The statistics are sobering. A recent MIT study analyzing over 300 initiatives found that 95% of AI projects fail in enterprise organizations. Research by NTT DATA indicates that between 70-85% of GenAI deployment efforts are failing to meet their desired ROI. Gartner predicts that 30% of generative AI projects will be abandoned after proof of concept by the end of 2025, and that over 40% projects will be canceled by the end of 2027. Meanwhile, VentureBeat found that 87% of data science projects never make it to production.

These statistics represent billions in wasted investment and countless missed opportunities to improve customer experiences genuinely. But here’s what most analyses miss: the root cause isn’t technical complexity or data quality issues. It’s a fundamental misalignment between AI capabilities and customer needs.

The Technology-First Trap

Walk into any boardroom today, and you’ll hear variations of the same conversation: “Our competitors are using AI. We need an AI strategy. What can we automate?” This technology-first approach is precisely why most AI initiatives become expensive experiments that gather dust.

The problem is that organizations are asking the wrong questions. Instead of starting with “What can AI do?”, successful companies ask “What problems do our customers actually face, and might AI help solve them?”

This distinction matters more than you think. When you start with technology, you’re looking for problems to justify your solution. When you start with customer problems, you’re evaluating whether AI is even the right tool for the job.

The MAYBE Framework: A Customer-Centric AI Approach

After observing dozens of AI implementations, ranging from successful to unsuccessful, I developed a framework that keeps customers at the center of every decision.

The MAYBE Framework is built on a simple philosophy I have shared with companies for years: Customer First. Technology Last. AI Maybe.

Its purpose is to reset the order of priorities. Too often, businesses rush to adopt AI or market an “AI-First” organization, without considering the customer impact. Between the customer and the technology are numerous steps, including strategic goals, operational needs, and human factors, that must be addressed to create real value. Only then should AI enter the conversation, framed not as the starting point but as a possible tool. In many cases, other technologies or even process changes will solve the problem more effectively.

The question is not “How do we use AI?” but rather “Should we use AI at all?” That is the shift the MAYBE Framework drives: slowing down, focusing on what matters most, and only then asking if AI is the right solution.

Before introducing AI into any customer interaction or process, ask yourself if your solution passes the MAYBE test:

M — Meaningful: Does the use of AI improve a customer outcome THEY care about? Not what you think they should care about, but what they actually value. Customer satisfaction surveys and direct feedback reveal the gap between assumed and actual priorities.

A — Accountable: Does a HUMAN own the decision and the result? AI should augment human judgment, not replace human accountability. When things go wrong, and they will, customers need to know a real person is responsible for making things right.

Y — Yields Control: Can the customer opt out, switch channels, or reach a human at any point? Forced AI interactions breed frustration. Customers should feel empowered, not trapped by automation.

B — Beneficial: Would a reasonable customer feel respected by this experience? This is about dignity and respect in every interaction over cost savings and corporate-first metrics.

E — Explainable: Can you explain in plain words how the AI informed the action? If you can’t explain it simply to a customer, you probably shouldn’t be using AI for that decision.

If you can answer yes to all five questions, you likely have a solid use case for AI; if you can’t, pause and reconsider whether AI is the right solution.

Design Thinking for a Customer-Centric AI Strategy

The MAYBE framework aligns naturally with design thinking principles, which have proven successful in human-centered innovation for decades. Design thinking’s emphasis on empathy, problem definition, and iterative testing provides the perfect foundation for customer-centric AI implementation.

Empathize first, automate second. Design thinking begins with deep empathy for users—understanding their real frustrations, motivations, and contexts. This directly supports the “Meaningful” criterion in MAYBE. Before considering any AI solution, spend time observing customers in their natural environment. What actually slows them down? What causes genuine frustration? Often, you’ll discover that the most pressing problems aren’t technical at all.

Define the right problem. Design thinking’s “Define” phase forces you to synthesize observations into clear problem statements. This prevents the technology-first trap that dooms so many AI projects. Instead of asking “How can we use AI?” design thinking asks “What specific customer problem are we solving?” This problem definition serves as your North Star for evaluating whether AI truly adds value.

Prototype with humans before algorithms. Design thinking emphasizes rapid prototyping and testing with real users. Apply this to AI projects by first prototyping the desired experience with human representatives. This reveals whether your core concept addresses genuine customer needs, separate from any challenges related to AI implementation. If the human-powered prototype doesn’t create value, adding AI won’t fix the fundamental problem.

Test assumptions early and often. Design thinking’s iterative approach naturally incorporates the “Yields Control” and “Beneficial” elements of MAYBE. By testing with customers throughout development, you discover their comfort levels with automation and preferences for human fallbacks. This continuous feedback loop ensures your AI serves customers rather than imposing technology upon them.

The combination of design thinking methodology with the MAYBE framework creates a powerful approach: start with human empathy, define real problems, prototype solutions, and test assumptions—all while ensuring AI enhances rather than replaces human agency and dignity.

Beyond the Capability-Reality Gap

The second primary reason AI projects fail is the persistent misalignment between perceived AI capabilities and reality. Popular media and vendor marketing have created unrealistic expectations about what AI can achieve, leading to what researchers refer to as the “AI capability illusion.”

Current AI excels at pattern recognition, language processing, and specific prediction tasks. It struggles with common-sense reasoning, handling edge cases, and understanding context in the same way humans do. Yet many projects are designed as if AI possesses human-level rationale and judgment.

Successful AI implementations acknowledge these limitations upfront. They design systems that leverage AI’s strengths while maintaining human oversight for complex decisions. They plan for edge cases and build graceful degradation paths when AI confidence drops.

The Customer Problem-First Methodology

Here’s how leading organizations approach AI implementation:

1. Start with customer research. Before considering any technology, conduct fresh customer interviews. What frustrates them most about current processes? What outcomes matter most to them? Often, you’ll discover that the most pressing problems don’t require AI at all, they need better processes, more transparent communication, or simpler interfaces.
2. Map the customer journey. Identify specific moments where customer experience breaks down. These friction points become your AI opportunity assessment areas. But remember: not every friction point needs an AI solution.
3. Prototype with humans first. Before building AI systems, test your proposed improvements with human representatives. This reveals whether the core concept addresses real customer needs, separate from any AI implementation challenges.
4. Design for transparency. Customers increasingly expect to understand how automated decisions affect them. Build explanation capabilities from the beginning, not as an afterthought.
5. Plan your human handoffs. Define clear escalation paths and ensure your human representatives are equipped to handle cases where AI reaches its limits.

Real-World Success Stories

Companies that follow customer-centric AI approaches see dramatically different results. A major bank reduced customer service resolution time by 40% not by replacing human agents, but by giving them AI-powered tools to surface relevant customer history and suggest solutions quickly. Customers could always speak directly with agents, and agents remained fully accountable for outcomes.

An e-commerce company improved recommendation accuracy by 60% by focusing AI on understanding customer intent rather than purchase history. They invested heavily in explaining why items were recommended and made it easy for customers to refine suggestions. The AI served the customer relationship, not the other way around.

The Path Forward

The future belongs to organizations that view AI as a means to better serve customers, not as an end in itself. This requires a fundamental shift in how we evaluate AI opportunities, from “what’s technically possible” to “what would genuinely help our customers.”

Your product, services, and company must constantly be positioned to solve genuine pain points. This starts with the customer. AI should serve your product vision, not replace it. Human problems first, then determine what technology can help.

Not everything needs AI to solve the problem. Sometimes the best solution is a clearer website, a more straightforward process, or a more empathetic human interaction. But when AI is the right tool, when it passes the MAYBE test and addresses real customer needs, it can transform experiences in profound ways.

The question isn’t whether your company will use AI or how to implement it. It’s whether you’ll use it in the service of your customers or in the service of technology trends. The companies that choose customers will be the ones still standing when the AI hype cycle inevitably cools.

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Chris Hood is an AI strategist and author of the #1 Amazon Best Seller “Infailible” and “Customer Transformation,” and has been recognized as one of the Top 40 Global Gurus for Customer Experience.