Moving Enterprise AI from Pilot to Production at Scale

Most enterprise AI initiatives never escape pilot purgatory. After working with dozens of Fortune 500 clients at Protiviti and Capgemini, Helena Ristov has identified the exact patterns separating companies that scale AI successfully from those stuck running endless proof-of-concepts.

In this episode (recorded as 2025 came to a close), Helena breaks down the practitioner's playbook for moving from AI experimentation to measurable business impact including why strong prototypes fail at scale, how to prioritize competing use cases, and why governance should be treated as design thinking rather than compliance theater.

Key Topics Covered

[00:00] Introduction

• Helena's background in AI Innovation Studios and Centers of Excellence
• Working with Fortune 500 clients on AI validation and deployment

[03:15] The 0-to-1 Validation Process

• How AI Innovation Studios validate use cases before full implementation
• The value vs. complexity prioritization matrix

[08:42] Why Trust Matters in Enterprise AI

• Defining "trustworthy AI" in practice
• Governance as design thinking vs. compliance theater

[14:20] Core Question 1: Experimentation to Execution

• Why strong prototypes don't automatically translate to production
• The technology gap between laptop demos and industrial-grade systems

[21:35] Core Question 2: Proving Value Through Measurement

• The one metric that matters to CFOs: net operating margin
• Why all secondary KPIs roll up to profit margin

[26:50] Core Question 3: Prioritization Framework

• The value vs. complexity matrix for depoliticizing AI decisions
• Focusing on high-impact, realistic-effort opportunities

[32:15] Core Question 4: Governance as Enabler

• Operational governance: logging, monitoring, accountability trails
• RACI/DACI frameworks for decision ownership

[38:40] Core Question 5: The Future of Enterprise AI

• The shift from assistive AI (efficiency) to agentic AI (productivity)
• Multi-cloud infrastructure reducing vendor lock-in risk

[44:25] Deep Dive: Assistive vs. Agentic AI

• Assistive AI helps humans work faster; workflows stay the same
• Agentic AI restructures work: decision-making, tool orchestration, multi-step actions

[50:10] Building Consensus with Competing Stakeholders

• How to get multiple directors aligned on AI priorities
• The structured roadmap approach: shared objectives → dependencies → milestones
• Framing deprioritization as sequencing, not rejection

[54:30] Closing Takeaways

• Successful AI adoption isn't about having the best models
• The discipline to validate, prioritize, and build trust into foundations
• Why companies that slow down to get governance right actually scale faster

About Helena Ristov

Helena Ristov has extensive experience establishing AI Innovation Studios and Gen AI Centers of Excellence at consulting firms like Protiviti and Capgemini, helping Fortune 500 companies validate which AI investments are worth making before committing millions to deployment.

With a Master's degree in Mathematics from the University of Texas at Austin, Helena has architected production Gen AI platforms for Fortune 20 clients and pioneered the development of Gen AI agent suites that balance innovation with data privacy and safety regulations.

Connect with Helena Ristov

• Helena Ristov's LinkedIn Profile

About Enterprise AI Unlocked

Enterprise AI Unlocked explores how organizations move from AI experimentation to measurable business impact. Hosted by Paul Brzozowski from Olakai's founding team, each episode uses a consistent five-question framework to unpack what works, what doesn't, and how to measure success when deploying AI at scale.

About Olakai

Olakai is the Enterprise AI Intelligence Platform that helps organizations measure, govern, and scale their AI deployments with confidence. Learn more at olakai.com.

Connect

• Paul Brzozowski's LinkedIn Profile