Where Should Your Business Begin with AI? Start with the Business Problem
By Joe Marrino, PMMA Advisory Group · Published · Last updated
Short answer
Begin with a valuable business problem, not a software purchase. Look at where work is slow, manual, or inconsistent and where revenue is being lost, then evaluate each AI opportunity by business value, feasibility, data, readiness, risk, adoption, cost, and measurability. Start with the one that can become a reliable operating capability.
Where to begin with AI at a glance
- Direct answer
- Begin with a valuable business problem, not a software purchase.
- Evaluation
- Score value, feasibility, data, readiness, risk, adoption, cost, and measurability.
- Operating principle
- Build a reliable capability with clear ownership and human review.
- Best next step
- Explore an AI Growth and Efficiency Assessment
Why starting with a tool usually stalls
Most leaders now agree AI matters. The trouble starts when the first step is choosing a product. A tool gets licensed, a few people try it, and six months later it is still an experiment. The problem was never defined, nobody owns the new process, and there is no way to tell whether anything improved. PMMA sees the same pattern again and again: AI pilots that do not become reliable operating capabilities. The fix is not a better tool. It is a better starting point.
Start with the work, not the technology
A useful AI opportunity almost always hides inside an everyday business problem. Common examples:
- Leads that are not contacted or followed up consistently.
- Employees coordinating critical work through email and spreadsheets.
- The same information typed into several systems.
- Slow or unclear handoffs between departments.
- Management reports that arrive late or do not help decisions.
- Onboarding that depends on one or two experts.
- Knowledge held by a few long-tenured employees.
Pick the problem that costs the most time, revenue, or risk. That is your candidate.
How to decide which AI opportunities are worth pursuing
Once you have a short list of problems, weigh each one against the same criteria:
| Criterion | Question to ask |
|---|---|
| Business value | What does solving this change for revenue, cost, capacity, or quality? |
| Feasibility | Can it be done with the people, systems, and time available? |
| Data availability | Is the information the process needs available and reliable? |
| Organizational readiness | Will the team that owns the process support the change? |
| Risk | What happens if the output is wrong, and who reviews it? |
| Adoption requirements | What training or habit change does it need? |
| Cost | What will it take to build, run, and maintain? |
| Measurability | How will you know it worked? |
The goal is not the most impressive use case. It is the one most likely to become part of how the business actually runs.
Do you need to replace your existing systems?
Usually not. Before recommending anything new, find out what already works. Many improvements come from redesigning the process, connecting systems you already pay for, adding targeted automation, improving reporting, or building a small focused application. Replacing every platform is rarely the first step. See how PMMA approaches AI process and workflow optimization.
Common mistakes when starting with AI
Buying software before defining the problem.
A license cannot supply a missing business case, process owner, or measure of success.
Automating a broken process as it is.
Automation can make inconsistency move faster. Redesign the work before automating it.
Trying to automate everything, including decisions that need human judgment.
Keep review and accountability where errors would have meaningful consequences.
Skipping training and adoption, so the new process never sticks.
A technically sound system produces little value if people do not understand or use it.
Not deciding up front how success will be measured.
Define the operating and business indicators before implementation begins.
A practical first-steps sequence
- Name the problem and who owns it.
- Map how the work happens today.
- Score the opportunity against the criteria above.
- Design the future process, including where people review AI outputs.
- Implement a focused first version.
- Train the people who use it.
- Measure adoption and impact, then decide whether to scale.
The sequence matters more than the calendar. Some steps take days, others take weeks, depending on scope.
Keep people accountable for important decisions
AI is best used to expand capacity, reduce repetitive work, and improve access to information. It does not need to make every decision. Important, sensitive, regulated, or high-consequence decisions should keep human review and clear accountability. Building that in from the start makes adoption easier and risk lower.
Where to go from here
If you know where the friction is, an AI Growth and Efficiency Assessment turns that list into prioritized, commercially defensible use cases. If lost leads are the most urgent problem, start with sales acceleration and revenue operations. If leadership needs help connecting AI to a wider set of initiatives, executive advisory is the better entry point.