Is AI really living up to the hype? Many companies are doubting it. Per Forrester’s recent report, less than a third of companies can link AI efforts to impact on profit and loss. As a result, CIOs are being tasked with bailing out failed initiatives and looking beyond technical fixes. This requires a deeper look into the governance process and how AI can be used strategically and practically.
For CIOs who are preparing to take over a failed AI project, here are our recommended steps:
Figure out where things went wrong. AI failures are typically caused by poor data quality, a fragmented strategy, unclear goals, lack of governance, and/or lack of training and communication. Was the project tied to a measurable outcome? Was data clean, accurate, and timely? Were users prepared to adopt it? Summarize findings in a short analysis report, and be specific. If clean data was an issue, for example, specify what about that data was problematic. Perhaps overlapping data sources resulted in redundancies. From the findings, identify what can be salvaged and what must be rebuilt.
No recovery effort succeeds without an engaged sponsor who is accountable for the results. The CIO must bring leadership back to the table and reconnect the AI initiative to business value. A concise charter should document purpose, scope, and measurable objectives. This ensures the effort is seen as business transformation, not an IT project. AI projects collapse when they’re just treated like a technology experiment. They must be treated like a business initiative.
Before coding anything new, fix the foundation. A simple readiness checklist can help prevent previous mistakes. Start with these three areas:
AI without oversight can be chaotic and create a lack of trust. CIOs should establish a governance framework that defines ownership, accountability, and decision-making. The steering committee should include IT, legal, data, and business leaders who can help set standards for model training, data management, monitoring, and compliance. Governance is not intended as bureaucracy but as structure that allows AI to scale strategically.
Rescuing a project also means rebuilding the team and partner ecosystem. Failed initiatives often reveal skill gaps and unclear responsibilities. CIOs must assess internal capabilities and invest in upskilling key roles. Externally, CIOs can realign partners (vendors, consultants, etc.) around business goals and performance metrics.
Cross-functional teams that blend technical expertise with domain knowledge are the most effective for moving forward. Teams should own outcomes rather than activities.
To rebuild credibility, shift the delivery model toward quick, visible wins. Instead of relaunching a massive, multi-year program, start small. Identify one high-impact use case that can deliver results in 60-90 days. Develop a minimum viable model to prove value, measure the outcome, and communicate success across the organization. This helps AI gain traction, restores executive confidence, and creates momentum for future AI investment.
Sustainable success ultimately depends on adoption and continuous improvement. Even the most accurate AI solution will fail if no one uses it. CIOs must lead communication and training efforts. Develop a communication and training plan that focuses on:
When the project stabilizes, the CIO’s final step is institutionalizing learning and creating a framework for AI moving forward. Organizations need a repeatable model for governance, retraining, post-implementation reviews, and continuous learning to help people use AI responsibly and stay up-to-date. An AI Center of Excellence consisting of internal experts can turn isolated projects into a strategic enterprise capability.
Currently, 39% of CIOs/CTOs lead AI technology strategy and 21% lead AI business strategy in their organization (Forrester). Those numbers are likely to increase as excitement fades and reality sets in. Organizations will need stronger governance and leaders will need to see real results to make AI a useful part of the business.
Measurable success requires more than technical know-how. The CIO must serve as strategist, communicator, and change leader. If your organization is facing a stalled or failed AI project, the best next step is to start preparing now.
At Trenegy, we help CIOs develop and implement realistic AI strategies that align with business needs and long-term goals. To chat more, email us at info@trenegy.com.
Trenegy is a non-traditional consulting firm based in Houston, Texas.