Maintain control while you drive innovation
Executive summary
AI is no longer a future consideration; it is a present-day imperative. As AI capabilities become embedded across platforms and accessible to nontechnical users, organizations face a dual challenge: enabling innovation while maintaining control. The forces driving AI democratization — and the governance gaps it exposes — require strategic shifts to manage AI responsibly at scale.
A strategic inflection point
AI adoption is rapidly changing how organizations operate. This transformation is being driven by four main forces: market momentum, executive focus, economic benefits and easier access. Leaders preparing for the future of AI need to understand these trends:
- Market momentum: Venture capital, corporate R&D and platform investments are fueling a wave of AI innovation. From standalone solutions to embedded features, AI is entering the enterprise from every angle.
- Executive urgency: As AI changes the business landscape at a dizzying pace, leaders feel pressure to align with stakeholder expectations and investor narratives.
- Economic promise: While top-line impact remains emergent, AI’s ability to drive productivity and reduce costs is already reshaping operational models.
- Lower barriers to entry: No-code and low-code platforms are empowering business units to deploy AI independently, often bypassing traditional IT and governance channels.
As a result of these trends, AI is no longer centralized as it rapidly alters the business environment. This decentralization changes everything about how AI must be governed.
AI democratization: Opportunity meets risk
As AI tools become accessible to more employees, organizations gain new opportunities but also face greater risks. Decentralized adoption can lead to gaps in oversight and increased exposure to compliance and operational challenges.
Governance gaps that are emerging today include:
- Fragmented oversight: Governance frameworks are lagging behind innovation, often siloed across privacy, legal and security teams.
- Shadow AI: Business units, frustrated by slow reviews, are deploying AI solutions outside formal oversight.
- Regulatory ambiguity: Enforcement is limited, and compliance baselines are unclear. Many frameworks are untested in practice.
- Talent deficit: Few organizations have AI risk specialists. Risk comprehension varies widely across teams.
- Post-deployment blind spots: GenAI solutions evolve post-launch, yet most governance focuses only on pre-deployment due diligence.
These shortcomings show that traditional governance models are not built for the scale, speed or complexity of democratized AI. A strategic shift is needed to take full advantage of AI’s benefits while managing the risks this technology brings to any organization.
5 governance shifts for responsible AI
To meet the moment, organizations must rethink governance from the ground up. Below are five critical challenges presented by AI — and the tactical and strategic responses needed to address them.
Challenge 1: Volume overload
The number of AI use cases is growing quickly, making it difficult for risk and compliance teams to keep up. Organizations need better ways to prioritize and align AI projects with business goals.
Tactical response: Introduce a business alignment and ROI checkpoint for AI innovators to consider and document before risk review. Use a standardized framework to assess strategic fit and justify investment.
Strategic response: Develop a dynamic, forward-looking catalog of AI priorities, owned by business units and reviewed by leadership. This guides development toward high-impact use cases.
Challenge 2: Review bottlenecks
Risk reviews are often slow and repetitive. Streamlining these processes can help organizations respond faster and more efficiently to new AI initiatives.
Tactical response: Implement a unified intake process to streamline common questions across risk domains.
Strategic response: Establish a centralized AI risk review team with dotted-line ties to privacy, legal and security. Empower embedded risk officers to conduct first-level reviews, escalating only high-risk cases, thereby creating a federated governance model.
Challenge 3: Incomplete risk detection
Manual reviews may miss emerging risks. Integrating automated testing and proactive monitoring is essential for effective risk management.
Tactical response: Shorten questionnaires and embed automated testing (e.g., bias and fairness) into pre-production workflows.
Strategic response: Invest in AI risk testing tools and train development teams to integrate them into the lifecycle. Shift from reactive to proactive risk identification.
Challenge 4: Reactive remediation
Addressing risks only after deployment can delay innovation. Building in preventive measures and guidance from the start helps organizations stay ahead.
Tactical response: Adopt platforms with built-in guardrails. Train developers to use risk prevention features during setup.
Strategic response: Provide detailed guidance and use AI assistants to help developers build secure, compliant solutions from the start.
Challenge 5: Unmonitored low-code AI
Low-code and no-code AI tools can bypass governance controls. Ongoing monitoring and clear publishing guidelines are needed to manage these risks.
Tactical response: Restrict publishing rights based on solution risk levels. Conduct ongoing reviews and certifications.
Strategic response: Deploy centralized registries and analytics to monitor AI usage. Update incident response playbooks to include AI-specific scenarios.
How we can help you
SERVICE
SERVICE
The path forward: Balancing innovation and control
AI democratization is not a risk to be avoided; it’s a reality to be governed. The organizations that thrive will be those that:
- Promote responsible adoption: Embed safety and ethics into every phase of AI development.
- Break down silos: Shift from fragmented oversight to federated governance.
- Automate risk detection: Use AI to govern AI, integrating intelligent tools into the governance lifecycle.
Whether you're an aggressive adopter or a cautious follower, governance must evolve as fast as AI itself.
Contacts:
Partner, Cybersecurity and Privacy Leader, Risk Advisory Services
Grant Thornton Advisors LLC
Content disclaimer
This Grant Thornton Advisors LLC content provides information and comments on current issues and developments. It is not a comprehensive analysis of the subject matter covered. It is not, and should not be construed as, accounting, legal, tax, or professional advice provided by Grant Thornton Advisors LLC. All relevant facts and circumstances, including the pertinent authoritative literature, need to be considered to arrive at conclusions that comply with matters addressed in this content.
Grant Thornton Advisors LLC and its subsidiary entities are not licensed CPA firms.
For additional information on topics covered in this content, contact a Grant Thornton Advisors LLC professional.
Trending topics
No Results Found. Please search again using different keywords and/or filters.
Share with your network
Share