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“When it comes to outsourcing the move to getting gen AI into operations, Gruber recommends a combination of outsourcing and keeping work in-house, although he believes it is more important for larger enterprises to engage a partner.
‘Enterprise apps [require much more work] than just making a demo work,’ he says. ‘Solve the important problems first. For example, modernize your enterprise to be more agile at partnering with other companies to solve strategic problems, empowering R&D to think more creatively and flexibly. You can use gen AI to solve these problems that are traditionally harder to solve.’”
from Moving Generative AI from Experimentation to Operation, Harvard Business Review, April 2025
Adopting AI isn’t about signing another software contract; it’s about strategically identifying where AI can genuinely accelerate your workflows, amplify your people, and unlock new value across the organization.
Consider how purchasing a new tool often goes: pre-contract demos, a few test drives, maybe a handful of post-purchase training sessions—and then it’s “good luck.”
But AI isn’t just another system to plug in. There’s a strategy to it, and the AI industry is evolving at a wildly rapid pace. So, what happens after the demo? That’s where most companies stall—staring at a shiny new AI tool without a real plan for how it fits into the business.
At Curious AI, we believe the real work begins when you move past the pilot: building a deliberate, operational AI strategy that strengthens your core.
AI’s transformative power isn’t realized in pilots. It’s realized when companies do the hard work of embedding, scaling, and governing AI inside real business workflows—when they move beyond experimentation into disciplined, strategic operationalization.
A recent Harvard Business Review Analytic Services report drives this point home:
“AI adoption is not without its hurdles. Many enterprises struggle with identifying the right use cases, sourcing data, ensuring quality, complying with regulatory requirements, and developing a compelling business case, including alignment with business objectives. The key to success lies in a well-defined AI strategy, robust governance models, leveraging ecosystem partners, and a collaborative approach that involves cross-functional teams. IT leaders must work alongside business executives to ensure gen AI implementations align with long-term corporate goals.”
It’s not just about playing with the latest tools. It’s about integrating AI as a living part of your organization’s DNA.
What True AI Operationalization Means
When we talk about “operationalizing” AI, we’re talking about more than spinning up a chatbot. We’re talking about weaving AI into the very fabric of a business:
- Embedding AI in customer service, logistics, finance, marketing, and HR workflows
- Building internal data governance to power clean, reliable models
- Creating continuous feedback loops to retrain and refine AI outputs
- Enabling humans and AI to collaborate within transparent, ethical frameworks
It requires rethinking how work gets done—and ensuring AI evolves with the organization, not as a bolt-on feature. After all, AI is not a one-and-done rollout where your team deploys a tool, checks a box, declares victory. This approach guarantees missed opportunities—and real risk.
Operational AI R&D means:
- Piloting AI across varied use cases
- Stress-testing models and workflows under real-world conditions
- Documenting successes and failures to inform scalable strategies
- Investing in continuous model improvement, data optimization, and user enablement
Without operational AI R&D, companies set themselves up for costly rework, fragmented systems, and eroding trust among employees and customers alike.
Operationalization Is a Maturity Model
At Curious AI, we believe in helping businesses grow through AI, not just “adopt” it. We view operationalization as a maturity journey:
- Experimentation (Pilots and early use cases)
- Operationalization (Embedding AI into workflows)
- Optimization (Refining models, processes, and outputs)
- Governance (Ensuring ethical, secure, repeatable AI)
- Innovation at Scale (Leveraging AI for strategic differentiation)
It’s why we structure our service tiers the way we do—meeting organizations where they are and preparing them for where they’re headed. Operationalizing AI isn’t just about technology; it’s about strengthening your ability to solve the problems that matter most—faster, smarter, and with more resilience. The future won’t belong to the companies who merely experimented with AI.
It will belong to those who built something real—something scalable, responsible, and deeply integrated into their organizations.
Operational AI R&D is how you get there.
At Curious AI, we’re not just helping companies “use AI.”
We’re helping them build a future where AI becomes a living, breathing part of their growth story.
Ready to discuss how your organization can operationalize AI? Contact us below to schedule a free pre-consultation call.
Reference:
Harvard Business Review Analytic Services. (2025). Moving Generative AI from Experimentation to Operation. Harvard Business Publishing.
