Artificial intelligence (AI) has moved from experimental side projects to the centerpiece of organizational strategy. Leaders increasingly see AI not just as an IT initiative but as a long-term driver of competitiveness. Yet despite the enthusiasm, most organizations remain stuck in the pilot stage. They can showcase proofs of concept, but adoption rarely scales across the enterprise.

Consider a familiar scene: A CEO green lights an AI pilot with excitement. Six months later, the demo dazzles at a board meeting, but on the shop floor, no one uses it. The dashboards gather dust, the algorithms fade into obscurity, and the project team quietly disbands. For many executives, this cycle of “impressive pilot, invisible impact” has become the norm.

Part of the challenge is generational. We are among the first wave of practitioners tasked with embedding AI into organizations in ways that will shape how they function for decades to come. Executives are confronted with a paradox. On the one hand, they are bombarded with promises from technology vendors, each offering “turnkey” solutions that claim to automate entire functions overnight. On the other, they find themselves without a reliable roadmap for how to identify their specific organizational needs, sequence adoption, and embed AI without disrupting ongoing business. Firms recognize AI’s potential, but too often progress stalls in the messy middle.

This article addresses that “missing middle” of AI transformation; the gap between ambition and scaled impact. It introduces a field-tested, evidence-based framework, grounded in both research and practice, to help leaders move beyond pilots and systematically embed AI into the design across operations and strategy, turning it into a driver of measurable and sustainable value.

The Missing Middle Problem

Embedding AI into operations and decision-making represents a new class of organizational change. Unlike past technological adoptions such as ERP or CRM rollouts which followed structured playbooks and had defined end-states, AI initiatives are exploratory and continuously adaptive. They are not about “installing” a system or introducing a new tool, but about reconfiguring workflows, governance, and decision-making itself. A predictive algorithm cannot simply be switched on like a payroll module; it must be trained, trusted, and iterated alongside shifting business conditions.

Companies today are experimenting with a wide range of functions. In manufacturing, predictive maintenance models promise to reduce downtime. In finance, anomaly detection and forecasting tools seek to speed close cycles and improve accuracy. In retail, AI-powered demand forecasting guides replenishment and pricing.

And yet, despite the variety of use cases, the reasons for failure are strikingly similar. Organizations can demonstrate technical feasibility in pilots but struggle to translate them into enterprise-wide adoption. This is the “missing middle” of AI transformation: the space between initial success and scaled impact. Recognizing and addressing this missing middle is essential before launching any AI initiative.

Research Foundations Behind the Framework

Research and surveys consistently highlight the gap in AI scaling. The 2025 Deloitte CFO Survey reports that fewer than 40 percent of automation initiatives deliver measurable value¹. The McKinsey Global AI Survey found that only 30 percent of AI pilots transition to scaled impact². Similarly, the Institute of Management Accountants emphasizes that finance adoption remains fragile without proper governance and trust-building³. Peer-reviewed studies echo this: Erik Brynjolfsson and colleagues show that productivity gains materialize only when firms redesign workflows around digital tools⁴. Meanwhile, a study by Iris Raisch and Simone Krakowski underscores that the critical enabler is not technical capability, but the intersection of organizational design and human-AI collaboration⁵.

These findings resonate with practice. In our work with multinational firms, the difference between success and failure rarely hinged on the algorithm itself. Instead, firms that scaled AI effectively shared three organizational traits:

• They diagnosed their needs with clarity, rather than chasing shiny use cases.
• They embedded governance and accountability early, ensuring trust in both data and models.
• They redesigned processes for scalability, rather than treating AI as a bolt-on experiment.

This alignment between practice and research forms the foundation for the five-stage framework outlined in this article. The framework bridges the “missing middle” by offering a practical roadmap for diagnosing needs, embedding governance, redesigning processes, building organizational literacy, and scaling adoption iteratively. It is designed to be both evidence-based and field-tested, making it useful for leaders, scholars, and students seeking to move AI from ambition to sustainable enterprise value.

The Five-Stage Framework for AI Transformation

Scaling AI requires more than deploying algorithms. It demands that organizations deliberately progress through interdependent stages that build clarity, trust, and scalability. The five stages outlined here offer leaders a practical roadmap for moving beyond pilots and embedding AI into the fabric of decision-making.

1. Diagnose and Align

AI initiatives often begin with enthusiasm but limited focus. Too many pilots are launched because the technology looks exciting, not because it solves a meaningful business problem. The real challenge is not just spotting pain points but determining whether they are the right ones to tackle first, whether AI will address a true business issue or merely automate an inefficient process.

• Intent: Success begins with precise problem framing. Effective initiatives are anchored in a well-defined statement of the issue at hand. This includes mapping decision flows, identifying where delays or inefficiencies create business risk, and establishing alignment on not only the “what” (the solution) and “why” (the value), but also the “who” it serves and “how” it shapes decision-making.
• Method: Research and practice consistently show that structured approaches like Six Sigma’s DMAIC framework, project charters, and purpose-chaining mechanisms help trace the flow from idea to impact. These methods facilitate early cross-functional alignment and critically validate assumptions, factors consistently emphasized in organizational change literature⁶.
• Result: The outcome of this stage is a prioritized set of business problems articulated with clarity, linked explicitly to measurable outcomes, and backed by a coalition of stakeholders aligned on implementation.

2. Governance and Accountability

Trust is the currency of adoption. Even sophisticated algorithms falter when employees doubt the integrity of the data or the reliability of model outputs. Effective scaling therefore depends less on technical capability than on the establishment of governance structures that clarify ownership, codify standards, and make accountability visible.

• Intent: The objective of this stage is to embed clear lines of responsibility and oversight into the way AI is developed and used. Governance involves defining ownership of data pipelines from source to consumption, setting quality standards, and ensuring that model training, monitoring, and application are explicitly tied to business outcomes through measurable KPIs.
• Method: Evidence from both practice and research highlights the importance of mapping data flows end-to-end to reveal points of vulnerability or ambiguity². Responsibility should be distributed through explicit assignments, for instance, data stewardship within finance, model validation within IT, and decision oversight through cross-functional councils. Mechanisms such as audit dashboards, review committees, and performance scorecards make accountability visible and governance transparent across the organization⁷.
• Result: The outcome will be a governance framework that specifies ownership, defines how data reliability is monitored, and establishes escalation protocols for performance issues. With governance in place, employees are more likely to trust AI outputs, leaders can directly connect initiatives to measurable outcomes, and adoption begins to move beyond pilots into embedded daily decision-making.

3. Redesign for Scalability

AI that works in one pocket of the business often fails when extended across the enterprise. Scaling requires rethinking workflows, incentives, and integration with existing systems. Point solutions cannot simply be “copied and pasted” across functions; success depends on redesigning processes so they can operate consistently, reliably, and on a scale.

• Intent: The purpose of this stage is to ensure that AI does not remain a localized experiment but becomes integrated into enterprise-wide processes. Redesign involves shifting workflows, clarifying decision rights, and realigning incentives so that AI adoption is not peripheral but core to “how the business runs.”
• Method: Research highlights that productivity gains from digital technologies emerge only when organizations reconfigure their workflows to accommodate them⁸. In practice, this often involves investments in shared data platforms such as warehouses, data marts, or lake houses, that eliminate duplication and provide a single source of truth. AI should embed directly into standard operating procedures, replacing them rather than layering on top of manual processes. Incentive structures and performance metrics are also recalibrated to reward collaboration and reinforce adoption across units.
• Result: The outcome is a set of enterprise-ready workflows where AI-enabled processes are standardized, repeatable, and trusted. Adoption is no longer fragmented across pilots but supported by incentives, governance, and integration into day-to-day operations, making scalability both achievable and sustainable.

4. Reuse and Build Data Literacy

AI solutions that succeed in a single function often falter when extended across the enterprise. Scaling requires more than replicating a point solution; it demands rethinking workflows, decision rights, and incentives so that AI is embedded in the fabric of daily operations. Point solutions cannot simply be “copied and pasted” across contexts. Sustainable impact comes from redesigning processes so they can operate consistently, reliably, and on a scale.

• Intent: The purpose of this stage is to ensure that AI does not remain a localized experiment but becomes integrated into enterprise-wide processes. Redesign involves shifting workflows, clarifying decision rights, and realigning incentives so that AI adoption is not peripheral but core to “how the business runs.”
• Method: Research highlights that productivity gains from digital technologies emerge only when organizations reconfigure their workflows to accommodate them⁹. In practice, this often involves investments in shared data platforms such as data warehouses, data marts, or data lakes, that eliminate duplication and provide a single source of truth. AI should be embedded directly into the standard operating procedures of process workflows, replacing rather than layering on top of manual processes. Incentive structures and performance metrics are also recalibrated to reward collaboration and reinforce adoption across units.
• Result: The outcome of this step will be a set of enterprise-ready workflows where AI-enabled processes are standardized, repeatable, and trusted. Adoption is no longer fragmented across pilots but supported by incentives, governance, and integration into day-to-day operations, making scalability both achievable and sustainable.

5. Start Small, Iterate, Scale

Large-scale transformations rarely succeed when attempted in a single leap. Pilots remain essential, but their value depends on being positioned as stepping-stones toward broader adoption rather than as endpoints. When framed correctly, they reduce risk, validate assumptions, and create momentum for enterprise-scale change.

• Intent: The goal of this stage is to use pilots not simply to prove technical feasibility but to test organizational readiness, identify risks, and demonstrate business value in a controlled way that can inform scaling.
• Method: Evidence from the McKinsey Global AI Survey shows that organizations with the highest ROI from AI treat pilots as minimum viable transformations (MVTs): experiments designed from the outset to be scaled². In practice, this means defining explicit learning objectives, linking pilots to measurable business outcomes, and embedding feedback loops that enable rapid iteration. Equally important is articulating in advance how insights from the pilot, whether successes, risks, or lessons learned will shape the roadmap for enterprise rollout.
• Result: The outcome is not just a “successful pilot,” but a validated use case with tangible business metrics and a sequenced plan for expansion. Leaders gain organizational confidence, stakeholders see evidence of value, and adoption shifts from fragmented experimentation to systemic transformation.

Figure 1: Five-Stage Framework for AI Transformation.

Pulling the Framework Together

The five stages shown in Figure 1 are not rigid steps to be checked off in sequence, but interconnected elements that reinforce one another. A strong diagnosis without governance results in stalled adoption; governance without redesign becomes bureaucracy; iteration without reuse wastes resources. The framework is best understood as continuous progression, where lessons at each stage inform the next, and feedback from later stages strengthens earlier ones. The horizontal arrow underscores this ongoing cycle of adoption and scaling progress is forward-moving, but refinement is constant.

Case Evidence: Automating Financial Close with AI

A global manufacturing company struggled with recurring delays in its monthly financial close, often taking nearly two weeks to reconcile accounts and resolve mismatched entries. Beyond missed deadlines, the delays created real financial risk: revenue recognition was postponed, cash forecasts were unreliable, and investor calls relied on provisional numbers. The CFO estimated working capital misstatements of nearly $50M each quarter, alongside higher audit costs from extensive year-end adjustments.

The company first tried to solve the problem by purchasing an off-the-shelf close automation tool. While it delivered dashboards and some automation, the underlying issues: late error detection, inconsistent reconciliations, and low trust in output remained unresolved.

It was at this point that the leadership team was introduced to our five-stage framework. Instead of starting with software, leaders diagnosed root causes, established governance, and redesigned workflows before layering in targeted tools. Each new role assignment, technology purchase, or process change was tied to a defined intent and metric. This gave teams a structured playbook to realign when challenges emerged and ensured improvements scaled across plants rather than staying siloed.

Implementation:

Step 1: Diagnosis

Finance leadership dissected the close cycle and found the bottleneck: over 70% of delays came from late error detection in intercompany reconciliations and manual postings. Errors surfaced only after consolidation, forcing costly rework. The problem was framed as a strategic question: How can we catch reconciliation and posting errors earlier so close timelines shrink, revenue recognition is accurate, and rework costs fall?

Step 2: Governance

A cross-functional steering group was created, including regional controllers, corporate accounting, and IT. This body established:

• Finance retained ownership of validation and reconciliations, while IT managed ingestion into the enterprise data warehouse.
• A governance charter was drafted specifying thresholds for anomaly detection, rules for escalating exceptions, and responsibilities for sign-off.
• Close-cycle KPIs (days to close, % of entries flagged, rework hours) were embedded in Power BI dashboards accessible to leadership.

This ensured governance wasn’t abstract, it was visible, measurable, and enforced.

Step 3: Redesign

Rather than layering AI on top of legacy reconciliations, the company re-architected workflows around the enterprise data warehouse.

• All journal entries were funneled into the warehouse before consolidation, where anomaly detection models flagged high-risk entries in near real time.
• Alerts were routed to controllers via workflow tools integrated with their ERP, meaning errors were resolved in-stream rather than discovered weeks later.

This shift reconfigured the close process from reactive firefighting to proactive exception management, making the workflows enterprise-ready instead of plant-specific.

Step 4: Reuse and Literacy

To avoid “reinventing the wheel” across plants:

• A shared data dictionary was built to standardize reconciliation definitions (e.g., what counts as “open” vs. “cleared” entries).
• A feature store was created inside the warehouse, storing reusable model features such as anomaly thresholds, reconciliation patterns, and journal templates. This allowed new plants to deploy anomaly detection without rebuilding models.
• Controllers were trained in “AI literacy” workshops to interpret flagged exceptions and feed outcomes back into the models.

This not only reduced redundancy but also gave finance staff confidence in how the AI was making decisions.

Step 5: Iterate and Scale

Once proven effective, the scope expanded to journal entry validation and accrual postings, embedding AI-driven checks earlier in the close cycle. Each stage of rollout was structured as a “minimum viable transformation”: test in one division, refine based on feedback, and standardize in the enterprise data warehouse before extending to the next function or plant. By year-end, the redesigned workflows were operational in five plants, providing a blueprint for company-wide adoption.

Strategic Impact:

By the end of the first year after implementation:

• Close timelines dropped from 12 days to 6
• Manual adjustments fell by 40%
• Audit fees decreased by 15% due to fewer late corrections

Table 1: DGRI framework

Beyond the numbers in Table 1, the company demonstrated that AI could be embedded into finance’s most sensitive process (the month-end close) without destabilizing operations. What began as a targeted fix for reconciliations evolved into a repeatable playbook for scaling AI in core financial processes, strengthening both accuracy and leadership confidence. Importantly, the same five-step DGRI framework is transferable to domains such as HR (workforce planning), procurement (vendor risk scoring), and marketing (campaign optimization). Finance served as the test bed, but the architecture and governance structures established a foundation for enterprise-wide AI adoption.

Implications and Path Forward

AI adoption in corporate finance is not a technology rollout; it is an organizational transformation touching strategy, governance, process design, and culture. Each stage of the framework diagnosis, governance, redesign, reuse, and iteration, represents a critical lever. On its own, each stage is complex and prone to derailment. Taken together, they form a system that allows leaders to see where progress stalls and where intervention creates the most value.

For leaders and executives, success requires more than board sponsorship; it demands a domain technology leader (or digital steward) who can bridge strategy, operations, and execution. This role ensures AI initiatives remain tied to business priorities and measured by outcomes, not activity. For managers, the framework provides a repeatable playbook that avoids one-off pilots and instead builds reusable assets, governance structures, and data literacy. For scholars, it offers a model to study why digital adoption falters, and which organizational conditions enable scale.

The broader implication is that sustainable AI transformation is less about algorithms and more about organizations. Some companies may need to reinforce governance; others may need to re-architect workflows or invest in reuse. The framework is not a universal sequence but a guide to diagnosing gaps and bridging them with discipline and intent.

Artificial intelligence will not reshape organizations simply because models improve. It will do so when leaders embed accountability, trust, and scalability into decision-making. The five-stage framework provides a roadmap for doing exactly that, helping firms move beyond fragmented experiments to build AI-enabled organizations capable of sustained enterprise value.

References

1. Deloitte. “CFO Survey 2025.” Deloitte Insights, (2025).
2. McKinsey & Company. “The State of AI: Global Survey.” (2023).
3. Institute of Management Accountants. “The Impact of Artificial Intelligence on Accounting and Finance.” (2024).
4. Erik Brynjolfsson and Georgios Petropoulos. “The Power of Prediction: Predictive Analytics, Workplace Complements, and Business Performance,” Workplace Complements, (2021).
5. Iris Raisch and Simone Krakowski. “Human-AI Collaboration and Organizational Design,” Journal of Management Studies 58, no. 6 (2021).
6. Michael Harry and Richard Schroeder. “Six Sigma: The Breakthrough Management Strategy.” New York, NY: Currency (2000); Project Management Institute. “A Guide to the Project Management Body of Knowledge” (PMBOK Guide), 7th ed. (Newtown Square, PA: PMI, 2021).
7. Iris Raisch and Simone Krakowski. “Human–AI Collaboration and Organizational Design.” Journal of Management Studies 58, no. 6 (2021).
8. Erik Brynjolfsson and Kristina McElheran. “The Rapid Adoption of Data-Driven Decision-Making.” Management Science 67, no. 5 (2021): 1–21.
9. Erik Brynjolfsson and Georgios Petropoulos. “The Power of Prediction: Predictive Analytics, Workplace Complements, and Business Performance.” Workplace Complements, June 2021.