When a global consumer and retail enterprise’s CEO, COO, and CTO reviewed their quarterly transformation dashboard, progress looked solid. The cloud migration was almost complete, the global capability center (GCC) had expanded, and agentic AI pilots were improving demand forecasting and promotion planning. By every formal milestone, the program appeared to be on track.

But the business was telling a different story. Margins remained under pressure, inventory volatility persisted, teams hesitated to act on AI recommendations, and data still required manual reconciliation.

Nothing was visibly broken—and that’s precisely the problem. Decisions were improving locally but not at the enterprise level. Value was created in pockets but did not accumulate where it mattered most: margins, working capital, and overall enterprise performance.

Despite significant investments in AI, innovation, and GCCs, many consumer and retail enterprises still struggle to capture value. The problem is no longer a lack of transformation. It is that multiple transformations are happening at the same time, without being designed to work together.

Where Transformation Gets Disconnected

The disconnect was easier to absorb when businesses had more room for inefficiency. Years of cost pressure have changed that. Today even small misalignments can surface quickly as lost sales, excess inventory, or service issues.

And there are more points of connection to manage. Retailers are building data-driven media businesses, while consumer goods companies are investing more directly in customer relationships. Pricing affects promotions, promotions affect demand, demand affects supply, and decisions must increasingly move across planning, commercial, and finance.

Yet transformation has largely moved forward on separate tracks. GCCs have scaled. Platforms have modernized. AI is assisting decisions. Processes are being redesigned. Each can demonstrate progress on its own. The value gap emerges in the space between them.

When the business needs to respond quickly, data has to be revalidated, assumptions drift, and teams align in the moment. Nothing necessarily breaks. But the enterprise slows precisely when it needs to move as one.

Understanding Where the Enterprise Stands

The greatest risk is misjudging the enterprise’s readiness across data, processes, and operating models. If leaders get that wrong, they can end up scaling the fragmentation they are trying to fix.

Navigating complexity: For most enterprises, activity is high and progress looks real at the functional level. GCCs scale analytics and planning support, platforms modernize, and AI pilots show promise. But coordination still happens late, often through manual effort and escalation.

Accelerating: In some organizations, these gaps start to close. Responsibilities across data, process, and execution are clearer, and decisions move more smoothly across functions. The business starts to behave more like a system, but progress remains fragile.

Compounding: A smaller group reaches a different point. Data, processes, platforms, and AI reinforce one another through real operating cycles, allowing connected decisions to repeatedly deliver value.

Two situations change the starting point. Spin-offs and newly formed entities have a clean-sheet opportunity to design operating models, governance, and technology together. Established leaders with margin headroom and operational stability face a different risk: hesitation.

Knowing where the enterprise stands shapes the choices that come next.

Five steps to turn momentum into compounding value

Knowing where the enterprise stands only matters if it changes what leaders do next. Five actions can help turn transformation momentum into compounding value:

1. Establish enterprise-level ownership.

Most organizations can name leaders who own GCCs, platforms, AI, or supply-chain programs. But it’s often less clear who is responsible for bringing merchandising decisions, demand signals, and execution constraints together. A transformation office can make these dependencies visible earlier, helping leaders sequence decisions across planning, supply, commercial, and finance rather than relying on escalation and individual intervention.

2. Organize around end-to-end integrated processes.

Transformation often follows functional lines: Finance improves reporting, operations modernizes supply chains, and commercial teams invest in planning and pricing. The gaps appear when these efforts need to work together. Organizing around end-to-end processes such as connected planning and order to cash allows decisions, data, and accountability to move together when conditions change.

3. Tier the agentic ambition.

Agentic AI pilots can show promise but may become harder to sustain when data varies, responsibilities are unclear, and decisions still require manual validation. Organizations making more progress scale AI deliberately, starting where data is reliable and decision boundaries are clear, while operating model design, governance, and control mature alongside it.

4. Integrate data governance into the operating model.

When demand signals or trade assumptions differ, teams spend time reconciling inputs instead of making decisions. Leaders need clarity on who owns the data across planning, supply, commercial, and finance. Building governance into everyday decision making creates a more consistent view of demand, value, and performance.

5. Lead with value and use it to redesign the enterprise.

Programs can improve while enterprise outcomes remain unchanged. Rather than asking which programs to fund, leaders should focus on where value is created, how it moves across the enterprise, and where it drops off. Investments can then follow outcomes rather than initiatives, with programs accelerated, reshaped, or stopped as conditions change.

The era of transformation for its own sake is reaching its limit. Consumer and retail organizations have invested heavily in analytics, platforms, GCCs, and agentic AI, but capability alone will not deliver the return. The risk is mistaking activity for progress while the gaps across functions remain.

Closing this gap is a leadership decision, not a technical fix. It requires discipline around how data, decisions, and execution connect across the enterprise.

The decisions executives make next on GCC models, clean-sheet opportunities, and how agentic AI scales will be difficult to unwind. Those who act with intent now are positioned to turn transformation into sustained returns.

EY exists to build a better working world, helping create 
long-term value for clients, people, and society and build trust in the capital markets. Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform, and operate. Working across assurance, consulting, law, strategy, tax, and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today. Explore more sector insights here.

© 2026 EYGM Limited.

All Rights Reserved.

This material has been prepared for general informational purposes only and is not intended to be relied upon as accounting, tax, legal or other professional advice. Please refer to your advisors for specific advice.

ey.com