“It is always with the best intentions that the worst work is done,” Oscar Wilde observed. As just about any CIO who has watched a carefully planned AI strategy suddenly fall apart will attest, good intentions are no guarantee of success.
No CIO wants to damage or delay an important AI initiative, yet it happens far more often than many leaders care to admit. Therefore, gaining strong control over AI plans is now a top key CIO priority.
Averting Danger
Simply doing AI for AI’s sake can burn a lot of money without achieving any tangible outcome, says Danilo Kirschner, managing director of Zoi North America, a cloud technologies and software development firm. “This is why desired business outcomes and the contribution value of implementing AI should be assessed before creating an AI strategy,” he observes in an online interview.
A CIO can inadvertently derail AI innovation by allowing risk-averse stakeholders — often the CISO or security teams — to impose overly restrictive controls that stall experimentation and business-led use cases, says Laura Stash, executive vice president of solutions architecture at systems and process modernization firm iTech AG, in an email interview. “Additionally, relying solely on off-the-shelf AI add-ons, like Microsoft Copilot, without integrating them thoughtfully into core business workflows can limit impact.”
One of the easiest ways a CIO can derail an AI strategy is by forcing a transition when the problems are actually with people or processes — not the technology, observes Allen Brokken, a practice lead for AI Infrastructure at Google Americas. “Right now, with the explosion of models and capabilities, it’s very easy to get caught up in the next big announcement or capability and lose focus on the fundamentals of your people and process,” he states. “This is especially true when existing technologies in your organization are already bringing promising advances.”
Acceptable Alternatives
AI is not a standalone initiative, says Tom Gersic, senior vice president of AI and digital business at data and digital engineering services company Altimetrik. “Making AI part of broader business transformation efforts and measuring outputs versus outcomes is critical,” he says in an online interview.
“The key to keeping an AI strategy on track is getting team members to analyze the latest developments, yet have the discipline to only act when it will truly move the strategy forward,” Brokken says.
Ensure that deployed AI solutions actually save time or add clear business value; optional tools that slow workflows are doomed to fail, Stash states. “CIOs should encourage collaboration, provide ongoing AI training to business users … and invest in upskilling IT teams on prompt engineering, bias detection, and testing best practices.”
Getting on Track
Require all key stakeholders to revisit the project’s strategic goals, Gersic recommends. “Audit data quality and access [and] define quick wins to restore confidence.” He believes that it’s also important to showcase early successes.
While AI strategy impacts many stakeholders, effective course correction requires only one or two accountable leaders empowered to drive decisions and act swiftly, Stash says. “Too much collaboration without clear ownership often leads to ‘analysis paralysis’ and stalled progress.”
“The strategy’s accountable leaders — typically the CIO, chief AI officer, or a designated AI strategy lead — must possess the authority and mandate to align business, IT, and security teams,” Stash says. These individuals must be willing to make tough calls and enforce a clear plan to fix or replace the existing strategy. “Also engage critical stakeholders as advisors, but retain ultimate responsibility to ensure momentum and results.”
Don’t be afraid to fail, Stash says. A catastrophic failure can be a career killer, yet small AI use case failures shouldn’t be. The key, she notes, is to fail fast and forward. “Identify the real issues — whether it’s data, people, or security — and tackle them head-on.” CIOs who openly address challenges and pivot to use cases that work will build credibility and resilience. “Leaders who fear failure risk stagnation.”
Drop the Wand
AI isn’t magic — it’s messy, iterative, and demands gutsy leadership willing to fail fast and fix faster, Stash observes. “If your AI strategy doesn’t make jobs easier or deliver measurable value quickly, it’s just expensive window dressing.”
The CIOs who win obsess over adoption, usability, and mission impact — not just tech specs or buzzwords, Stash says. They invest boldly in people, data, and real change. “The others,” she notes, “get left in the dust.”
