Slack started as an internal communication tool at a gaming company. AWS began as Amazon's internal infrastructure. Claude Code emerged from Anthropic's internal development workflows before becoming a product that's now helping define the AI coding category. What internal AI tool in your organization could become your next revenue stream?
As you might remember from previous issues, I use a three-bucket framework for explaining the potential of AI value creation: Boosting Productivity, Creating New Value, and Driving Disruption. In this issue I want to explore a direction that not enough leaders consider - when internal productivity tools can become products and new offerings.
When I work with companies on their AI initiatives, the first bucket - Boosting Productivity - is usually where they start. A team builds an AI assistant for writing customer emails. Another creates a tool for analyzing sales data. Engineering adopts and optimizes a coding companion that speeds up development. These internal tools generate genuine value: saved time, reduced errors, faster delivery.
But why stop there? Why not turn the best internal productivity tools into an offering that would be worth more outside the organization rather than just inside?
The shift from internal tool to product is not accidental. It could follow a systematic path. First, you build something to solve your own pain. You iterate based on internal feedback. The tool gets good - really good - because it's solving a problem you deeply understand. Then it could be a good time to ask: "Why isn't this a product?"
This pattern isn't new. What's different in the AI era is the speed at which this transition can happen and the breadth of tools it applies to.
I experienced this firsthand with TempoHack - a time tracking and productivity tool I originally built to solve my own problem: tracking time across multiple consulting projects and understanding where my hours actually went. After using it internally and refining it based on my own needs, I realized other consultants and freelancers faced the same challenge. Using Base44, I was able to turn this internal tool into an AI-powered product that could serve others - without the months of development that would have been required just a few years ago.
According to Menlo Ventures' 2025 State of Generative AI report, enterprise AI has surged from $1.7 billion to $37 billion since 2023 - now capturing 6% of the global SaaS market and growing faster than any software category in history. A significant portion of this growth comes from companies productizing capabilities they built first for themselves.
In a recent interview at AWS re:Invent, Colleen Aubrey, SVP of Applied AI Solutions at AWS, described a phenomenon I've observed with clients too: tools that spread organically within organizations often signal productization potential.
"There are a good amount of AI tools that have grown up in one team within Amazon," she explained, "that like, you know, a month later you've got thousands of people using them." This internal "social contagion" - where a tool built by one team rapidly spreads to others - is a powerful signal that you've solved a real problem worth productizing.
What's particularly interesting is how this is accelerating. Aubrey described pushing teams to achieve "10 people and 3 months" instead of "50 people and 9 months" - constraints that force teams to leverage AI in the development process itself. The result was faster cycles from prototype to working product, and more opportunities to identify which internal tools deserve external attention.
As a leader, you should at least consider this path, and I would go so far as saying that you must get your teams to launch at least a couple of experiments with this pattern in mind.
Not every internal tool should become a product. Here's how to evaluate whether your internal AI capabilities have external potential:
- Assess domain specificityAssess domain specificity Internal tools that solve generic problems (email writing, document summarization) face intense competition - ChatGPT, Claude, and dozens of others already serve these needs. But tools built on your proprietary knowledge and data, unique workflows, or specialized domain expertise create genuine differentiation.
Ask: Does this tool work because of general AI capability, or because of something specific we know or do?
- Evaluate the knowledge moatAs I discussed in Issue #2 with the Ainno example, proprietary knowledge becomes your moat. Generic AI can answer generic questions. But AI trained on your specific methodologies, terminology, and examples creates value that competitors can't easily replicate.
The waste management company I mentioned in Issue #3 had years of collection records, customer patterns, and operational knowledge. AI built on that data solved problems in ways that off-the-shelf tools couldn't approach.
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Measure internal impact firstMeasure internal impact first Before productizing, document the internal value created. Time savings, error reduction, customer satisfaction improvements - these become your proof points for external customers. If your internal users aren't enthusiastic, external customers won't be either.
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Consider the scaling requirementsConsider the scaling requirements Internal tools can afford rough edges. Scaled products cannot. Moving from internal prototype to external product requires investment in security, compliance, support, documentation, and reliability. Make sure the potential market justifies this investment.
I see organizations taking different paths from internal tool to market offering:
Model 1: The standalone productTake the internal tool, package it properly, and sell it as a separate product. This works when the tool solves a problem common across your industry or adjacent industries. The advantage is clear product focus. The challenge with this model is that you're now running two businesses - your core business and a software company.
Model 2: The service enhancementEmbed the AI capability into your existing services, creating differentiation without launching a new product line. Consulting firms do this well—building AI tools that make their consultants more effective, then offering those enhanced capabilities as part of their service packages.
Model 3: The platform playBuild the infrastructure that allows others to create similar tools. This is the most ambitious model - essentially becoming a platform provider. It requires significant investment but can create substantial recurring revenue if successful.
If you've identified an internal AI tool worth productizing, here's the progression I recommend:
Phase 1: Document and stabilizeCapture what makes the tool work. Document the training data, the prompts, the integration patterns. Stabilize the architecture so it can handle external users without constant internal intervention.
Phase 2: Pilot with partnersFind 3-5 friendly external users - partners, customers, or industry contacts - willing to test the tool. Their feedback will reveal assumptions you didn't know you were making. Pay attention to what they try to do that the tool doesn't support.
Phase 3: Productize the experienceBuild the wrapper: onboarding flows, documentation, support channels, billing infrastructure. This phase is where many internal-to-product transitions fail. Don't underestimate the work required to make something customer-ready.
Phase 4: Launch and learn (Ongoing)Go to market with a clear value proposition based on your pilot learning. Plan for rapid iteration - the first external customers will teach you more than months of internal use.
The companies winning in enterprise AI aren't necessarily those with the most sophisticated technology. They're the ones who recognize that solving their own problems deeply creates valuable intellectual property—and who have the discipline to productize that knowledge effectively.
Your internal productivity tools might be your most underleveraged asset. The question is whether you're treating them that way.
To explore the full landscape of enterprise AI spending and where productized tools are gaining traction, I recommend Menlo Ventures' comprehensive 2025 State of Generative AI report: 2025: The State of Generative AI in the Enterprise
For insights on how Amazon is accelerating internal AI development and seeing tools spread organically across teams, watch Colleen Aubrey's interview at AWS re:Invent 2025. Note how she also touches about building trust with AI agent team members (around minute 16:20):