AI Readiness for the Enterprise – Beyond the Hype

YOU ARE GOING TO FAIL

This isn't a threat—it's an invitation. Failure, if embraced well, becomes your sharpest tool for transformation. Failure is essential, and as an enterprise leader and decision-maker tasked with scaling AI responsibly, you will only get your organization AI-ready by using those failures productively. You will fail and fail repeatedly, and that's a good thing. Failing is easy. I remember not studying enough for my first epidemiology master's exam. I already had a PhD in Mathematics, so I figured I didn't need to study. I was wrong! Although the Mathematics part was easy, remembering how to read and answer the questions being asked was not. So, if failing is easy, why must we get good at it to help our organizations become AI-ready? It's because failing well is hard. You need to take failures and use them productively, and you need an organization with the necessary skills and mindsets to do this. So, we want you to fail but to do so well, and we'd like to posit some particular skills that can help you and your organization achieve this.

As leaders navigating this inflection point of AI, we must move beyond hype cycles to cultivate the human capabilities that allow AI to serve not just profit, but purpose. The ability to "fail well" is not only a competitive advantage but an ethical imperative. In a rapidly evolving AI landscape, readiness demands that we learn, adapt, and lead responsibly across the systems we shape. The ability of "failing well" embodies the responsibility to mitigate harm and uphold societal trust. When AI systems falter, whether due to biased algorithms or opaque decision-making, real people bear the consequences, from discriminatory outcomes to eroded privacy. Leaders like you embrace failure as a learning opportunity, transparently addressing and correcting missteps, demonstrating accountability, and prioritizing human well-being over profit or reputation. This approach not only prevents escalation of harm but also fosters public confidence in AI technologies, ensuring they evolve to serve a greater purpose aligned with fairness and equity.

When you think about your organization, company, or industry, you already know (or at least suspect) that you must embrace AI, specifically Generative AI and Large Language Models (LLMs), to stay competitive or even to edge ahead. AI ambitions are growing alongside the investments made in this space. In 2024, global venture capital investment in AI has surged with a 62% year-on-year growth to $110bn¹. That level of investment fuels not only new foundational models but new models of working and new opportunities for application. It brings a lot of choices and a lot of continuous and rapid change to the field.

With the rapid progression of the field and the widespread adoption of GenAI, choosing the right approach to a given problem is likewise challenging. In a recent report, only 26% of companies are reportedly achieving and scaling value². Is it due to the choices that need to be made? Is an Agentic approach the best, a Retrieval Augmented Generation approach, a tuned small or large general model, etc.? Will my choice today still be correct in a week, month, or year? So, we have uncertainty in the right approaches, uncertainty in realizing value, and uncertainty due to the rapidly changing nature of the area. However, 26% of companies do succeed in creating value.

How can you be in the 26% that succeed? You need to be ready to decide and for that decision to be wrong. To fail but fail well. Accepting and incorporating the art of failing isn't easy, and a single chapter doesn't solve all your needs. However, the art of failure is the ability to quickly apply what you have learned to the next strategic approach to the problem you are solving.

But ‘How do I do this?’ I hear you cry. It all starts with your workforce. You need to train them not just in AI or specifically GenAI (although that's certainly useful) but in three core competencies:

● Openness to Change

● Strategic adaptability

● Resilience

Openness to Change

GenAI isn't a flash in the Pan – For Example, Deloitte explicitly states that generative AI is not a fleeting trend and that the rate of progress is increasing. Their October 2024 report³ highlights that GenAI use cases are proliferating across business functions and that organizations see an "increase in the number of foundational capabilities that help the organization access GenAI advancements as they emerge." We need to be open to this rate of change, and we aren't. Substantial psychological evidence shows that many believe themselves to be open-minded or open to change. Still, openness to change is much rarer than self-perception suggests⁴. So, GenAI and the field, in general, are moving quicker than ever, and we are not as open to change as we think. How do we address this? You need your workforce to train for this skill and then practice it in their daily work to intentionally strengthen their ability to shift perspective and help incorporate the challenges associated with AI.

Openness to change invites alternative perspectives and approaches. Embrace a willingness to experiment with something new, even when you don't have all the information, and seek new ways of working to improve the impact of your efforts. Acknowledge the initial effort and celebrate the first step toward change. Overcoming resistance and making the first change can feel daunting, but this is where the momentum starts. So, if you are not yet seeing the value of AI in your organization (or are, but not as much as desired), experiment with small changes first. Instead of overhauling an entire process with AI, try implementing one slight improvement and seeing how it impacts your workflow.

There is one crucial myth to dispel, which I continually face from my peers and other leaders.

THE MYTH: With everything on our plates and all these unknowns, we can't afford to spend time changing how we do things. Rethinking these processes is too time-consuming and costly when we all take on extra responsibilities, and experimenting with minor adjustments would slow us down.

THE TRUTH: Even with new responsibilities or in situations where we don't have all the information, experimenting with small innovations is critical. Openness to change, especially seemingly small changes, helps us identify innovative and efficient AI processes that save time and effort, allowing our hard work to have the most significant impact. In an era of AI acceleration, failing well becomes not just a leadership skill—but a systems-level intervention.

"Systems that enable success with today's model reinforce behaviors inconsistent with discovering tomorrow's model." – Russell Ackoff, a pioneer in systems thinking and organizational theory.

Take action: Streamline your work. Identify one area of your work where openness to change could help streamline your organization's workflow. To help you, consider the following questions:

● What is taking up much time and not providing an equal benefit or return?

● When do you spend extra time clarifying for others or seeking clarity yourself?

● Where do you lose time by indirectly approaching or not discussing specific topics?

Identify one new action, statement, approach, or behavior you can implement or suggest and plan to put it into action (e.g., when you tackle a particular task at a specific upcoming meeting, etc.)

Strategic adaptability

The second skill you need your workforce to have is strategic adaptability. You're following GPS directions, focused on reaching a clear destination. Suddenly, an accident ahead stops you in your tracks. Do you wait for the original path to clear or reroute to keep moving forward? Once openness to change is in place, strategic adaptability enables leaders to navigate uncertainty with clarity. Training strategic adaptability in the context of AI can help you create efficiencies within your team and even help efforts produce better outcomes.

What do we mean by "strategically adaptable"? Like a rerouting GPS, being strategically adaptable helps you stay focused on where you're going, navigate uncertainty, and adjust your route as new information arises—all while keeping your destination in sight. You aren't failing per se, but your initial plan may no longer be viable. When you're strategically adaptable, you are:

● Comfortable with ambiguity and adept at asking complex, often unanswerable questions ● Able to identify unknowns, define clear objectives, and maintain a broad, thoughtful perspective

● Able to craft and execute an integrated approach to overcome obstacles to achieve those objectives, even when the perfect path forward is unclear

● Vigilant over your progress—but flexible enough to change elements of your approach as you go to achieve a greater impact

So, how do you put it into practice? With all the uncertainty inherent in AI, 1) Identify a north star to guide your decisions rather than trying to chart a detailed map before you begin. This allows you to identify parameters, a clear business need, and the objective while leaving space to dive courageously without knowing all the information. 2) Use the company's broader objectives as your compass to ensure you're moving in the right direction. As you work toward your north star and chart your path, don't be afraid to follow the compass to adjust decisions—not the objective itself— if variables, assumptions, or information change, requiring a different path.

How does this put you ahead? It reduces pressure to be perfect. Strategic adaptability allows you to experiment and adjust rather than aiming for a flawless plan. You can fail fast. It increases agility - strategic adaptability helps you respond calmly to changes and stay on track. By asking the team to focus on what is in their control and shift to critical priorities, you can fail well. It also improves efficiency, strategic adaptability enables faster decisions by focusing on what's necessary instead of waiting for perfect information–you aren't afraid to fail.

Take action: Just as with openness to change, think with an enterprise mindset and seek strategic opportunities for AI. Identify one system, process, or other barrier in your thinking or seek strategic opportunities for applying AI. What support, if any, could help you overcome the barrier? Identify one action or statement you can use regularly to help you or your team think with an enterprise mindset and seek strategic opportunities.

Resilience

If openness sparks momentum and adaptability charts the course, resilience keeps the engine running. Resilience is the force that sustains innovation when change is hard, and the path forward is unclear—making it the most essential of all three skills for building AI-ready enterprises. Learning to be resilient isn't as simple as growing a thick skin or not caring that something didn't work out as planned. In my humble opinion, the core and most crucial skill binds all three. If you and your team cannot learn to be resilient, you will not get the opportunity to be open to change or strategically adaptable. Building resilience is tough. Consider how a martial artist develops both the technique and the strength of muscles, bones, and tendons to break bricks. This is something close to my heart as I've broken my share of bricks (and my hands). If you don't have the proper technique and haven't been conditioning your hands by continually bashing them against obstacles, you won't build the resilience you need to succeed. The same is 100% true when incorporating AI into your company. Without the technique of resilience and coping with failures, you won't build the strength you need to succeed. So, in the context of AI, this means weathering failures and setbacks and viewing the ability to fail productively and quickly as a necessary part of identifying new and better outcomes, ideas, or ways of working. It is important to see the valuable opportunities in failure and deliberately choose to learn as much as possible from the experience.

You can probably spot the theme here; just like the other two skills, experiment with minor changes and keep trying. - "I see a few ways this could be tweaked— I'm going to make a few changes and try again.". But for resilience, also reassess and adapt when there are changes outside your control (e.g., external changes) - "This government policy change has shifted the landscape, but it also revealed some gaps we can address. Let's discuss what we've learned here to adjust our strategy and test a new approach.". You don't need to go it alone; tap into it and strengthen your social support. - "This didn't go as expected, and I'm finding it tough to keep going. Can I tell you about it, and maybe you can share what you've done in the past to help you keep pushing forward?" Lastly, gather insights from multiple perspectives. - "From your perspective, what do you see that went wrong? What does it teach us about doing this differently next time?"

Take Action: Push for an outcome, a result, or an approach; keep trying, and discuss failure. Identify an instance where you failed and didn't try again. How could you have reacted with more resilience? Also, identify an instance where you failed and tried again. How did you exercise resilience in this situation?

Summary

By embedding your workforce's three core skills: openness to change, strategic adaptability, and resilience, you can build a successful enterprise, move past the hype, and integrate meaningful scaled AI solutions that realize value. While you fail, you make and practice the skills, and you need those skills to implement AI/GenAI effectively. It's a virtuous circle, a self-reinforcing chain of positive events, where each good outcome leads to another, creating continuous improvement or sustained success⁵.

But remember, the three core skills aren't just for the enterprise. They are also for you, and here's how you can change your thinking.

Open to Change – Think this: "If we weren't tied to how we've always done things, how would we approach this problem?" Not This: "This is how we've always handled it, and it's worked fine so far—let's just stick to the process."

Strategically Adaptable – Think this: "How would you approach this if you were making the final call?" Not this: "I'll make the final call on this—just focus on executing, not questioning the approach."

Resilient – Think this: "What did we learn from this approach that we can apply moving forward?" Not This "Mistakes in this space can be costly — let's play it safe and avoid experimenting too much."

So, with these three core concepts, you can fail well, and as a leader, you can take actions that foster an environment for AI. Build your tolerance for "error," allowing for fast and productive failures.

YOU ARE GOING TO FAIL

FAIL WELL.

Footnotes

1. DealRoom, Opening Moves in Global AI Venture Capital, 2025.
2. Boston Consulting Group, AI Adoption in 2024: 74% of Companies Struggle to Achieve and Scale Value, 2025.
3. Deloitte, State of GenAI Q4 2024 Report, 2025.
4. Wanberg, C. R., and J. T. Banas, “Predictors and Outcomes of Openness to Changes in a Reorganizing Workplace,” Journal of Applied Psychology 85, no. 1 (2000): 132–142.
5. Oxford English Dictionary, s.v. “virtuous cycle,” accessed May 2025