While governmental regulations are not yet refined, the immediate concern is the lack of internal governance to help tech companies determine the right use cases, data standards, accountability and other enterprise decisions. Without that guidance, nonstandard solutions are confined to the silos where they originate.
To regain the control required for effective risk management and planning, companies need smart governance that aligns their AI use cases, processes, structures, vendors, data, compliance and security across their evolving solutions. “At the same time, your governance needs to allow an efficient path for compliant innovation,” Schulz said. “That’s how you create governance that drives business value.”
Technology firms need to empower their AI innovation while they also ensure that innovation drives financial performance with effective agility, scalability and risk management.
Proactive AI coherence
Tech firms have already seen AI solutions enhance performance across finance, operations, risk and other areas. However, companies need AI coherence in order to scale and grow the value of their AI solutions into the future.
Six steps to build AI coherence
Leaders can take practical steps to drive AI coherence across existing solutions and evolving initiatives.
1. Establish cross-functional accountability
“AI coherence cannot succeed unless you have cross-functional collaboration,” said Grant Thornton Technology Modernization Partner Tony Dinola. Create governance structures that bring together finance, risk, technology and business leaders around shared priorities. “You need to define clear ownership for outcomes, not just activities,” Dinola said. This engagement upfront will help ensure that decisions reflect enterprise impact rather than functional perspectives.
2. Integrate risk and control frameworks
Incorporate AI into existing risk management processes, including controls testing, monitoring and reporting. “Establish an accountable plan to move toward continuous validation models that provide real-time visibility into how systems are performing and where risks emerge,” said Grant Thornton Risk Advisory Managing Director Greg Haberer. This continuous validation builds confidence in solutions and results, while also reducing the risk of surprises in regulatory reviews.
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3. Rationalize tools and data environments
Assess where AI initiatives are creating duplication or fragmentation. Consolidate tools and align data sources to support consistency and scalability. This step improves cost efficiency and enables more accurate reporting and performance measurement.
4. Identify and define relevant AI use cases
Ensure that the organization clearly captures AI use cases in a standardized way that allows leaders to evaluate and compare their requirements and potential returns. This is a streamlined but consistent approach to forming a foundation for prioritization and planning that should inform your AI investments.
5. Anchor AI initiatives to enterprise value drivers
Enforce the alignment of AI initiatives with core business outcomes such as revenue growth, cost optimization, risk reduction and customer experience. “Evaluate your use cases in terms of how they contribute to these outcomes and make sure to establish clear metrics for success,” Dinola said. This process is a key element in prioritization and investment decisions for new initiatives.
6. Build operational capabilities, not just models
Focus on how AI operates within the business. Develop processes, workflows and supporting roles that ensure AI outputs are actionable and integrated into decision-making. This includes embedding AI insights into financial planning, risk assessment and operational processes.
Unified progress
As noted in Grant Thornton’s 2026 AI Impact Survey for the Technology Industry, tech firms have adopted AI faster than they have built controls for it.
AI coherence requires companies to embed a unified perspective into the business operating model. That involves visibility and accountability across finance, risk, technology and operations. Business functions must share a common view of how AI is performing, what risks it introduces and how those risks are managed. This transparency enables faster decisions and stronger accountability.
Leading organizations are pushing beyond the boundaries of early initiatives as they develop more connected operating models. By establishing governance that integrates standards into ongoing processes, they empower continuous management of both risk and performance.
This approach supports the shift from periodic assessments to continuous monitoring, testing and response, ensuring that AI systems are part of this continuous model rather than an exception to it. Connected and continuous execution also supports faster decisions, clearer accountability and stronger outcomes. It enables organizations to adapt as conditions change while maintaining confidence in how their systems operate.
AI coherence doesn’t happen by accident. Organizations need a proactive push for AI coherence that aligns enterprise strategy, execution and governance behind AI solutions. Without that, firms will struggle to move from AI value from pilots to profits. “In the long term, it’s an essential part of the link between AI innovation and performance,” Schulz said.
Contacts:
Managing Director, Risk Advisory Services
Grant Thornton Advisors LLC
Managing Director and Risk Advisory Technology Enablement leader driving AI governance, SOX modernization, and platform‑enabled assurance solutions.
Denver, Colorado
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