AI Use Case Discovery: find the AI use cases worth building
The advantage goes to the organizations that build the right thing.
With Anthropic Claude and AI agents on ServiceNow, a working version of a good idea is within reach for almost any team. So the advantage goes to the organizations that build the right thing, learn quickly, and connect every use case to what the business is trying to achieve.
Your strategy and your pain points
The use cases worth funding
Value and readiness
A prioritized backlog with a clear first move
When building is fast, choosing well is where the value is decided
AI Use Case Discovery helps you answer three questions before you commit budget. When almost anything can be built, those answers separate AI that changes how the business runs from AI that adds features nobody asked for. We apply the same discipline to the AI we build for ourselves.
Are we building the right thing?
Can we learn and iterate quickly?
Does this use case serve our strategy?
One way to compare every idea.
Ideas arrive from every department, in different language and at different levels of detail. Discovery puts them into one format, so ranking them becomes a decision rather than a debate.
Value you can defend.
Every use case states what would measurably improve, by how much, and how it will be checked after go-live.
Readiness known before the build.
Every idea assumes a stable process, clean data, accessible systems and a named owner. Discovery checks all four up front, so a gap surfaces while it is still cheap to close.
The right technology for the problem.
Requests often arrive as "we want an AI agent". Sometimes an agent is right. Often the better answer is an automation, an integration, or a process change first, and you know which before you spend.
A structured way to turn business priorities into AI decisions
AI Use Case Discovery is a structured engagement that starts with your business strategy and your most important pain points, then identifies and assesses AI use cases against consistent criteria. For each one it answers what problem is being solved, whether AI is the right instrument, what kind of AI fits, what data and oversight it needs, how valuable and how feasible it is, and whether it proceeds. It ends in decisions, a prioritized backlog and a clear way forward.
- 01Strategy and pain points
- 02The work as it runs today
- 03AI opportunity
- 04Value hypothesis
- 05Feasibility
- 06Readiness
- 07Risk
- 08Technology fit
- 09Priority and first move
Start with your strategy and the work, then choose the AI
Discovery begins with what the business is trying to achieve and where it is losing time, money or customers today. That keeps every use case tied to strategy from the first conversation.
A use case with a real process behind it can be measured, and a measurable use case can be ranked, funded and built with confidence.
We combine stakeholder conversations with your actual operational data, and we start where the process data shows repetitive, high-value work. You get use cases grounded in how your business really runs, detailed enough to build from.
The right answer for every problem, with AI where it adds the most
For every candidate we ask one question explicitly and record the answer: does this problem actually need AI? It keeps your AI budget on the problems where AI adds real capability, and it gets the other wins built faster and at lower cost.
Redesign the process.
When manual work exists because the process is inconsistent, the process gets fixed first, so automation does not scale the problem.
Connect the systems.
When the information exists but does not move, an integration solves it. This is one of the most common findings.
Automate with rules.
When the trigger, the action and the output are all known, rules-based automation is cheaper, more predictable and easier to audit than a model.
Leave it human.
When the judgment, the relationship or the accountability is the point, the work stays with a person.
Where AI is the right instrument, the next decision is which kind.
Automation
A known trigger produces a known action, with no inference
Stable rules, deterministic output, auditability matters
Clear rules and clean triggers
Predictive AI
Learns from patterns in historical data to classify or predict
Real history exists and the task repeats
Volume, quality and time in production
Generative AI
Reads, summarizes, extracts, drafts or transforms
Unstructured input, human-readable output
Current context and usable knowledge
AI assistant
Helps a person do the work
The judgment should stay human but the legwork should not
Context at the point of work
AI agent
Performs defined work toward an objective using tools
Multi-step work with a clear boundary and a measurable outcome
Permissions, tools, oversight and an owner
Agentic workflow
Several agents coordinating across steps and systems
End-to-end process work
All of the above, plus governance
Agents are the most requested option, so they often get proposed for problems an automation would solve more cheaply and more safely. The reverse happens too: conventional automation drops out of the conversation once it turns to AI, and the simplest win goes unbuilt. Discovery keeps both on the table, so you get the fastest safe win first.
The cheapest AI project is the one you decide not to run.
Related: assistants and agents on the platform → ServiceNow AI. When the systems are the problem → ServiceNow Integrations. When the process should change first → Business Process Consulting and Design.
Already have a list of AI ideas?
Bring it. We will tell you which ones need AI at all.
Every use case described the same way, so you can compare them
Every candidate is written up in the same format. That consistency is what makes prioritization work.
Problem
What is going wrong, in one sentence
Who
Whose work it affects
Trigger
What starts it
Input
What information it works from
AI role
What the AI is actually doing
Permitted action
What it may change, and what it may not
Human role
Where a person stays in the loop, and why
Data required
What it needs, from where, under whose permissions
Outcome
What should be different afterwards
Metric
How anyone would know it worked
If nobody can name how success will be measured, the use case is not ready to prioritize or fund yet.
Fund the use cases that are valuable and deliverable
Discovery produces a value hypothesis: a range, the assumptions behind it, and how it will be measured afterwards. For mid-market operations the most credible measure is usually capacity released from low-value work, rather than headcount reduction, because it is measurable and it holds up in procurement. Cycle time, cost to serve, quality and error rate, risk reduction and revenue impact are all in scope where the process supports them.
We test each use case across a broad set of feasibility dimensions, and the pattern is consistent: these decide feasibility far more than model capability does.
AI Use Case Discovery produces a value hypothesis, which is enough to compare use cases against each other. Roadmapping produces the business case, which is enough to decide an investment.
The model is rarely the hard part.
Prepare foundations
Valuable and not yet deliverable. You get the list of what must change first, which often creates more value than the use case itself.
Do now
High value, feasible, ready enough. Usually one to three use cases.
Stop
Low value, low feasibility, or the risk outweighs the return. Naming these protects budget for the use cases that deserve it.
Validate next
Valuable and plausible, with one open question that a short proof would answer.
Every position comes with its reasoning, so the decision holds up in front of a board. We keep the reasoning visible instead of collapsing it into one blended score, which would only start an argument about weightings.
Five readiness checks, and the model is not one of them
Process
Is the process consistent across teams, are the exceptions documented, are the decisions defined, and does everyone agree what a good outcome looks like?
AI amplifies the process it enters. On a stable process it compounds value; on an inconsistent one it produces inconsistency faster.
Data
What data it needs, where it lives, who owns it, whether it is trustworthy and reachable under existing permissions, whether history is required, whether the knowledge is current, and whether it contains sensitive information.
Generative quality depends on the knowledge behind it, so data is checked first, not later.
Technology
Where the process runs, what the AI must read and write, how much integration is required, and whether the platform you already own can do it.
Predictive approaches need real history in your instance; generative ones largely do not.
Governance
What the AI may decide and do, which actions need approval, what must be logged, what happens when it is uncertain, and whether the decision is regulated.
We flag it in discovery, and it is designed in the governance work that follows.
Ownership
A named business owner and a named technical owner who both know they hold the role.
This is the check that fails most often, and closing it is a business decision rather than a technical one.
Related: whether the data is reachable → ServiceNow Integrations. Governance and oversight → AI Control Tower. Regulated decisions → EU AI Act Readiness.
ServiceNow, Claude, conventional automation, or a combination
We implement ServiceNow and Anthropic Claude, and we choose per use case. ServiceNow orchestrates the workflow and carries the governance. Claude brings the intelligence. You get the honest answer rather than the convenient one, and sometimes that is technology we do not implement at all.
Conventional automation or integration
When the rules are stable and the outputs should be deterministic, or when the real problem is that systems do not talk to each other. Cheaper, more predictable and easier to audit.
ServiceNow AI and agents
When the process already runs on ServiceNow, the work updates ServiceNow records, enterprise permissions matter, or governance should sit next to the workflow.
Anthropic Claude
When the problem is knowledge work: reasoning across unstructured material, synthesis of documents or policy, or work that spans enterprise knowledge rather than one process.
A combination
Often the strongest answer: Claude for knowledge and reasoning, ServiceNow for governed execution, connected by design. This is our own decision logic, applied use case by use case.
ServiceNow's AI Agent Advisor can mine instance data for automation candidates, and we use it where it applies. It works from real record history, so it adds most on an established instance. Discovery adds what the tooling cannot judge: whether the business should solve the problem with AI, whether the process is stable enough, and who owns the outcome. We validate the platform's advisory tooling against your current release before we rely on it.
AI Use Case Discovery decides which AI opportunities are worth doing. ServiceNow AI builds and governs the ones you choose, inside the business process.
Examples of what discovery finds
Opportunity and account summarization, so sellers spend less time assembling context.
Quote assistance where configuration and pricing rules are complex enough that expert time is the bottleneck.
Case summarization and resolution notes, which work from the first ticket because they read the current case rather than learning from history.
Triage and routing, where the value depends on whether your case taxonomy and customer data are reliable.
Project and portfolio summarization for status reporting that currently consumes manager time.
Demand and work item analysis, turning requests into structured, comparable items.
We keep this list short on purpose. Your strongest use cases come from your own strategy and your own work.
A shortlist you can fund, and the reasoning behind it
A long list of opportunities is easy to produce, and it hands the hard decision back to you. Discovery gives you a short list you can act on, the reasoning behind each position, and a clear set of candidates we recommend not pursuing, with the reasons. That second list often protects the most budget.
We run the same method on our own work.
On our configuration and pricing work we scored four candidate areas against the same five questions, logged the evidence line by line, and took exactly one forward as a deliberate pilot. The decision about what to build came before the build.
You do not need fifty AI ideas. You need to know which three deserve investment, and why.
Seven stages, in this order for a reason
Align on strategy and the business problem
What the business is trying to achieve, what is being lost, where, how often and what it costs. No technology in this conversation yet.
Stages 2, 5 and 6 use process evidence, data checks and a look at your platform, which is why the output goes further than an ideation session. You get decisions you can fund and a backlog your team can start building from.
We build the AI we help you choose
Business first, technology second.
We start with what needs to improve, and every use case has to state its problem and its metric before it gets scored.
We know AI and the operations it enters.
We implement ServiceNow across commercial, customer and project operations, and Anthropic Claude alongside it. We build and run AI agents, in our own operations and in client environments. That matters because most AI use cases succeed or fail on the operational layer: process variation, data quality, permissions and ownership.
We recommend the simplest thing that solves the problem.
Including technology we do not implement. We name the leading system per capability and put it in writing, including the capabilities we think should stay outside ServiceNow.
Discovery detailed enough to build from.
Our design question sets across customer service, sales and order management, and workforce management run to hundreds of structured questions, with the standard behavior documented against each, in English and German. Implementation starts from the output, without redoing a slide deck.
The people who run your discovery
The senior practitioners who run your discovery have built automation and value-realization practices before, at scale. They bring a method rather than a template: structured question sets in English and German across three ServiceNow modules, and a driver-based approach to scoping and estimation.
They are the same people you meet in the first conversation, with no account layer and no handover to a team you have never met.
Build the AI that deserves the investment
Bring us your strategy, the AI ideas already on the table and the pain points you most want solved. In a first conversation with the practitioners who would run your discovery, we look at what you are considering, what you already own, and where AI will make the biggest difference to your business.
Value hypothesis
A range of expected value with its assumptions stated, enough to compare options. Not a business case.
Predictive AI
Learns from history to classify or forecast.
Generative AI
Reads, summarizes, extracts or drafts from current context.
AI assistant
Helps a person do the work, while the person keeps the decision.
AI agent
One piece of AI given a job, a set of tools and a boundary.
Agentic workflow
A business objective handled by several agents working in sequence.
Exception rate
How often the process does not follow the normal path.
Capacity release
Time freed inside an existing team.
Human oversight
A named person with the competence and authority to intervene.
LLM, large language model
The AI model itself, for example Claude.
The questions buyers ask us.
What is AI Use Case Discovery?
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A structured engagement that starts with your business strategy and pain points, then identifies and assesses AI use cases against consistent criteria: the business problem, whether AI is the right instrument, what kind of AI fits, data and permissions, human oversight, value, feasibility and risk. You leave with a prioritized backlog, the reasoning behind it and a recommended first move.
How do you identify good AI use cases?
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By starting with the strategy and the work, then looking for candidates. Discovery maps how a process actually runs, then looks for specific signals: high-volume repetitive work, unstructured information a person currently reads to make a decision, manual triage and routing, knowledge retrieval, repeated decision patterns, slow handoffs, manual content creation, exceptions that need investigation, and inconsistent execution between teams. A good candidate is one where the work is real, the volume is known, the outcome is measurable and someone owns it.
How do you prioritize AI use cases?
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Against business value and implementation feasibility together, with readiness and risk as overlays. Every candidate is defined in the same format first, because inconsistent definitions cannot be compared. Use cases fall into four outcomes: do now, validate next, prepare foundations, or stop. Each position keeps its reasoning visible, so the decision holds up in front of a board.
How do you calculate the value of an AI use case?
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We build a value hypothesis: a range, the assumptions behind it, and how it would be measured after go-live. For mid-market operations the most credible dimension is usually capacity released from low-value work, alongside cycle time, cost to serve, quality and error rate, and risk reduction. AI Use Case Discovery produces a value hypothesis, which is enough to compare use cases against each other. Roadmapping produces the business case, which is enough to decide an investment.
How do you know whether a process is ready for AI?
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Discovery checks for the signals that a process needs work first: the process differs by team, ownership is unclear, exceptions are undocumented, input data is inconsistent, decisions have never been defined, or nobody agrees what a good outcome looks like. AI amplifies the process it enters, so an inconsistent process produces inconsistency faster.
What is the difference between AI automation and AI agents?
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Automation: a known trigger produces a known action, deterministic and auditable. An AI agent: defined work toward an objective using tools and context, inside a permission boundary, more capable and more consequential. Between them sit assistants, which help a person do the work while the person keeps the decision. The practical risk is that agents get proposed for problems an automation would solve more safely.
Does every AI use case need generative AI?
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No. Generative AI reads, summarizes, extracts and drafts from current context. Predictive AI classifies and forecasts from historical patterns and needs real volume and history. Plenty of problems need neither: they need rules-based automation, an integration, or a process that should change first. Discovery records which applies and why.
How do you decide whether to use ServiceNow AI or Claude?
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By the nature of the work. ServiceNow fits when the process already runs there and enterprise permissions, records and governance matter: it orchestrates the workflow. Claude fits when the problem is knowledge work: reasoning across unstructured material, synthesis, or work spanning enterprise knowledge rather than one process. Often the strongest answer is both, connected by design. Sometimes it is conventional automation.
What happens after AI Use Case Discovery?
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Each surviving use case leaves with a named route: implementation for ServiceNow process work, integrations for data reachability, AI Control Tower for governance, EU AI Act readiness for regulated decisions, Claude for knowledge work, process design where the process should change first, and roadmapping when more than a handful of use cases survive. Where nobody in your team has taken this stack into production yet, a Forward Deployed Engineer can carry the first use case end to end.