AI Won't Fix Broken Systems—Digital Maturity Will

The companies dominating AI are the ones who invested in digital maturity first.

AI is everywhere. Every trend report, leadership meeting, and strategic plan now involves an AI strategy. But adopting AI too soon may do more harm than good. In a recent Wall Street Journal article, Professor Joe Peppard argues that most companies aren’t actually ready for an AI strategy because their underlying systems and data cannot support one (Peppard, 2025). After studying his perspective through the lens of digital marketing and healthcare operations, I find his argument not only convincing but urgently relevant.

AI can amplify strengths, but it also amplifies weaknesses. If a company’s data is fragmented, its systems are outdated, or its teams are overwhelmed, no algorithm will magically turn things around. Before organizations chase the latest AI tool, they need to build the digital maturity that makes AI workable in the first place.

Why AI Falls Apart in Unprepared Organizations

Many AI pilots fail not because the models are flawed, but because the environment supporting them is unstable. As Peppard (2025) emphasizes, companies routinely struggle with inconsistent data, legacy systems, and siloed processes, conditions that AI cannot compensate for. Instead of solving operational problems, AI often exposes them.

Recent studies reinforce this reality. Gartner (Edjlali, 2025) predicts that 60% of AI projects will be abandoned by 2026 due to poor data quality or inadequate readiness. McKinsey & Company (Mayer et al., 2025) similarly reports that while nearly all organizations invest in AI, only about 1% of leaders consider their companies truly mature in their deployment capabilities.

This foundational unpreparedness isn’t theoretical; it shows up in real-world settings.

Consider a healthcare system adopting AI to forecast patient volumes. If scheduling platforms don’t integrate, if the historical data is incomplete, or if staff aren’t trained to interpret AI outputs, the predictions will be inconsistent or ignored. The AI didn’t fail. The system around it wasn’t ready.

In other words:

It’s rarely AI that fails.

It’s the environment around it.

When leaders push AI adoption prematurely, the technology simply highlights the bottlenecks, misalignments, and inefficiencies that were already undermining performance.

The Architecture of Digital Maturity

So if AI alone can’t drive transformation, what should organizations prioritize first?

The answer is building digital maturity across three essential areas.

1. Data Quality and Integration

Reliable AI depends on reliable data. Clean, structured, well-governed data is the bedrock of every model. That means integrating data sources, eliminating inconsistencies, establishing governance, and capturing richer historical information. Without these steps, machine learning outputs cannot be trusted, let alone scaled.

2. Modernized Systems and Reduced Technical Debt

AI cannot thrive on top of outdated digital infrastructure. Whether in supply chain operations, healthcare workflows, or marketing analytics, legacy systems introduce friction that disrupts real-time automation and analytics. Modernization, often long overdue, is non-negotiable.

3. Workforce and Cultural Readiness

Technology alone cannot drive change. As Kyndryl (2025) reports, over 70% of organizations say their employees aren’t prepared for AI-driven workflows. Training, responsible-use guardrails, and clear communication are critical for aligning teams with new capabilities. Without cultural readiness, even the best tools gather dust.

Digital maturity doesn’t just make AI possible—it makes AI sustainable.

What Companies Gain When They Slow Down

Organizations that build digital maturity before adopting AI unlock entirely different outcomes than those that rush. Once data is trustworthy, systems are integrated, and teams are trained, AI becomes a scalable enhancement rather than a disruption.

With strong foundations, AI can:

• improve personalization and campaign performance in digital marketing

• support faster, more confident decision-making

• forecast demand with higher accuracy

• automate repetitive or error-prone tasks

• reveal insights buried in complex data ecosystems

Research reinforces this dynamic. MIT Technology Review Insights (Bandhakavi, 2024) found that companies with robust data ecosystems are more than twice as likely to achieve measurable value from their AI investments. Their advantage doesn’t come from adopting AI early but from embracing it correctly.

This is why moving deliberately often outperforms moving fast. Organizations that resist the pressure to chase AI prematurely avoid costly missteps and failed pilots that drain budgets and erode internal trust. By prioritizing digital maturity first, they position themselves to deploy AI that actually works, scales, and delivers sustainable results.

In the long term, the real competitive edge doesn’t belong to companies that adopt AI first. It belongs to those who build the strongest foundation for AI to succeed.

AI Isn't a Lifeboat—It's a Spotlight

AI isn’t a shortcut to transformation. It’s a mirror that reflects the strength or weakness of the systems it sits atop. When organizations try to implement AI without addressing underlying issues, the technology exposes exactly where processes break down, where data is unreliable, and where teams lack support. The outcome isn’t innovation but frustration.

But when the groundwork is strong, AI becomes a natural extension of the organization’s capabilities. It enhances decision-making instead of overwhelming it. It strengthens workflows instead of complicating them. It scales because the environment around it is built to support that scale. Companies that commit to digital maturity first will adopt AI effectively and sustainably.

The organizations that thrive with AI won’t be the ones that rush to implement it; they’ll be the ones that invest in building the conditions where AI can succeed. They’ll modernize their systems, elevate their data quality, and prepare their people. And once those pieces are in place, AI will not feel like a leap forward; it will feel like a logical next step.

Until then, drafting an AI strategy is not the priority.

Building digital maturity is the strategy.

References

Bandhakavi, S. (2024, October). Survey reveals 78% of businesses unprepared for GenAI due to poor data foundations. MIT Technology Review Insights. https://www.techmonitor.ai/ai-and-automation/survey-reveals-78-of-businesses-unprepared-for-gen-ai-due-to-poor-data-foundations/

Edjlali, R. (2025, February 26). Lack of AI-ready data puts AI projects at risk [Press release]. Gartner. https://www.gartner.com/en/newsroom/press-releases/2025-02-26-lack-of-ai-ready-data-puts-ai-projects-at-risk

Kyndryl. (2025, May 15). AI workforce impact report: Why most businesses are not yet ready for AI. Kyndryl. https://www.kyndryl.com/us/en/about-us/news/2025/05/ai-workforce-impact-report

Mayer, H., Yee, L., Chui, M., & Roberts, R. (2025). Superagency in the workplace: Empowering people to unlock AI’s full potential at work. McKinsey & Company. https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work

Peppard, J. (2025, March 15). Why most companies shouldn’t have an AI strategy. The Wall Street Journal. https://www.wsj.com/business/c-suite/ai-strategy-mistakes-5db90efadb90efa