Building AI into a product is easy to get excited about and hard to get right. Most teams either start with the technology and work backward to find a use for it, or they identify real problems but generate ideas without any grounding in what AI can actually do today. Neither produces a roadmap worth building.
The approach that works: map the user journey, identify the pain points worth solving, then ideate and prioritize AI solutions against those problems. That is what this post covers.
Know What AI Can Actually Do
Before your team brainstorms solutions, everyone in the room needs a working understanding of what AI can and cannot do today.
The landscape changes fast. A team working from assumptions formed 12 months ago will dismiss ideas that are now straightforward to build, or pursue ones that remain out of reach. Staying current means following credible practitioners, reading release notes, and occasionally testing new capabilities directly. The questions you are trying to answer are practical: can this problem be solved with existing approaches, does AI offer something meaningfully better, and if so, what?
This does not need to be exhaustive. It needs to be current enough that the ideas your team generates are grounded in what is actually possible.
Diverge Before You Converge
Ideation works in two phases: diverge first, converge second.
In the diverge phase, the goal is volume. Generate as many ideas as possible without filtering for technical or business constraints. Judgment kills early ideation. An idea that sounds impractical at first often seeds a better one.
There are many ideation techniques to choose from. Select ones that push participants toward creative thinking rather than defaulting to obvious solutions. Your team’s working knowledge of AI capabilities is what keeps the ideas tethered to the realm of the possible.
Two techniques that encourage creative stretch work well here. A design charrette brings the full team together in a short, structured session where everyone generates ideas independently before sharing them. The second technique is to set a numeric target: 25 ideas in 15 minutes, for example. The constraint forces the group past the obvious answers. The first ideas in any session tend to be the ones everyone already had. The pressure to reach 25 surfaces the ones worth examining.
Take a support agent who faces this pain point: searching four separate systems for every ticket. A diverge session might produce ideas like a unified search interface, auto-suggested responses based on past tickets, or a real-time assistant that surfaces relevant articles without the agent asking. Some of these are clearly more feasible than others. That is not a reason to exclude them during divergence.
For additional structured techniques, this post on facilitation methods that get everyone talking covers several worth knowing.
In the converge phase, the team clusters related ideas, identifies the strongest candidates, and begins applying real constraints. The output is a shortlist, not a final decision.
Prioritize on Impact and Feasibility
With a shortlist in hand, evaluate each idea on two dimensions: impact and feasibility.
Impact has two components. User impact asks whether solving this problem saves meaningful time, reduces significant effort, or materially improves the quality of work. A useful test is whether your solution acts as a painkiller or a vitamin. Painkillers address problems users are already motivated to solve. Vitamins improve quality of life but get cut when budgets tighten. Ideas that clear the painkiller test generate adoption in ways that nice-to-have improvements do not.
Business impact asks whether building this use case aligns with your organization’s strategic priorities. Does it strengthen your market position, support customer retention, reduce churn, or move revenue? Ideas that connect directly to what your business is trying to achieve are easier to fund, easier to defend, and more likely to survive the next planning cycle.
Feasibility assessment for AI involves questions that general product development does not always ask:
- Training data: Does the data needed to train or fine-tune a model exist, and is it accessible?
- Accuracy requirements: How consistent does the output need to be? A suggestion the agent can accept or ignore tolerates more variance than a decision the system makes autonomously.
- Additional components: Does the idea require retrieval systems, integrations, or infrastructure the product does not currently have?
- Volume: At expected usage levels, does the economics of the solution hold?
Applying those three dimensions to the support agent ideas produces a clear picture:
| Idea | User Impact | Business Impact | Technical Feasibility |
|---|---|---|---|
| Unified search across all knowledge sources | H | H | M |
| Auto-suggested response based on similar past tickets | H | H | M |
| Real-time article retrieval while agent types | H | M | M |
| Automated ticket categorization on arrival | L | M | H |
| Resolution consistency scoring across agents | M | H | L |
Unified search and auto-suggested responses score highest across all three dimensions and are the natural candidates to prototype first. Automated ticket categorization is technically straightforward but delivers limited user value in this context. Resolution consistency scoring addresses a real business problem but presents significant feasibility challenges: measuring consistency across agents requires a reliable ground truth the system may not have.
For a more structured approach to scoring, ICE, RICE, and weighted scoring frameworks are worth knowing.
Prototype Before You Commit
Scoring ideas on a matrix narrows the field, but it does not tell you whether a model will handle the use case reliably. Before committing to build, prototype quickly. Most AI platforms expose an API. Take your highest-priority idea, construct a realistic prompt using real examples of the problem, and run it against the most common cases you expect the system to encounter. This takes hours, not weeks.
The prototype surfaces things no prioritization exercise predicts: whether the model handles edge cases gracefully, where it produces confident but wrong output, and whether the quality is consistent enough to help users or variable enough to create new problems. The goal is not a production build. It is a fast signal about whether the idea holds up before your team invests in it.
That signal tells you whether the technology can do the job. It does not tell you whether users will find value in it. A design sprint answers that question before any significant build investment begins.
