How to Scale AI Across the Enterprise: Practical Guide

Feb 17, 2026

Artificial Intelligence (AI) is no longer confined to the realms of futuristic speculation or niche applications. It has become a critical component of enterprise transformation. However, while many CEOs and boards frequently mention AI in earnings reports and strategic announcements, the real challenge lies in scaling AI across the enterprise effectively. As discussed in a high-level panel featuring industry leaders, this journey requires structured planning, cultural evolution, and a focus on measurable value creation. This article explores the pivotal insights shared during the discussion, tailored for decision-makers aiming to navigate the complexities of scaling AI.

The Gap Between AI Aspirations and Execution

"The focus is no longer on AI’s potential - it’s on value creation," noted Kumar Krishnamorti, technology strategist at PwC. Despite the increasing mentions of AI in corporate strategies, actual capital expenditures in AI initiatives remain flat across most industries, with exceptions in high-tech and energy sectors. This disparity highlights a critical disconnect: while enthusiasm for AI is high, many organizations struggle to translate AI’s promise into scalable, impactful outcomes.

The central question is how to bridge this gap. Success hinges on moving beyond "use-case purgatory" and adopting integrated, orchestrated portfolio choices. Enterprises must connect AI initiatives to tangible business outcomes - whether it’s margin growth, customer retention, or revenue expansion - while navigating an ever-changing technological frontier.

Key Considerations for Scaling AI

The panel outlined three major factors that determine the success or failure of AI projects within enterprises. Here’s a deeper dive into these areas:

1. Data Readiness: The Foundation of AI

Sanjay Sriata, Chief Digital Strategy Officer at Genpact, emphasized that data is a foundational asset - and a frequent stumbling block. Many companies lack clean, well-organized, and accessible data environments, which makes it nearly impossible to leverage AI effectively.

According to Sriata:

Companies should avoid launching AI initiatives unless data hygiene is addressed first.
Orchestrating AI around well-structured data can create a unique competitive advantage, even when utilizing widely available models.

Actionable Insight: Before initiating AI projects, conduct a comprehensive audit of your organization’s data quality, governance processes, and accessibility.

2. Operating Model Redesign

Implementing AI doesn’t just automate existing processes; it redefines them. As Sriata explained, generative AI fundamentally alters workflows, creating new connections upstream and downstream. This disruption demands a reassessment of the operating model, particularly in terms of:

The remaining workload for human employees after AI automation.
New skills required to collaborate with AI systems effectively.

For example, one Asian bank discovered that their decades-old separation of sales and service departments no longer made sense when deploying AI-driven processes. By collapsing the two departments, they achieved seamless end-to-end workflows powered by AI, improving outcomes.

Actionable Insight: Reimagine workflows to align with AI’s capabilities, and involve CHROs and other leaders to address the human impact of these changes.

3. People and Cultural Evolution

AI adoption isn’t just a technical exercise - it’s a cultural shift. As Krishnamorti pointed out, enterprise AI scaling requires a concerted effort to upskill employees and foster widespread AI literacy.

Roger Barger, SVP at Oracle’s AI/ML division in OCI, echoed this sentiment, arguing that successful AI projects rely on people, not just technology. For instance, Oracle has incorporated AI into its software products in ways that align with users’ natural workflows, making adoption more seamless.

Actionable Insight: Establish AI training programs for employees at all levels, and showcase successful use cases to build confidence and curiosity around AI.

Emerging Challenges in the AI Landscape

While AI presents transformational opportunities, the panel also highlighted key challenges that enterprises must address:

1. Navigating Rapid Technological Change

Krishnamorti described the current AI landscape as a "jagged frontier" where advancements occur at breakneck speed. Model makers like OpenAI and Anthropic release updates multiple times a year, while traditional enterprise software providers like SAP and Salesforce launch annual updates. Organizations must integrate these innovations into legacy systems while maintaining stability and security.

2. Managing Technical Debt

Sanjay Sriata warned that the proliferation of AI tools and platforms risks creating technical debt on an unprecedented scale. Without a cohesive enterprise architecture, organizations may find themselves with fragmented AI systems - agents embedded in HR, sales, and legal systems that don’t communicate with one another.

Actionable Insight: Establish a unified AI strategy and enterprise architecture to avoid sprawl and ensure interoperability between AI systems.

3. The Lifespan of AI Projects and Vendors

Dimitri, a panelist with a global strategy and advisory background, raised concerns about reliance on third-party AI startups. Many innovative solutions may not survive long-term, leaving enterprises with unsupported tools.

Actionable Insight: Regularly assess vendor stability and build internal capabilities to reduce dependence on external providers.

The Role of Leadership in Driving AI Transformation

Leadership plays a critical role in guiding organizations through this complex AI transformation. From defining strategy to ensuring alignment across teams, executives must address several key areas:

Succession Planning and Legacy: Leaders should aim to embed AI as a core component of the business so thoroughly that specialized digital transformation roles become obsolete.
Experimentation and Decision Making: Encourage innovation at the edges while maintaining clear decision-making processes to avoid fragmentation.
Cross-Functional Collaboration: As AI blurs traditional department boundaries, leaders must foster collaboration between roles like CHROs and CIOs to manage human and AI agents effectively.

Key Takeaways

Data is Non-Negotiable: Ensure data cleanliness and accessibility before launching AI initiatives.
Reimagine Operating Models: Design workflows that align with AI’s potential, rather than retrofitting outdated processes.
Focus on People: Upskilling, cultural buy-in, and AI literacy are as critical as technical implementation.
Manage Technical Debt: Avoid fragmented AI systems by planning for enterprise-wide integration and scalability.
Leadership Matters: Executives must lead with a clear vision, balancing experimentation with long-term strategy.
AI as a Business Driver: Tie AI initiatives to measurable outcomes like efficiency gains, customer satisfaction, and revenue growth.
Prepare for Rapid Change: Build agility into your organizational processes to keep pace with technological advancements.

Conclusion

Scaling AI across the enterprise is not a linear journey. It’s a complex process that involves rethinking data strategies, operating models, and cultural dynamics. Business leaders and decision-makers must approach this transformation with a structured, measured mindset while remaining adaptable to rapid innovation.

By prioritizing value creation, fostering cross-functional collaboration, and investing in both people and technology, organizations can unlock AI’s full potential and drive sustainable competitive advantage in the age of intelligence.

Source: "From Pilots to Impact: Scaling AI Across the Enterprise" - MIT Sloan CIO Symposium Videos, YouTube, Jan 3, 2026 - https://www.youtube.com/watch?v=C-2AK-DhG5o