Last month, I sat across from a CMO who runs marketing for a $400 million services business. She told me her company had invested heavily in a new CRM, built dashboards for every team, and launched a data analytics project with a major consulting firm.

Then she said something that really got my attention:

“We’ve got all this data, and I still don’t know what our customers actually need from us next year.”

This is the paradox that defines leadership in 2026. Organisations have more data than ever before, yet less clarity. They have more AI tools available than at any point in history, yet most leaders haven’t personally used them in any meaningful way. And the gap between those who are leaning in and those who are watching from the sidelines is becoming a chasm.

The gap between the leaders who are building AI capability today and those still “waiting for the right moment” is compounding invisibly, every single day.

The Data Readiness Gap Is a Leadership Problem

According to Deloitte’s 2026 State of AI in the Enterprise report, 42% of companies believe their strategy is highly prepared for AI adoption—but they feel significantly less prepared when it comes to infrastructure, data quality, and talent [1]. BCG’s AI Radar report found that C-level executives who are deeply engaged with AI are 12 times more likely to be among the top 5% of companies winning with AI innovation [2].

Read that again: twelve times more likely.

This isn’t a technology problem. It’s a leadership problem. Most organisations aren’t stuck because AI is insufficient. They’re stuck because the foundations underneath—the data, the culture, the willingness to experiment—aren’t ready.

I’ve written previously about how organisations slowly lose visibility of the very thing that drives long-term performance: their ability to respond to customers and the market [8]. As companies scale, attention shifts from customers to internal metrics—forecasts, budgets, targets, and quarterly results. None of this is wrong. But it creates blind spots. And in the age of AI, those blind spots are widening faster than ever.

Why “Waiting for the Strategy” Is the Riskiest Strategy of All

Here’s what I see happening in most organisations: the board says “AI is a priority.” A task force is assembled. Consultants are engaged. A 12-month roadmap is produced. And meanwhile, 12 months pass where nobody in the leadership team has personally used an AI tool to solve a real business problem.

This is the equivalent of a CEO declaring that customer-centricity is the number one priority—and then never speaking to a customer. I’ve seen this pattern before, and I wrote about it when I discussed how leaders of million-customer companies stay connected to reality [9]. The most effective leaders don’t just receive filtered reports. They create what I call “visceral knowledge”—understanding that lives in your gut, not just your head. They try to purchase their own product. They call their own service line. They sit with customers.

The same principle applies to AI. You cannot lead an AI transformation if you haven’t personally felt the friction, the surprise, and the potential of these tools. You need visceral knowledge of AI, not just strategic knowledge.

The Atomic Habits of AI-Ready Leadership

In a recent article, I explored how James Clear’s Atomic Habits framework applies to customer-centricity transformation [7]. Clear’s central insight is deceptively simple: you do not rise to the level of your goals; you fall to the level of your systems.

The same principle applies to AI readiness.

Your organisation will never become AI-ready through a single transformation initiative. It will only become AI-ready when the right behaviours become automatic—when experimentation, data literacy, and iterative improvement become habits embedded in your culture.

Consider the brutal math of 1% daily improvement. Get 1% better at using data and AI tools each day, and you’re 37 times more capable after a year. But if you’re declining—falling further behind competitors who are building these habits—you deteriorate to nearly zero.

Here’s what this looks like in practice:

Habit 1: Get Your Hands Dirty

The single most important thing a leader can do right now is use AI tools personally. Not delegate it. Not watch a demo. Use them.

Ask an AI assistant to summarise your last 10 customer complaints and identify patterns. Use it to draft a customer communication and compare it to what your team produced. Feed it your NPS verbatims and ask it what your customers are really saying. Try building a simple automation that saves you 30 minutes a week.

Will the output be perfect? No. And that’s the point.

The value isn’t in the first attempt. It’s in the iteration. It’s in learning what AI does well, what it doesn’t, where it hallucinates, and where it reveals something your team missed entirely. This iterative process—experiment, fail, refine, improve—is how you build the judgment to lead an AI-enabled organisation.

Half of the CEOs in BCG’s 2026 survey believe their job stability depends on getting AI right this year. Yet 60% admit they have intentionally slowed implementation due to concerns about errors [2]. This tension between urgency and fear is where leadership is tested—and where the experimental mindset becomes your greatest asset.

Habit 2: Make Data Readiness a Daily Practice, Not a Project

AI is only as good as the data it consumes. Yet many organisations are, as one analyst described it, “building the runway while the AI plane is already in the air.” Informatica’s 2026 survey of 600 data leaders found that nearly 7 in 10 organisations have adopted generative AI, but 75% of data leaders say employees need serious upskilling in data literacy [3].

This isn’t about a massive data transformation project. It’s about daily practices. When I worked with Canon Medical Systems ANZ, Managing Director Monica King didn’t launch a “data initiative.” She introduced a habit: adding a customer story to the start of every meeting across the organisation. Real stories. Real data. Real customer experiences. Over time, this single practice built what I call “customer muscle memory” throughout every function and contributed to compound annual revenue growth of 12.5% [11].

Now imagine extending that habit. What if every meeting started not just with a customer story, but with a data insight generated by AI from your latest customer feedback? What if your teams were trained to question the data—to ask “what are we not seeing?” before making decisions?

The organisations that will thrive aren’t those with the most data. They’re those with the best habits around data. Data readiness is a cultural discipline, not a technology project.

Habit 3: Create Permission to Fail—Then Iterate

When Jeff Bezos was asked why Amazon’s growth and profitability was growing exponentially, he said: “It’s probably because of what we did three years ago.” That compound effect only works when you start.

PwC’s 2026 AI predictions make it clear: technology delivers only about 20% of an initiative’s value. The other 80% comes from redesigning work [4]. That redesign requires experimentation. It requires teams to try, fail, learn, and try again.

Sean my cofounder saw this principle in action with Johannes Spille at Rosen Group [10]. Johannes didn’t wait for an executive mandate to begin transforming customer-centricity across the organisation. He proposed an idea, got executive support, and then did something remarkable: he invited participation rather than prescribing solutions.

He presented the MRI findings transparently and asked one powerful question: “What matters most?”

The response was 34 volunteers across 14 departments. Not mandated. Volunteered.

The same approach applies to AI adoption. The leaders who will succeed are not those who mandate AI usage from the top down. They’re those who create safe spaces for experimentation, celebrate the learning that comes from failure, and relentlessly iterate toward better outcomes.

The Identity Shift: From “We Use AI” to “We Are an AI-Ready Organisation”

James Clear’s most powerful insight is that true behaviour change is identity change. The difference between “we’re implementing AI tools” and “we are an organisation that continuously learns, experiments, and adapts with technology” is profound.

When customer-centricity becomes who you are, rather than what you’re trying to achieve, decisions become easier. An employee doesn’t need to consult a policy manual to know whether to accommodate a customer request. The same is true for AI. When learning and experimentation become your identity, you don’t need a 50-page AI strategy document to know that every team should be exploring how AI can improve their work.

At Vodafone, under CEO Vittorio Colao, the company co-developed the “Vodafone WE CARE” framework—a simple, shared set of principles that made it easy for any leader in any market to prioritise customer needs. They didn’t create a complex playbook. They created a shared identity. The result: NPS leadership in 19 of 22 markets and a 14-percentage-point swing in EBITDA [11, 12].

What would the equivalent look like for AI readiness? It would be a shared commitment—visible from the board to the front line—that says:

“We are an organisation that uses data and technology to understand our customers better than anyone else. We experiment. We learn. We iterate. And we never stop.”

The Compounding Risk of Inaction

I’ve written about the “Plateau of Latent Potential”—the period where consistent effort produces no visible results. Most organisations give up during this phase, just before the breakthrough.

But there’s a darker version of this curve that applies to inaction. While you’re waiting, your competitors are building AI capabilities that compound invisibly. They’re training their people. They’re cleaning their data. They’re running experiments that fail 80% of the time but produce breakthroughs in the other 20%.

McKinsey’s data shows 88% of businesses are now using AI in some form [5]. But there’s enormous variance in depth and sophistication. Only about 4% of firms have truly mature, AI-driven capabilities across all functions [6]. The window to build foundational capability is not closing—but the cost of catching up is increasing every quarter.

Remember: Kodak didn’t fail because digital photography was a surprise. They failed because their leadership lost touch with where customers were heading. Nokia’s leadership didn’t lack intelligence or effort. They lacked clear visibility of what was actually happening in the market and the willingness to act decisively on it.

The question is not whether AI will transform your industry. It will. The question is whether you’ll be the one driving that transformation or the one being disrupted by it.

A Practical Framework: Your AI Leadership Readiness Checklist

Based on what I’ve observed working with leaders across industries, here is a simple framework for building AI readiness—not through a single initiative, but through daily leadership practice:

1. Start with yourself. Have you personally used AI to solve a real business problem this week? If not, start today. You cannot lead what you don’t understand at a visceral level.

2. Measure your starting point. Most leaders operate with blind optimism about their culture. They believe they’re more data-driven than they actually are. The Market Responsiveness Index (MRI) gives you an honest, evidence-based view of how responsive your organisation truly is—including whether you have the cultural foundations for AI adoption. You cannot improve what you don’t measure.

3. Create weekly AI experiments. Dedicate one hour per week for your leadership team to try AI tools on real problems. Share what worked, what didn’t, and what surprised you. Make it safe to fail. The insight is in the iteration, not the initial attempt.

4. Clean your data as a habit, not a project. Every meeting, ask: “What data did we use to make this decision? How fresh is it? What are we missing?” Build data discipline into daily operations.

5. Invest in people before platforms. 82% of companies in early stages of AI maturity haven’t implemented a talent strategy for AI [6]. The technology is available to everyone. Your competitive advantage is in your people’s ability to use it.

6. Shift from goals to systems. Don’t set the goal of “become AI-driven.” Build the system: weekly experiments, monthly reviews of what’s working, quarterly assessments of data readiness, and annual cultural measurement to track progress.

The Question That Changes Everything

When I work with leaders on customer-centricity, I always return to a simple diagnostic question: “What is it actually like to be our customer today?” Not what you hope it is. Not what your dashboard says. What is it actually like?

The AI equivalent of that question is this:

“If a competitor used AI to understand our customers better, move faster, and deliver more value than we do—how long would it take before our customers noticed?”

If the honest answer makes you uncomfortable, good. That discomfort is data. And data, as we’ve learned, is only valuable when you act on it.

The leaders who will define the next decade are not the ones with the biggest AI budgets or the most sophisticated platforms. They are the ones who got their hands dirty first. Who built the habits of experimentation and iteration into their culture. Who understood that AI readiness is not a destination—it’s a practice.

But amid all the noise about AI transformation, there is one question that must sit above every other: is this making things better for our customers?

AI that improves how you understand customers, anticipate their needs, and deliver value—that’s worth pursuing relentlessly. AI that adds complexity, creates friction, or distances your people from the humans they serve—that’s getting in the way.

Every AI experiment, every data initiative, every new tool should be held against this standard: does it help us serve our customers better? If the answer is yes, lean in. If the answer is no—or “we’re not sure”—that’s a signal to stop, listen, and recalibrate.

Because in the end, your organisation won’t rise to the level of your AI strategy. It will fall to the level of your AI habits.

And the habit that matters most? Remembering that what’s best for the customer is always what’s best for your business.

Ready to find out if your organisation has the cultural foundation for AI adoption?

The Market Responsiveness Index (MRI) measures the eight behavioural disciplines that determine whether your teams can adapt, innovate, and respond—to customers, to markets, and to the technological shifts reshaping every industry.

Start with measurement. Then build the habits that compound.

Discover your organisation’s true readiness

References

[1] Deloitte (2026). The State of AI in the Enterprise, 8th Edition. Deloitte AI Institute. Survey of 3,235 leaders conducted August–September 2025. Available at: https://www.deloitte.com/us/en/what-we-do/capabilities/applied-artificial-intelligence/content/state-of-ai-in-the-enterprise.html

[2] Boston Consulting Group (2026). AI Radar 2026. Survey of 640 CEOs and 2,360 senior leaders. Reported in: World Economic Forum (January 2026), “CEOs Are All In on AI but Anxieties Remain: What Leader Confidence Indicates for 2026.” Available at: https://www.weforum.org/stories/2026/01/ceos-are-all-in-on-ai-but-anxieties-remain/

[3] Informatica (2026). CDO Insights 2026: AI Adoption Accelerates, but Trust and Governance Lag Behind. Survey of 600 global data leaders. Available at: https://www.informatica.com/blogs/cdo-insights-2026-ai-adoption-accelerates-but-trust-and-governance-lag-behind.html

[4] PwC (2026). 2026 AI Business Predictions. Available at: https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-predictions.html

[5] McKinsey & Company (2025). AI adoption data cited in: Linder, C. (2026), “Readiness to Results in the Age of AI: Four Imperatives for 2026,” TechPoint Community Connect keynote, February 2026. Available at: https://techpoint.org/ai-readiness-imperatives-2026

[6] TechRepublic (2026). AI Adoption Trends in the Enterprise 2026. Published January 7, 2026. Available at: https://www.techrepublic.com/article/ai-adoption-trends-enterprise/

[7] Clear, J. (2018). Atomic Habits: An Easy & Proven Way to Build Good Habits & Break Bad Ones. Penguin Random House.

[8] Crichton-Browne, S. (2026). “You Can’t Handle the Truth: Why Most Leaders Say They Want Clarity — But Won’t Take the First Step.” MarketCulture Blog, March 12, 2026. Available at: https://blog.marketculture.com/2026/03/12/you-cant-handle-the-truth-why-most-leaders-say-they-want-clarity-but-wont-take-the-first-step/

[9] Brown, C. L. (2026). “The 4 Ways CEOs of Million-Customer Companies Stay Connected to Reality.” MarketCulture Blog, January 25, 2026. Available at: https://blog.marketculture.com/2026/01/25/the-4-ways-ceos-of-million-customer-companies-stay-connected-to-reality/

[10] Crichton-Browne, S. (2026). “Leading Without the Title: How Johannes Spille is Driving Strategic Change at Rosen Group.” MarketCulture Blog, February 26, 2026. Available at: https://blog.marketculture.com/2026/02/26/leading-without-the-title-how-johannes-spille-is-driving-strategic-change-at-rosen-group/

[11] Brown, C. L. (2026). “Why Your Customer Centricity Transformation Keeps Failing—And What James Clear’s Atomic Habits Reveals About the Fix.” MarketCulture Blog, February 12, 2026. Available at: https://blog.marketculture.com/2026/02/12/why-your-customer-centricity-transformation-keeps-failing-and-what-james-clears-atomic-habits-reveals-about-the-fix/

[12] Brown, L., Brown, C. L. & Crichton-Browne, S. (2025). The Human Culture Imperative. MarketCulture Strategies. See also: Brown, L. & Brown, C. L. (2014). The Customer Culture Imperative. McGraw Hill.

As a Customer Experience (CX) professional, we are always looking for ways to improve the customer journey and our career growth. One of the most effective ways to do this is to apply Ethan Evans’s Magic Loop approach to your career development. Ethan Evans is a former VP at Amazon with extensive first hand experience of what it means to be customer-obsessed. His Magic Loop approach has helped numerous professionals achieve their career goals. I have adapted his model to add my take on it but of course all the credit goes to Ethan for developing this great model. You can see the full model laid out here. Let’s dive into how you can apply this approach to your own career development.

Step 1: Self-reflection – Are you doing the best job you can in your current CX role?

The first step to applying the Magic Loop approach is simple but critical: Self-reflection. Take a moment to reflect on your current position and assess your career goals, what you want to achieve, and where you want to be in the future. Analyze your CX strengths and weaknesses, which areas interest you, and what you would like to learn and improve. Are you strong in customer survey research? Great dealing with customers one on one? Able to build journey maps? Can you influence team or organisational culture to be more customer centric? Are you good at building CX business cases?
Make a list of actionable steps to achieve these goals and develop a plan with a practical timeline. The more specific and measurable the objectives, the better you will be able to increase your skills and improve your career growth.

Step 2: Experimentation – How can you help your manager?

The Magic Loop approach suggests that experimentation is essential to career development. Ask your manager what you can do to help, then do it. Be open to new experiences, take advantage of networking opportunities, and exit your comfort zone. Try and learn new things, take new courses, involve yourself in different projects, or volunteer to gain new skills. Engage leaders in other functions, gain a broader and deeper perspective of the business. Exploring new areas can help you see opportunities from different perspectives, allowing you to utilize various perspectives to solve problems more effectively.

Step 3: Make sure you implement

Whatever your manager asks, take it on, get it done. After this experimentation, evaluate your experience and knowledge. Reflect on the new skills you’ve learned and explore how they can be used to enhance your career development. Analyze whether the skills align with your career goals, the challenges you faced, what you enjoyed, and what you found challenging. Use this feedback to adapt your plan and determine what steps you need to take to continue your learning journey.

Step 4: Ask your manager how you can develop further so you can advance your CX career

The final step is to collaborate with your manager to advance. Use the evaluation feedback to improve your plan and reinforce your strengths. Identify what areas you want to improve and focus on learning new skills relevant to your career goal. Seek feedback from your manager, colleagues and other leaders, and identify gaps between your current skills and your career goal. Use this feedback to tailor your personal development plan so that it aligns with your career goals. This feedback loop mirrors the one that many of you are responsible for in your organizations, the customer feedback loop.

Step 5: Do as they recommend to improve your value.

Again make sure you take on their recommendations and stretch yourself to demonstrate your value as a CX pro, take on a new challenge that will grow your competency and contribute to the business.

Step 6: Advanced Magic Loop. Suggest your own ideas!!!

Ethan recommends for those who are more advanced in their careers that they recommend ideas.

You could use language like this to suggest an idea, “I noticed that we are becoming a little too internally focused and sometimes we forget to get customer input. I’ve been thinking we could get the team’s input by running a simple customer-centric culture assessment to gather everyone’s view. Would that be helpful?”

The most successful CX professionals we know have taken on the challenge of influencing their organisations level of customer centric culture. What better way to have impact than to help change the environment within which your organization operates!

In fact as I am writing this post, there is a team of awesome CX professionals enrolled in the Masters of Customer Experience Management at Michigan State University currently undertaking their own projects to determine their organizations’ level of customer centricity.

For them it is a great way to get their senior leaders involved in a conversation while providing valuable data on how they benchmark versus the best and uncover the hidden factors that maybe stopping their organization’s growth.

Repeat this Magic Loop!

Conclusion:

Ethan Evan’s Magic Loop approach is a proven way to build your career. Its cycle is designed to encourage movement and improvement. As a Customer Experience (CX) professional seeking career development, using the Magic Loop approach can help you achieve your goals and reach your true potential. The Magic Loop approach will allow you to assess your current career position, encourage experimentation, assess your learning experience, and reinforce your new skills while closing the gaps.

So, take that first step today, and start experimenting, why not try the MRI Benchmark, a customer-centric culture assessment tool, and witness the magic of the loop approach unfold in your career!