In the race to become AI-first, many organizations find themselves in an unexpected stall – neither early-stage nor truly transformative. This opinion piece explores what’s often referred to as the “squeezed middle” of data maturity: a state where tools are in place, dashboards are abundant, yet meaningful value remains elusive. Somewhere between data awareness and true transformation lies a space where progress slows, ambitions stretch thin, and clarity fades.

Drawing inspiration from Maslow’s hierarchy of needs, the piece presents a new framework to understand the stages of data maturity, surfaces the illusion of readiness, and outlines a practical roadmap to help enterprises move from insight to intelligent execution. For data and transformation leaders, it offers a timely lens and clear direction for advancing with purpose.

A new enlightenment for data

We are living through a renaissance of intelligence — not human, but artificial. Machines now learn, predict, and act — not merely based on code, but on vast swathes of data we generate, curate, and catalog. Yet, in this age of silicon intellect, many organizations find themselves suspended between aspiration and action.

Despite years of investments in data platforms, governance programs, and analytics dashboards, a striking number of enterprises remain stuck in a state of arrested development. They are neither data-poor nor data-elite. Instead, they occupy an uncomfortable middle — functional, compliant, even insightful — but far from transformational.

This is the squeezed middle of data maturity, where progress plateaus and ambition quietly erodes.

The anatomy of data maturity: from survival to self-actualization

To understand the predicament, we must reflect on the nature of maturity itself. Drawing inspiration from Maslow’s hierarchy of needs, we can map a parallel in the evolution of data maturity. Just as individuals progress from meeting basic needs to achieving self-actualization, organizations move through stages of data capability that reflect growing sophistication and value creation:

The journey from data awareness to AI self-actualization

• Accessible data (What happened?) — Survival: At this foundational level, organizations begin to collect and store data. The focus is on availability, basic reporting, and understanding past events. Data is disorganized and siloed, but the seeds of a data culture are sown. Example: A retail chain tracks point-of-sale transactions but lacks consolidated customer insights.
• Reliable data (Why did it happen?) — Assurance: The next level brings clarity. Data quality improves, governance structures emerge, and organizations start ensuring that data is accurate, trusted, and safe. Root cause analysis and compliance become central. Example: A bank ensures regulatory reporting accuracy but struggles to unify customer data across channels.
• Inceptive analytics (What will happen?) — Efficiency: Here, organizations start to adopt analytical tools and frameworks. Automation enters the picture, and business users gain access to insights that forecast future trends. Pipelines run smoothly. Reporting becomes standardized and self-service analytics takes hold. Example: A healthcare provider predicts appointment no-shows but hasn’t fully optimized care pathways.
• Strategic guidance (How should we respond?) — Insight: Organizations begin using data to inform strategy. With BI platforms, machine learning models, and real-time analytics, they start to receive recommendations — not just answers. Business decisions are increasingly influenced by algorithms and scenario modeling.
  Example: A logistics company dynamically reroutes deliveries based on predictive traffic analytics.
• Autonomous intelligence (Automatically make the best decision)  — Self-actualization: Few organizations reach this pinnacle. Here, AI and automation enable systems to act on insights with minimal human intervention. Personalization, intelligent workflows, and continuous learning define the enterprise. Data not only supports the strategy, but it also shapes and executes it. Example: A fintech platform dynamically adjusts credit limits in real-time using machine learning models.

The journey along the data maturity curve is not just technical; it is deeply philosophical. It challenges organizations to rethink not just how they operate, but why.

Important reality: Progress is not linear or uniform

It’s crucial to recognize that data maturity does not evolve uniformly across an organization. Different teams, functions, or regions may mature at different paces:

• A marketing team might already leverage predictive customer segmentation, while finance still relies on static spreadsheets.
• A supply chain division might automate routing decisions even as HR grapples with siloed employee data.

Maturity grows in pockets before it blooms enterprise-wide. Recognizing and nurturing these seeds of progress — even if scattered — is key to escaping the squeezed middle.

The squeezed middle: Shadows on the wall

Many organizations today dwell in the squeezed middle space where ambition to be AI-driven collides with the unresolved fundamentals of being truly data-driven.

They have dashboards, data lakes, and digital lingo. Yet, like Plato’s cave dwellers who mistake shadows for reality, these organizations confuse visibility with understanding. Metrics are watched but not questioned. Data is abundant, but insight remains elusive.

In this middle state, the presence of data tools creates an illusion of maturity. Movement is mistaken for momentum. Governance becomes a ritual. Reports affirm rather than illuminate. Strategy talks of AI while foundations remain brittle.

This is not a failure of technology; it is a failure of perception. To transcend the cave, organizations must recognize the illusion and turn toward the light: alignment, trust, interpretation, and intentional use of data.

AI demands more than just clean data — it demands courage. The courage to experiment, to fail, to question established truths. And most importantly, to cede control. This is a philosophical challenge, not just a technological one.

Out of the shadows: The inner work of becoming AI-ready

The leap from data-rich to AI-driven cannot be bought. It must be built—from within.

To go from good to great in the age of intelligent automation, organizations must undergo not just a digital shift, but a philosophical awakening:

• From control to empowerment: Governance must evolve from gatekeeping to enabling responsible experimentation. Empower your teams to question, explore, and adapt.
• From certainty to probabilistic thinking: AI doesn’t trade in absolutes. It deals in likelihoods. Organizations must grow comfortable with nuance, confidence intervals, and risk-managed decisions.
• From silos to stewardship: Maturity isn’t just technical — it’s relational. Break down psychological and organizational walls. Encourage shared ownership of data as a collective responsibility.

Culture — not code — will separate the merely digitized from the truly intelligent.

Navigating the squeezed middle: A practical roadmap for organizations

To rise above the squeezed middle and ascend toward AI maturity, organizations must embrace both intention and iteration. Progress demands more than investment; it demands leadership, alignment, and conscious design.

Here’s a pragmatic roadmap to move from being “comfortably numb” to becoming truly AI-ready:

1. Start small with purposeful proofs of concept (POCs)

Big transformations begin with small wins. Identify targeted business problems where data-driven approaches can deliver meaningful value quickly. Focus on POCs that connect analytics to strategic objectives, not just technical experiments.

Example: Pilot a customer churn prediction model within a single region before scaling enterprise-wide.

2. Engage and educate leaders at every level

Executive alignment is critical, but leadership must exist beyond the C-suite. Equip mid-level managers and frontline leaders with data literacy programs, AI fluency sessions, and decision-making frameworks grounded in insights.

Data maturity is as much about empowering leaders to ask better questions as it is about building better models.

3. Embed data into strategic planning forums

Data cannot be an afterthought or a reporting function — it must live at the heart of strategy formulation. Update planning processes to include data scientists, governance experts, and AI ethicists from the start, not post-fact.

Example: Embed predictive analytics teams into annual budgeting cycles, product development reviews, and risk assessments.

4. Expand governance to embrace ethics and accountability

Modern governance isn’t just about control — it’s about care. Evolve governance structures to ensure data use aligns with ethical standards, regulatory expectations, and social impact goals.

Example: Set up an AI Ethics Council to review model bias, decision transparency, and explainability across key algorithms.

5. Link every data initiative directly to business strategy

No more data for data’s sake. Every analytics project, every AI pilot, every governance enhancement must be explicitly tied to delivering on the business’s strategic ambitions.

Ask consistently: How does this data initiative move the needle on revenue growth, customer retention, operational efficiency, or innovation?

Final word: Moving with patience, acting with urgency

True maturity is not a straight line — it’s a spiral staircase, full of moments of reflection, acceleration, and course-correction. Celebrate small wins. Sustain cultural momentum. Stay anchored in purpose.

The squeezed middle is not a dead-end. It is a crucible, a pressure point where organizations that dare to lead with vision, soul, and strategy will forge their future. Between ambition and actualization, the middle holds its own alchemy — if organizations dare to reflect.

About the Authors:

Bhavna Mehta serves as Assistant Vice President – Data and Analytics at Cincinnati Children’s Hospital Medical Center, where she leads the Enterprise Data & Analytics and Population Health IT Strategy. With over a decade of progressive leadership at the institution, she oversees data governance, business intelligence, analytics, engineering, and Epic population health platforms. Mehta is recognized for her strategic vision and ability to foster a data-driven culture that drives innovation and improves patient outcomes. She has led enterprise-wide transformations, integrating data and technology to support clinical and operational excellence. Known for her execution-focused leadership, Mehta excels in managing multidisciplinary teams through change and delivering high-impact solutions. As a member of CDO Magazine’s Global Editorial Board, Mehta brings valuable insights into healthcare analytics, data strategy, and technology leadership.

Flavia Saldanha is a seasoned Modern Data Strategist, Architect, and Engineer with over 15 years of experience driving data innovation within the banking industry. She specializes in enterprise data strategy, cloud data architecture, and regulatory reporting, with a proven ability to lead digital and data transformations at scale. Saldanha’s leadership has consistently delivered improved operational efficiency, cost reduction, and enhanced risk management through modern data solutions. Adept in Agile and SAFe methodologies, Saldanha excels at bridging the gap between technical teams and executive stakeholders to drive strategic outcomes. She is recognized for fostering a data-driven culture and championing the integration of AI to support intelligent decision-making and long-term business value.