Exploring AI Business Transformation Best Practices

A fundamental divide is emerging in the corporate landscape. On one side, many companies are essentially slapping AI onto their existing processes, achieving measurable efficiency gains while remaining locked within familiar frameworks. Conversely, a smaller, leading cohort leverages AI as a catalyst to reimagine what becomes possible.

Recent research from ServiceNow and Oxford Economics highlights the cost of this strategic misalignment. Surveying nearly 4,500 executives worldwide, they uncovered a counterintuitive finding: AI maturity declined across most organizations this year, with a nine-point drop in performance. Surprisingly, merely applying AI to existing operations has weakened companies’ strategic confidence in AI.

Yet within this landscape, a core group deemed AI Pacesetters achieved remarkably different outcomes, scoring nine points higher in maturity and reporting superior results across experience, efficiency, and innovation metrics. What drives the success of these organizations in harnessing AI?

The Strategic Integration Model: Lessons from an AI Pacesetter

The distinction between traditional and transformational AI approaches becomes evident in organizations like Orica. Their method reveals the strategic principles essential for successful AI adoption. Rachael Sandel, Orica’s Group Chief Information Officer, notes that their approach embeds AI strategy within their broader technology and innovation strategies.

This integration philosophy recognizes AI as an enabler rather than a separate initiative to be merely added on. While other companies often ask how AI can optimize individual processes, Orica poses a more profound question: how can AI unlock new capabilities and transform their operations to achieve broader goals?

This distinction is crucial—it separates tactical implementation from strategic transformation. Instead of pursuing isolated efficiency gains through bolt-on solutions, Orica established integrated capability platforms that amplify organizational potential across multiple dimensions.

Their governance structure exemplifies this systemic thinking. Orica’s AI Center of Excellence brings cross-functional teams together to prioritize AI initiatives and make critical decisions about time and resource allocation. An AI council addresses senior policy decisions and high-risk initiatives, ensuring that AI development aligns with broader strategic objectives rather than only departmental goals. This governance framework fosters accountability, turning disjointed AI experiments into coordinated strategic capability building.

Human-AI Collaboration as a Competitive Advantage

A key indicator of transformational thinking is how these forward-looking organizations approach workforce evolution. Rather than viewing AI primarily as a replacement mechanism, they see human-AI collaboration as a unique competitive advantage.

Sandel emphasizes the importance of hiring curious and adaptable individuals and providing training and upskilling opportunities for existing employees. Strategically, they implement a blend of top-down and bottom-up approaches, merging talent management with leadership adaptation to facilitate technology-driven changes.

This dual-track methodology addresses a critical oversight in most AI implementations: the assumption that technology adoption alone inspires transformation. Strategic leaders acknowledge that AI’s full potential only emerges when organizational capability evolves alongside technological advancements.

The implications extend beyond operational efficiency to fundamental questions about work design. Sandel notes, "We’re going to have teams where humans and agents work alongside each other," prompting important organizational considerations: How do you manage performance? What KPIs should you analyze? How can you implement effective feedback loops? These aren’t just operational issues but architectural challenges that necessitate rethinking core organizational assumptions.

From Operational Improvements to Business Transformation

Sandel’s long-term vision illustrates a trajectory from tactical gains to strategic reinvention. "In the future, I foresee widespread AI adoption across various business processes," she asserts. “AI agents will perform tasks alongside humans across platforms and data sources, collaborating with other agents and humans.”

This forward-looking perspective surpasses mere process automation, encapsulating what Sandel calls "a fundamental shift in skills," transforming how work is designed and what future management roles will entail.

The competitive implications grow increasingly evident when examining the economic modeling behind transformational AI. Research indicates that if all Forbes Global 2000 companies achieved advanced AI integration levels, it could yield an additional $113 billion in gross margins across the Fortune 2000.

This figure surpasses simple efficiency gains, signaling the magnitude of strategic advantage available to organizations deftly navigating AI-driven business model evolution instead of just AI-enabled process optimization.

The Strategic Imperative: Three Critical Principles

The central challenge for executives isn’t primarily technical but conceptual. Most AI strategies focus on incremental improvements that, while yielding tangible efficiency gains, ultimately constrain organizations within existing operational frameworks.

Strategic AI integration requires an alternate approach anchored in three critical principles:

1. Strategic Integration Over Tactical Addition: Rather than tacking AI onto current processes, organizations should incorporate AI capabilities into broader strategic objectives to unlock new value creation opportunities.

2. Capability Architecture Over Technology Implementation: Build vibrant organizational systems and a culture that fosters human-AI collaboration as a competitive advantage, reframing the perception of AI as a replacement tool.

3. Future-Oriented Design Over Current-State Optimization: Construct organizational structures that capitalize on AI’s transformational potential, avoiding constraints imposed by existing operational limitations.

Moving Beyond Optimization Toward Reinvention

Recent research underscores a growing divide between organizations using AI merely to refine existing operations and those leveraging AI to fundamentally rethink their possibilities. This distinction will heavily influence competitive positioning as AI capabilities continue to evolve.

The future demands integrated approaches that view AI as a strategic ally in business model evolution. The true value of AI resides not simply in enhancing current efficiencies but in enabling capabilities that were previously unimaginable.

For strategic leaders, a pressing question arises: Are you merely refining existing processes, or are you designing a business model that embraces the possibilities that AI capabilities present?

This essential divide—between incremental optimization and bold reimagination—will ultimately differentiate organizations that lead in innovation from those that become hindered by their pursuit of operational perfection. What side of this critical shift will your organization occupy?