AI Products
What is an AI product
An AI product is a reusable capability for solving a recurring task class with AI. It can be an internal solution, service, platform, or functional block that plugs into different departments and scenarios.
The key difference from a one-off pilot is reusability. A pilot closes one case for one team. A product closes a class of similar tasks for many teams and outlives a single project.
Without a product layer, the company builds a new solution for every request: duplicates appear, the same task class is solved in different ways, pilots are not reused, IT and security re-review similar solutions, and impact cannot be measured. The product layer turns scattered solutions into a managed set of capabilities.
AI product characteristics
Every AI product — platform or applied — is described by one set of characteristics. They create a shared language: products can be compared, maturity assessed, and evolution decisions made.
| Characteristic | What it describes | Why it matters |
|---|---|---|
| Type | Platform or applied | Defines the user and how the product evolves |
| Task class | Which recurring task class it closes | The core test: is there a product at all |
| Target audience | Who it is for: teams, roles, departments | Without an audience a product is not reused |
| Owner | Who is accountable for value and evolution | A product without an owner degrades |
| Reusability | How many scenarios and departments it can serve | Distinguishes a product from a one-off solution |
| Maturity | Idea, pilot, production, evolution, retirement | Defines what to do with the product next |
| Risk profile | Access to data and systems, cost of error | Sets the depth of control and approvals |
| Delivery track | How the need is closed and driven to impact: artifacts, approvals, processes | Sets the implementation route for the product type and the risk of the automated process |
| Support loop | Who maintains it after launch | Without support a product dies after the pilot |
| Metrics | Usage, quality, impact, cost | Show whether the product is justified |
These same characteristics form the minimum product card in the portfolio catalog — a single record that shows what the product is, who it is for, and what state it is in. The full card structure is defined in the AI product card artifact.
Two AI product levels
Type is the first product characteristic. By type, AI products split into two levels.
1. Platform AI products
Platform products are base technology capabilities on top of which applied solutions are built.
They do not always solve a business task directly, but they let the company quickly create, test, and scale AI scenarios.
| Product | What it gives the company |
|---|---|
| Corporate LLM | Shared access to models, prompts, policies, logging, and guardrails |
| RAG platform | Search and answer generation over corporate knowledge |
| ML platform | Training, deployment, and monitoring of ML models |
| Code agent | Faster development, code analysis, testing, and documentation |
| Agent runtime | Environment for running, controlling, and monitoring AI agents |
Platform products answer:
Which base AI capabilities does the company have?
2. Applied AI products
Applied AI products are services built on top of platform capabilities and oriented toward a specific task class. They can use several platform products at once.
For example, a "Regulatory document assistant" can use an LLM to generate documents from templates, a RAG platform to find relevant information, and agents under the hood to automate routine steps.
| Product | Built on | Task class |
|---|---|---|
| Knowledge base assistant | LLM + RAG | Document Q&A |
| Document generator | LLM + templates + workflow automation | Standard document preparation |
| Project manager assistant | LLM + RAG + MCP Atlassian + agent orchestrator | Statuses, minutes, risks, plans |
| Inquiry analyzer | LLM + ML classification + integrations | Inquiry analysis, routing, prioritization |
| Developer assistant | Code agent + repositories + docs | Development, review, tests, documentation |
| Compliance agent | LLM + RAG + control rules | Text, document, operation checks |
Applied products answer:
Which recurring business tasks can the company already solve with AI?
Where to Find the Full Product List
The full list of classes is not duplicated on this page. It lives in AI product taxonomy: that page captures 18 market classes and maps them to the operational catalog of no more than 10 delivery tracks.
This page answers a different question: what counts as an AI product in the operating model and how to decide whether a recurring task class deserves a separate product. The taxonomy classifies the market and business requests; the AI product model supports a management decision inside the portfolio.
How to identify which AI products you need
A product portfolio is not designed top-down by picking trending technologies. It is derived from demand — bottom-up. The most practical way is to take the company's last 30–50 AI requests and run them through a simple exercise.
- 1Remove department names
- 2Cluster by verb
- 3Map verbs to products
- 4Apply the 3×5 rule
Step 1. Remove department names. Describe the functional need, not "AI for HR" or "contract review for legal." Behind a department name there is almost always the same task class.
| Request | Functional need |
|---|---|
| HR chatbot | search and answer over internal knowledge |
| Contract review | extract, compare, and check documents |
| Developer assistant | generate, explain, and review code |
| Risk model | predict probability, classify events, detect anomalies |
Step 2. Cluster by verb, not by department. Almost all AI requests reduce to a small set of verbs: find, summarize, extract, compare, classify, generate, predict, automate, recommend. A verb that recurs across functions is a signal that you need a reusable capability, not another department-specific solution.
Step 3. Map verbs to products. Behind each recurring verb stands an AI product candidate.
| What people need | AI product candidate |
|---|---|
| Find answers | Knowledge search / RAG |
| Process documents | Document intelligence |
| Capture meetings | Meeting intelligence |
| Predict or score | ML platform |
| Automate steps | Workflow automation / agents |
| Build tools faster | Code agent |
| Govern all AI initiatives | AI governance platform |
Step 4. Apply the 3×5 rule. A capability deserves to become a separate AI product only if it:
- appears in at least 3 different functions;
- can support at least 5 real initiatives;
- has repeatable data, security, and delivery requirements;
- can be configured rather than rebuilt for every case;
- has a clear owner and roadmap.
If a candidate fails the 3×5 rule, it is still a project for a specific case, not a product.
Candidates split into two levels right away: what closes a task class for users is an applied product; what several applied products need at once (LLM access, knowledge search, running agents) is a platform product.
For each product candidate, decide build / buy / partner — build it, buy a ready solution, or partner; the choice depends on task uniqueness, data and security requirements, speed, and cost. Review the portfolio at least quarterly: the market of models and tools changes faster than most technologies, and some "build" decisions are eventually cheaper to replace with "buy."
Anti-pattern and right approach
Anti-pattern. Legal, HR, procurement, risk, and IT — every department asks for its own separate RAG. The company ends up with five similar solutions, five support loops, five architecture reviews, and five security approaches.
Right approach. The company sees a common task class — "search and answers over documents" — and develops one platform product, then builds applied assistants on top of it:
- Legal assistant
- HR assistant
- Procurement assistant
- Risk manager assistant
- IT support assistant
The base technology is one; the applied products are adapted to different classes of users and tasks.
Key principle
The company must manage not a stream of scattered requests, but a portfolio of reusable AI products through which those requests are closed. Without it, AI adoption becomes a set of disconnected pilots; with it, the company gets less duplication, faster delivery, clearer architecture, managed security, and transparent accountability.
Connection to initiatives
An AI product answers "which reusable capabilities the company has." How a company travels from a business need to confirmed impact through these products is the topic of the AI initiative portfolio: one product serves many tasks, and one task can use several products.