Productizing vs. Operationalizing AI: Why Most Organizations Fail at Both
How the Five Chains of Change Sabotage Your AI Strategy and What to Do About It
The AI revolution isn't coming—it's here. Yet despite billions invested in artificial intelligence initiatives, most organizations struggle to translate AI potential into measurable business impact. After working with over 400 executives on digital transformation and AI implementation, I've discovered that the problem isn't technological—it's strategic.
Organizations consistently confuse two fundamentally different approaches to AI integration: productizing AI and operationalizing AI. This confusion, combined with the psychological barriers I call the "Five Chains of Change," creates a perfect storm that sabotages even the most well-funded AI initiatives.
The Two Pillars of AI Strategy
Successful AI transformation requires mastering both dimensions of artificial intelligence integration, each serving distinct but complementary purposes.
Operationalizing AI: Empowering Your People
Operationalizing AI focuses internally on your employees and answers the question: "How can we support our teams in leveraging AI-powered platforms and tools to boost productivity?"
This involves integrating generative AI tools like ChatGPT for content creation, Microsoft Copilot for document automation, and AI-powered analytics platforms for decision-making. It's about making your existing workforce more efficient, creative, and capable through artificial intelligence augmentation.
Consider an international airport client of mine with access to massive data lakes—live flight plans, passenger check-ins, security wait times. By operationalizing AI through Microsoft Copilot Enterprise, they transformed their predictive analytics capabilities. Their AI now generates real-time maintenance recommendations, optimizing when and where to focus repairs on jet bridges, gates, and critical infrastructure. Instead of reactive maintenance, they're now predictively resourced, addressing the most urgent problems before they become crises.
Productizing AI: Delighting Your Customers
Productizing AI focuses externally on customers and users, answering: "How do we leverage our data to differentiate our user experience and create new value propositions?"
This means embedding artificial intelligence directly into your products and services to create competitive advantages that customers can't get elsewhere. Salesforce exemplifies this approach—they've saved 50,000 hours in employee productivity by using the same Salesforce Einstein products they sell to customers, creating a feedback loop that improves both internal operations and customer offerings.
Why Organizations Struggle: The Five Chains of Change
Despite understanding these distinctions, most organizations fail at both operationalizing and productizing AI. The barriers aren't technical—they're psychological. The same Five Chains of Change that sabotage traditional transformation efforts create even more complex problems when applied to AI initiatives.
Chain 1: Clutter and Chaos - The AI Tool Explosion
The Problem: Organizations suffer from "AI tool proliferation"—departments independently adopting dozens of generative AI platforms without coordination or strategy.
Marketing adopts Adobe's Creative Cloud AI features. Finance implements Airbase's generative AI capabilities. Sales deploys Gong for conversation intelligence. Success teams use Intercom's AI chat. Each department optimizes locally while creating chaos globally.
The AI Impact: Teams become overwhelmed by competing AI platforms, none of which integrate effectively. Instead of productive adoption, organizations develop AI fatigue.
The Solution: Apply the "Zoom Out to Zoom In" principle. Before implementing individual AI tools, map your entire AI ecosystem. Identify overlaps, gaps, and integration opportunities. Create an AI governance framework that encourages experimentation within strategic boundaries.
Chain 2: Comfort with the Status Quo - AI Resistance
The Problem: Employees resist AI adoption not because they fear technology, but because they're attached to existing workflows and expertise that made them successful.
The AI Impact: Even when organizations provide access to powerful AI tools, adoption remains superficial. People use ChatGPT for basic tasks while avoiding the deeper integration that would transform their work.
The Solution: Make AI adoption personal and voluntary initially. Show skeptics specific use cases relevant to their current challenges. One executive assistant I work with now sees my ChatGPT conversations, learning how I craft prompts and coach AI systems. Seeing the process demystifies the technology and builds confidence.
Chain 3: Competing Interests - The AI Coordination Crisis
The Problem: Successful AI implementation requires unprecedented coordination between departments that typically operate independently.
Imagine a marketing manager wanting to implement Adobe Creative Cloud's AI features. This "simple" decision requires:
Procurement to evaluate vendors and ensure compliance
IT to assess data privacy, storage, and integration requirements
Finance to approve budget implications
Security to validate data protection standards
Legal to review terms and usage rights
The AI Impact: AI initiatives stall in bureaucratic complexity, creating frustration and missed opportunities.
The Solution: Develop cross-functional AI implementation teams with clear decision-making authority. Create streamlined approval processes specifically for AI tools, recognizing that traditional procurement cycles are too slow for the AI landscape.
Chain 4: Constraints - The AI Investment Paradox
The Problem: Organizations either under-invest in AI due to budget constraints or over-invest without clear ROI frameworks, creating resource allocation challenges.
The AI Impact: Teams either lack access to powerful AI tools or waste money on expensive platforms they can't effectively utilize.
The Solution: Start with high-impact, low-cost AI implementations. Measure time savings and productivity gains early. One client tracked that their AI implementation saved 50,000 employee hours—at $50/hour, that's $2.5 million in tangible ROI within six months.
Chain 5: Apathy - AI Implementation Fatigue
The Problem: Constant AI announcements and tool updates create change fatigue, leading to disengagement even among early adopters.
The AI Impact: Teams stop exploring AI capabilities, missing breakthrough applications that could transform their work.
The Solution: Focus on AI outcomes, not features. Share stories of how AI implementations have made people's work more meaningful, creative, or impactful. Connect AI adoption to personal and professional growth rather than just efficiency gains.
Building Change Fluency for AI: The Path Forward
Successful AI integration requires developing Change Fluency in the context of artificial intelligence—the ability to navigate AI implementation with confidence while avoiding common pitfalls. This involves mastering three critical skills:
1. Critical Thinking for AI Coaching
Develop the ability to review and improve AI outputs. Ask: "What will advance the thinking here? What will make this better?" The most successful AI users become skilled coaches, iteratively improving prompts and outputs.
2. Strategic Storytelling for AI Influence
Great AI ideas die without the ability to influence stakeholders. Learn to tell compelling stories about AI impact that bring teams and leaders along the journey. Focus on human outcomes, not technical specifications.
3. Iterative Imagination for AI Innovation
Like a child building with Lego blocks, develop comfort with experimental AI implementations. When one approach doesn't work, quickly pivot to new possibilities rather than defending failed strategies.
Practical Next Steps
For Operationalizing AI:
Audit current AI tool adoption across departments
Create integration guidelines that encourage experimentation within strategic boundaries
Measure productivity impacts with baseline time-tracking before AI implementation
For Productizing AI:
Identify customer pain points where your data could create AI-powered solutions
Develop minimum viable AI features that customers can experience immediately
Create feedback loops between internal AI usage and customer-facing AI features
For Breaking Change Barriers:
Start with voluntary AI adoption programs rather than mandates
Create cross-functional AI implementation teams with clear authority
Share specific success stories that demonstrate AI impact on meaningful work
The organizations that thrive in the AI era won't be those with the most advanced technology—they'll be those that develop the Change Fluency to integrate AI effectively across both internal operations and customer experiences.
Which dimension of AI integration—operationalizing or productizing—represents your biggest opportunity for breakthrough results?
Jay Kiew is a change navigation strategist, keynote speaker, and author of the upcoming book "Change Fluency: 9 Principles to Navigate Uncertainty and Drive Innovation." His Change Fluency framework has helped executives across industries develop the adaptive capacity to transform AI investments into measurable business impact. Contact us to learn how.
