Editor’s note: The following is a guest post from Francis Brero, VP of AI strategy at HG Insights.
We’ve all seen the alarmist headlines proclaiming that 95% of AI initiatives are failing. But the sky is not falling. In fact, these so-called failures are actually a sign of success.
It’s better to run 1,000 experiments with a 10% success rate than two experiments with a 50% success rate. Failure rate is the wrong metric when velocity is the only thing that matters.
As AI becomes embedded throughout business and society, the role of a chief AI officer has emerged as a bridge between innovation and practical utility. C-level executives and boards have been supportive of AI initiatives, but businesses are starting to question AI experiments as costs rise.
To help drive adoption, CAIOs must educate the C-suite that if the company can learn from an experiment, it’s actually a success.
It’s difficult for companies, and people, to change — yet AI constantly propels change. One of the key responsibilities of a CAIO is to drive talent management change.
Successful change management hinges on aligning employee and company identities while incentivizing positive attitudes toward AI-driven shifts. Leaders must find ways for people to see how exciting it can be to relearn a skill, or ask themselves why they’ve always done something a certain way.
There’s a disconnect between expected AI efficiency gains and where we’re heading. Companies are heading toward a stage where, instead of 10 people doing what 100 workers used to, now these same 10 people will do what 100 workers were previously unable to do.
Finding those 10 people is incredibly important. Traditional models of scaling through hiring cheaper resources are becoming obsolete. That’s why leaders need to rethink talent density, shifting towards hiring the right systems thinkers who can automate execution.
Organizations should set targets for task automation. With current AI models, it’s practically impossible to get to 100% for any given task. Achieving 80% is feasible for many job duties, so teams can map out opportunities to go from 0% to 80% as helpful resource allocation.
AI agent value output
Part of this talent density equation means getting to a point where the value output of AI agents surpasses the output of human contributors. This is imperative because that’s when people become managers of these AI agents.
These changes are happening quickly. Each week seemingly brings a new AI release or mini-crisis inflection point to rile everyone up. AI leaders are charged with keeping teams up to speed without overwhelming them with information.
It’s crucial for CAIOs to guide people on incorporating AI into their day-to-day work strategically and impactfully. When done properly, the most rewarding aspect is the visible success of individuals overcoming complex challenges more efficiently.
Security is also top of mind amid change. Every company has employees using ChatGPT, whether it’s officially sanctioned or not. Company data is going into ChatGPT. But the security risks go far beyond data leakage.
Model context protocol servers can be used to exfiltrate data if an adversarial player knows how to prompt them. There have been reports of people sending calendar invites with a prompt that will inject instructions to an LLM when it reads the content of the invite.
Executives should start thinking about the implications of all this. As the AI landscape moves into 2026, CAIOs will need to work closely with CISOs to determine the best compromise between velocity and risk.
The workslop problem
Another concern is the prevalence of workslop. Experts estimate that 30-60% of internally shared documents are AI generated, constituting mediocre content passed off as legitimate work.
Documents with title case, abundant em dashes and paragraph separations scream “this was written in ChatGPT.” Slides generated by Google’s Nano Banana and similar tools are starting to show up and they overcrowd without providing clarity. This creates slow but existential drift at a time where product-market fit is extremely fluid.
The CAIO needs to educate executives about the risks, steering the AI work culture towards accountability and transparency. Leaders must invest in building workslop detection, starting low-tech and improving over time.
Why the CAIO role is existential
The fate of any enterprise depends on its ability to drive the right kind of AI usage across the organization.
Not just adoption. Not just velocity. The right kind of usage that balances experimentation with security, amplifies talent density and prevents workslop from derailing product-market fit.
Companies that get this wrong are building on quicksand. Companies that get it right are building the future.
From talent density challenges to security risks to workslop, there’s a lot on CxOs’ plates ahead. But the CAIO isn’t just another executive role. It’s the role that determines whether a company survives the AI transition or gets left behind.
