Why Your CEO Needs an AI Inventory Yesterday 🤯🚨
AI Chaos Is Costing You Millions - Fix It with AI Portfolio Management
#beyondAI
In the corporate world, the call for more structure and governance in managing AI initiatives is growing louder—and for good reason. Over the past two decades, we’ve seen companies enthusiastically dive into AI, hiring experts left and right. Some approached this strategically, while others took a more scattershot approach. The results? Varied, to say the least.
AI teams across organizations have been busy building models: some for internal use, others to enhance their company’s core services or products. But here’s the common thread—most have little to no visibility into what their counterparts in other teams are working on. It’s not uncommon for one team to discover—by pure accident—that another department is building the exact same thing, just for different stakeholders.
This kind of overlap isn’t a minor annoyance; it’s a serious problem. When teams unknowingly duplicate work, they multiply costs without multiplying value. Worse, they lose time—time that could have been spent improving or scaling existing solutions.
And here’s the heart of the issue: AI models, much like any other corporate asset, need to be managed with clear ownership, transparent value tracking, and robust usage guidelines.
When companies don’t think of AI as part of a broader portfolio, they miss out on the opportunity to scale. Anyone who’s been involved in building a model knows how resource-intensive it is. It’s a heavy lift, and once that lift is complete, why wouldn’t you maximize its impact?
The ideal scenario is this: build a model once and adapt it for multiple use cases with minimal effort. That’s scalability. But the reality? It’s far from this ideal.
Take enterprise IT departments as our grown-up cousins, for instance. Most large companies understand that maintaining a well-organized software portfolio isn’t a luxury—it’s business-critical. Why not borrow from these established practices? Why not take what works in IT and adapt it to AI?
This is why I’m making the case for every company with a serious investment in AI to introduce AI Portfolio Management. It’s not rocket science. The tools, processes, and frameworks we need are already at our disposal. The challenge isn’t invention—it’s adaptation and integration. We don’t need to overhaul entire systems. We just need to introduce one extra step into daily workflows—a step that saves countless others down the line. Seems like a good trade-off, doesn’t it?
For AI Product Managers, this topic is equally relevant. While this article is written for companies at large, it’s packed with insights to help you as an individual contributor. Whether or not your organization already has portfolio and inventory management in place, understanding these concepts empowers you to:
Manage your AI products more effectively.
Capture key details to contribute to a future portfolio.
Align your work with broader organizational goals—and prepare for the moment your work becomes part of something bigger.
Happy reading 🛋️
What Is AI Portfolio Management?
It’s easy to have different associations when we talk about a portfolio. For me, at the beginning of my own portfolio journey, the term conjured up multiple images:
A stock market portfolio—assets reflecting financial worth.
An artist’s portfolio—a curated collection showcasing their skills.
A job application portfolio—a selection of your best work proving your capabilities.
What ties all these examples together is a common theme: they’re all curated collections that reflect value. They aren’t random assortments—they’re purposeful, intentional, and designed to give a clear picture of worth.
This is exactly how we should think about an AI portfolio in a corporate context. It’s not just a list of all the models your teams have built or the AI initiatives underway. It’s a curated collection of assets that reflect the value your organization is generating with AI.
And, just like a stock portfolio or an artist’s portfolio, an AI portfolio needs to be actively managed.
Active management means making tough decisions:
Which models are worth further investment?
Which should be retired or repurposed?
What new initiatives will maximize resources and align with company goals?
Every portfolio faces limiting factors:
For the artist, it’s the finite space on a gallery wall.
For the broker, it’s the amount of money available to invest.
For the corporate AI space, it’s budgets, technical expertise, data availability, and more.
To make your AI portfolio as valuable as possible, you need to navigate these constraints wisely.
There are countless reasons to establish an AI Portfolio Management function. To make it easier for you to decide whether to implement this function—or not—I’ve compiled a list of the key benefits I’ve come across:
1. Strategic Alignment
Benefit: Align AI investments with business goals. Identify and prioritize AI projects that drive measurable outcomes like cost reduction, revenue growth, or customer satisfaction, ensuring alignment with strategic business objectives.
2. AI Use Case Portfolio Optimization
Benefit: Avoid redundancy and maximize value. Evaluate AI use cases to identify overlapping efforts, such as multiple teams working on similar models or data sets, and consolidate efforts to optimize resources.
3. AI Risk Management
Benefit: Minimize risks in AI deployment. Track AI model performance, compliance with regulations (e.g., GDPR, AI Act), and ethical risks like bias or unintended consequences to proactively address issues.
4. Cost and Resource Optimization
Benefit: Manage AI investments for maximum ROI. Provide visibility into the costs of training, deploying, and maintaining AI models and identify areas to reduce computational expenses or improve operational efficiency.
5. Improved Decision-Making
Benefit: Data-driven prioritization of AI initiatives. Use key performance indicators (KPIs) like accuracy, adoption rate, or time-to-value to make informed decisions about continuing, pivoting, or stopping AI projects.
6. Enhanced AI Governance
Benefit: Ensure compliance, ethical AI use, and accountability. Establish governance frameworks to monitor AI model usage, ensure fairness, and mitigate risks, while providing transparency into AI decision-making processes.
7. Supporting AI Transformation
Benefit: Facilitate company-wide AI adoption. Plan and coordinate the rollout of AI initiatives across departments, ensuring infrastructure readiness and aligning transformation efforts with business needs.
8. Enterprise-Wide AI Architecture
Benefit: Maintain a unified and scalable AI infrastructure. Design a robust AI architecture that integrates with existing IT systems and supports data pipelines, model lifecycle management, and deployment workflows.
9. Enhancing Collaboration Across Teams
Benefit: Foster alignment between data scientists, engineers, and business teams. Provide a shared platform or repository for tracking AI models, datasets, and results to improve collaboration and reduce silos.
If you carefully went through this list, you might have realized that establishing a powerful AI Portfolio Management function requires one critical foundation: bringing all AI teams across the organization together to document their new, in-development, and live AI products in one centralized place. This step is essential to enable that new function to work effectively.
And yes, I’m not saying this is an easy endeavor. It’s challenging, time-consuming, and requires commitment across all levels of the organization. But it’s necessary.
I firmly believe that every disruptive technology—and by now, I think we can all agree that AI falls into this category—requires an organizational and processual change to reveal its true potential. Without this shift, we’ll only see the power of AI at the tech level, never fully realizing its impact on the business side.
You’ll know your efforts have been successful once you’ve built and maintained a robust AI Inventory. Only with an AI Inventory can your AI Portfolio truly unfold the benefits I’ve outlined above.
What Is an AI Inventory?
The word inventory takes me back to my school days when some of us worked part-time at a large grocery store during the summer. The task was simple: inventory. At least, that’s what they called it. What it really meant was counting every item on the shelves, documenting what was there, and noting what needed restocking. It was tedious, but also revealing. You’d discover that some products were flying off the shelves while others were collecting dust in the corner.
This is the essence of an inventory: it’s a systematic record of what you have. And while it may sound basic, having an accurate inventory is critical to making informed decisions. In retail, it helps manage stock levels. In AI, it’s a dynamic map of your AI ecosystem, detailing every asset—what it’s for, who owns it, how often it’s used, and how it might be repurposed or reused. Without this map, portfolio management is like trying to curate an art exhibit without knowing which pieces are in storage.
An AI Inventory provides visibility into the organization’s AI landscape. It ensures that every AI product and use case is accounted for, enabling the Portfolio Management function to make strategic decisions with confidence. Without an accurate and well-maintained inventory, AI efforts can quickly become fragmented, redundant, or misaligned with business goals.
So what exactly should be captured in an AI Inventory to support Portfolio Management? Here's a checklist to ensure your inventory captures everything it needs:
Key Details to Capture for Each AI Product & AI Use Case
General Information
Name of the AI Product or Use Case
Description of its purpose and functionality
Status (e.g., conceptual, in development, live, retired)
Ownership and Responsibilities
Owning team or department
Product Owner or main point of contact
Data Owner (if applicable)
Business Context
Problem it solves or opportunity it addresses
Stakeholders involved (internal and external)
Business unit(s) it supports
Expected business impact (e.g., cost reduction, revenue growth, efficiency improvements)
Technical Details
Type of AI model (e.g., regression, classification, generative)
Underlying technology or framework (e.g., TensorFlow, PyTorch)
Data sources used for training
Deployment environment (e.g., cloud, on-premises)
Performance Metrics
Key performance indicators (KPIs) for the model
Accuracy, precision, recall, or other relevant metrics
Current performance levels
Reusability Potential
Similar use cases or models it could support
Level of customization required for reuse
Dependencies or prerequisites for reuse
Lifecycle Management
Date of creation or deployment
Maintenance schedule
Version history
Compliance and Governance
Data privacy considerations (e.g., GDPR, CCPA compliance)
Ethical considerations (e.g., bias assessments)
Approval processes it has undergone
Cost and Resources
Development costs (time and budget)
Operational costs (e.g., compute resources, licensing fees)
Resource allocation (team members involved)
Usage and Impact
Frequency of use
Business outcomes achieved (quantitative and qualitative)
Feedback from end users
Final Thoughts
I’m pretty sure there’s more that could be tracked in an AI inventory, and the list will likely evolve. But I believe this is a solid starting point. And trust me, implementing even this foundational structure will already be challenging enough.
That said, I firmly believe unlocking the full potential of your organization’s AI initiatives requires more than technical innovation. It goes beyondAI. It demands organizational and processual change. The sooner companies embrace this, the sooner the benefits will materialize.
For AI Product Managers, this guide highlights the details you need to capture when building an AI product—even in the absence of formal portfolio or inventory management.
Because one thing is certain: someday, the CEO will ask, “Who can tell me how much value we’ve generated from our AI initiatives?” And in that moment, you’ll want to be the one still sitting confidently in your seat, ready to report not only on your products but on their contribution to the company’s overall success.
So, why not start now?
JBK 🕊
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