The AI-first transformation imperative

By Nicolas Teisseyre, Marie Lê de Narp and Yaroslav Stetsenko

The era of AI experimentation is over. What began as isolated pilot projects and proof-of-concepts has evolved into a fundamental restructuring of how businesses operate, compete, and create value. Organizations across industries are now grappling with a critical question: How do you move from scattered AI initiatives to enterprise-wide transformation that delivers sustained competitive advantage?

Our analysis of leading AI transformations reveals a clear pattern: Success belongs to organizations that treat AI not as a technology overlay, but as a catalyst for reimagining their entire operating model. However, the path to transformation varies significantly. While some organizations pursue comprehensive AI-first strategies, many large corporates — constrained by legacy IT systems and fragmented data orchestration — find end-to-end transformation complex with limited immediate ROI. These organizations often prioritize a smaller number of high-impact, well-scoped use cases that deliver measurable value without requiring wholesale organizational overhaul.

The shift from automation to augmentation

Traditional approaches to AI implementation focused on automating existing processes — replacing human tasks with algorithms to drive efficiency gains in deterministic, relatively simple workflows. While these initiatives often delivered immediate cost savings, they rarely transformed the business fundamentally.

Today's leading organizations are pursuing a different path. They're deploying agentic AI systems — autonomous agents capable of executing complex, end-to-end workflows while collaborating dynamically with human experts. Unlike traditional automation suited for simple, deterministic processes, agentic AI excels in complex, adaptive, and non-deterministic environments where real-time decision-making and contextual understanding are essential. This shift represents a move from simple automation to intelligent augmentation, where AI doesn't just replace human activities but amplifies human capabilities and enables entirely new forms of value creation.

Consider the evolution we're witnessing in business process outsourcing. Traditional BPO providers are rapidly transitioning from labor arbitrage models to AI-powered operations platforms. They're not just automating call center interactions; they're creating intelligent ecosystems that combine data curation, model monitoring, and human-in-the-loop services to deliver outcomes their clients couldn't achieve independently.

The transformation maturity framework: From failure to advantage

Our research identifies a maturity progression that distinguishes successful AI transformations from failed initiatives. Rather than viewing these as independent pillars, leading organizations understand the causal relationships and sequential dependencies between capabilities. The framework progresses from foundational requirements to advanced competitive capabilities:

1. Strategic clarity before technology deployment

Organizations that achieve transformational impact begin with clear business objectives and work backward to technology choices. They prioritize use cases based on business value, feasibility, and organizational readiness—not technical feasibility alone. Missing this foundation leads to scattered pilots that never scale.

2. End-to-end process redesign

Rather than automating existing workflows, leaders redesign entire processes around AI capabilities. This approach delivers exponentially greater value than point solutions. Without proper process redesign, organizations risk automating inefficient or dysfunctional workflows — embedding problems rather than solving them.

3. Data strategy driven by use cases

Leading organizations avoid the "build it and they will come" approach to data infrastructure. Instead, they implement data strategies pulled by specific AI applications, ensuring every data investment drives measurable business value. Poor data quality and accessibility remain the primary bottleneck preventing AI initiatives from scaling — no amount of sophisticated algorithms can compensate for inadequate data foundations.

4. Governance as competitive advantage

In an era of increasing regulatory scrutiny, robust AI governance frameworks become differentiators. Organizations with strong governance can move faster and take greater risks because they have systems to manage those risks effectively. Systematic underinvestment in governance frameworks leads to scaling failures as compliance concerns halt deployment.

5. Human-AI symbiosis in operating models

The most successful implementations create new hybrid roles where humans and AI systems collaborate seamlessly. This requires rethinking job designs, performance metrics, and organizational structures. Failure to invest adequately in change management — often requiring more resources than the technology itself — results in user resistance and low adoption rates.

6. Partnership ecosystem orchestration

No organization can build all necessary AI capabilities internally. Success requires orchestrating complex ecosystems of technology partners while avoiding vendor lock-in. Organizations must continuously reassess make-or-buy strategies to maintain strategic control while accessing best-in-class capabilities.

7. Continuous value measurement and optimization

Transformational AI initiatives implement sophisticated measurement systems that track both technical performance and business impact. For agentic AI systems specifically, key metrics include:

• End-to-end task completion rate – measures true autonomous capability
• Share of workflows completed without human escalation – indicates real-world usability and trust
• Output reliability and trustworthiness – tracks consistency and quality
• Frequency of unsupported or incorrect statements – identifies areas requiring improvement
• Alignment between confidence levels and actual correctness – ensures the system knows what it knows

Without robust evaluation frameworks that enable continuous learning and reuse, organizations fail to capitalize on their AI investments beyond initial deployments.

8. New commercial models aligned with value creation

As AI changes how value is created and delivered, organizations are pioneering new pricing and service models—moving from transaction-based to outcome-based commercial relationships. This represents a lagging indicator of transformation maturity, emerging only after organizations have mastered the foundational and enablement capabilities.

The implementation reality

Despite the promise, most AI transformations struggle to deliver sustained business impact. Our analysis suggests this failure stems from three systematic underinvestments:

1. Change management and organizational readiness – Organizations consistently underestimate the cultural and behavioral changes required for AI adoption.
2. Evaluation and measurement frameworks – Without sophisticated systems to measure both technical performance and business impact, organizations cannot demonstrate value or drive continuous improvement.
3. Knowledge reuse and scaling mechanisms – Failure to build systems that capture learnings and enable reuse across use cases prevents organizations from achieving economies of scale.

The organizations achieving breakthrough results approach AI transformation as a multi-year journey requiring fundamental changes in culture, skills, processes, and governance. They invest heavily in change management — often more than in the technology itself — recognizing that the human dimension of transformation is typically the most challenging.

These leaders also maintain a portfolio approach to AI initiatives, balancing quick wins that build momentum with longer-term transformational projects that reshape competitive dynamics. They understand that sustainable AI advantage comes not from any single algorithm or application, but from building organizational capabilities that enable continuous innovation and adaptation.

Looking ahead: The next phase

As AI technology continues to evolve at unprecedented speed, the window for building competitive advantage through AI transformation is narrowing. Organizations that delay comprehensive transformation risk finding themselves permanently disadvantaged against competitors who have built AI-native operating models.

The next phase of AI transformation will be characterized by even greater integration between human and artificial intelligence, more sophisticated autonomous systems, and new forms of value creation that we can barely imagine today. Organizations that build the foundational capabilities now — the governance, culture, processes, and partnerships — will be positioned to capitalize on these advances as they emerge.

The question facing leaders today is not whether to pursue AI transformation, but how quickly and effectively they can execute it. Understanding the maturity progression, recognizing the sequential dependencies between capabilities, and investing in systematically overlooked areas will determine which organizations successfully navigate this transformation.

In a world where AI capability increasingly determines competitive advantage, the cost of moving slowly — or building on weak foundations — may be existential.

For organizations ready to accelerate their AI transformation journey, Roland Berger combines deep industry expertise with proven transformation capabilities to help clients achieve breakthrough results. Our AI-first approach ensures that technology investments deliver sustained business value while building capabilities for continuous innovation.