Daniel A. Keller, CEO and Cofounder of InFlux Technologies Limited and Flux.
AI competition is permeating throughout the world, with competition between nations and competition between AI companies. Across industries, including logistics and sales, I’ve also observed some companies racing to adopt AI and gain a competitive edge.
But in my view, not enough leaders are embracing AI quickly enough and deeply enough, to the point where I see a haves-versus-have-nots divide emerging that’s putting some companies on top while others are lagging behind. In fact, according to a June 2026 report commissioned by ManpowerGroup Talent Solutions, “only 3% of organizations report leaders are highly prepared to manage AI-enabled ways of working.” The report also noted that “the biggest barrier to AI transformation is no longer technology adoption but the lack of workforce readiness.”
For leaders, it’s not enough to attach an AI label to their companies. There has to be substance behind the label if they want to be on the cutting edge of AI—and the competition.
What Leaders Should Do To Stay On Top Of AI
I caution against leaders delaying action and pinning their hopes on emerging talent to drive AI strategy in the years to come. Waiting can cost their organizations relevancy and pull them into obscurity. The time to act is now. Leaders can take several steps to stay on top of AI.
First, leaders should make a mindset shift. Specifically, they should accept that AI is rapidly evolving and get comfortable with being uncomfortable and with outside-the-box thinking. Given the pace of AI advancement, I believe leaders who are more pragmatic (those who rely primarily on proven processes, predictability and extensive planning) may find it more difficult to keep up. That doesn’t mean AI implementation shouldn’t be carefully planned. Rather, leaders shouldn’t let the pursuit of certainty come at the expense of continuous learning, experimentation and adaptability.
Leaders should also strive to develop a strong understanding of AI concepts. They don’t necessarily have to become technically proficient in AI. In most cases, I don’t think they should. Details such as how LLMs are built and trained and how code works are what engineers and technical specialists—but not all leaders—need. For many leaders, understanding what enables AI and where AI can create value is more important than understanding technical details.
Next, there’s engagement. I believe that any leader who hasn’t seriously engaged with AI yet should start doing so now and encourage their team to do the same. If there’s already engagement, it should be continuous. By engaging with AI in different ways, such as experimenting with chatbots for routine tasks and using an LLM to summarize key information from documents, leaders and their teams can start grasping how these tools can benefit them.
In addition to engagement, there should be hands-on, continuous education. Microlearning (short, ongoing learning opportunities) can help teams stay up to date with AI without lengthy training programs. Following AI developments in the news, signing up for webinars and workshops and participating in AI-related communities can be other effective ways to stay in the loop about AI advancements.
Key AI Implementation Mistakes Leaders Should Avoid
While I see staying on top of AI as imperative, leaders should also be wary of AI’s limitations and risks and avoid making mistakes that can hinder their progress.
For one, leaders shouldn’t assume that AI can fully take over human roles without oversight. Instead, they should treat AI as a tool to augment employees’ expertise and work, particularly repetitive, monotonous work, so that employees can have more time for strategic and dynamic tasks. Consider a July 2026 CNBC article that noted that some employers “who laid off workers citing AI are already starting to regret it.” The article pointed to Ford as one example, stating the company “is reportedly rehiring hundreds of experienced human engineers to work on quality issues that automated systems couldn’t address.”
Leaders should also craft clear data governance policies that guide them through the following questions: what data is being shared, where that data goes, who controls it and how it is protected. Uploading sensitive data to AI tools, especially public AI tools, can put companies at risk of their proprietary information becoming publicly exposed. Strong data governance policies help organizations use data with AI tools while safeguarding privacy to the extent possible.
Additionally, leaders should be mindful that AI tools can produce biased or inaccurate results. Using high-quality data and maintaining human oversight can help organizations identify and mitigate these risks before AI-generated outputs influence business decisions.
How Leaders Should Decide Between Outsourcing Versus Building AI Tools Internally
As I’ve previously written, organizations can successfully implement AI tools even if they have limited internal expertise and lack big budgets.
One of the key implementation questions for leaders is whether to partner with third-party providers of AI tools or build them in-house. Building AI tools in-house has its pros, such as greater data privacy and customization, but doing so can be extremely time-consuming and expensive. I don’t recommend this approach for most companies unless they have the internal expertise and resources to keep pace with AI’s rapid evolution and have specific reasons to build internally, such as AI being a core part of their business model or needing greater control over their data for privacy and governance purposes.
Partnering with third-party vendors is often less time-consuming and expensive, but it can come at the cost of greater data privacy and customization. However, if leaders ask prospective vendors the right questions about their level of AI expertise, their data privacy and governance policies, their scalability and so forth, they are more likely to find a partner aligned with their goals.
As AI Evolves, Leaders Should Be Ready To Adapt
I see AI as still being in its early stages, and I predict it will change dramatically in the years to come. Leaders should cultivate and maintain mindsets of adaptability and continuous learning. As AI evolves, leaders should be ready to navigate changes to safeguard their professions and companies.
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