Jesse Lee Coggan - AI Adoption Strategist
AI creates no shortage of possibilities for professional services firms.
Document preparation, research, client communication, knowledge retrieval, reporting, and administrative work can all appear to be candidates for AI support.
But an opportunity isn’t automatically a priority.
In its second-quarter 2026 survey, Statistics Canada reported that 32.4% of businesses in professional, scientific and technical services had used AI to produce goods or deliver services during the preceding 12 months. That measures adoption, not whether an individual workflow creates commercial value.
A repetitive workflow may have little commercial significance. A time-consuming task may not be suitable for automation. And an impressive demonstration may reveal little about whether a capability belongs in everyday operations.
For firm leaders, the challenge isn’t identifying every place AI could be used.
When a new AI capability becomes available, it’s tempting to search for somewhere to apply it.
A more commercially useful starting point is:
Which business objective are we trying to advance, and how could improving a particular workflow contribute to it?
For a professional services firm, objectives might include:
These objectives establish what the firm wants to achieve.
Reducing document preparation time, improving knowledge access, or shortening turnaround times are different. They’re operational improvements that may contribute to commercial outcomes.
For example, reducing the time spent preparing client deliverables might increase engagement profitability or release capacity for additional paid work.
But recovered time doesn’t automatically become revenue. The firm must be able to translate that capacity into a meaningful business result.
This is why AI prioritization requires more than identifying inefficient tasks.
Sometimes the better answer is a simpler process, clearer ownership, improved templates, or conventional automation.
Business objectives provide direction, but they’re too broad to evaluate as individual AI opportunities.
“Increase client lifetime value” identifies an intended commercial result.
“Use AI in client service” suggests a general area of activity.
Neither defines what would actually change.
“Help prepare an initial draft of a recurring client status report using approved project information” is more concrete.
Leaders can examine its information sources, process steps, professional review requirements, and current delivery effort. They can then assess whether improving it would meaningfully support profitability, client retention, or another objective.
Before evaluating an opportunity, describe the workflow in plain language: When something happens, who does what, using which information, to produce what result?
If the workflow is unclear, the team may need to understand the process before deciding whether AI belongs in it.
There isn’t one universal scoring model that works for every professional services firm.
A useful evaluation should make trade-offs visible rather than create the appearance of mathematical certainty.
These nine questions provide a practical basis for comparing opportunities.
Identify the commercial result: revenue growth, profitability, client lifetime value, brand performance, retention, or market expansion.
Then establish how the proposed workflow improvement could contribute.
If the connection isn’t credible, the opportunity needs further examination before receiving investment.
Define the workflow’s starting point, activities, information inputs, outputs, and handoffs.
Identify which activities AI might support and which decisions must remain with people.
A clearly bounded workflow is easier to evaluate, test, and improve than a broad ambition to “use AI” across a department.
Examine how often the workflow occurs, where delays or rework arise, and how those problems affect delivery costs, revenue opportunities, or client relationships.
High frequency can increase the value of an improvement, but frequency alone doesn’t determine priority.
An occasional activity may still warrant attention when its commercial consequences are substantial.
Estimate how improving the workflow could contribute to the objective.
Potential value might include:
Account for implementation costs, ongoing oversight, training, and review.
An operational improvement is evidence of better workflow performance, not automatically proof of financial return.
The business case should distinguish expected value from value actually demonstrated.
Professional services workflows frequently involve confidential client information, financial records, proprietary knowledge, or commercially sensitive material.
Determine which information the system would access, where it would be processed, and whether the proposed permissions satisfy contractual and professional obligations.
A commercially attractive workflow may be inappropriate for a particular AI tool or deployment approach.
AI can help organize evidence, summarize documents, prepare drafts, or identify inconsistencies.
That doesn’t mean it should independently approve commitments, determine professional advice, or make consequential decisions.
Identify the workflow owner and the person accountable for its outputs.
If decision authority is unclear, resolve it before implementation.
Review requirements should reflect the nature of the work and the consequences of error.
An internal working draft may require different oversight from client-facing advice or a decision involving sensitive information.
Specify what must be checked, who performs the review, and when approval is required.
Human oversight should be designed into the workflow rather than added as an informal safeguard afterward.
For practical review, approval, and escalation points, read Human Oversight in AI Workflows.
A successful demonstration isn’t the same as a successful business outcome.
Measurement should distinguish between two levels.
Operational performance
Commercial performance
Not every pilot can demonstrate changes in revenue or CLV within a short evaluation period.
Where commercial effects take longer to emerge, establish credible intermediate indicators without presenting them as realized financial results.
Measure the complete workflow, including the time spent checking and correcting AI-assisted outputs.
Agree on the decision criteria before testing begins.
The firm might:
These decisions should reflect both operational results and the strength of the commercial case.
Once the nine questions have been answered, leaders can compare opportunities through three dimensions.
Business value: How meaningfully could this opportunity advance a defined commercial objective?
Operational feasibility: Are the workflow, information, ownership, permissions, and resources sufficiently clear to support testing?
Consequences and controls: What could go wrong, what would the consequences be, and what safeguards would be necessary?
A high-value opportunity with unresolved information permissions may need further preparation.
A modest opportunity with clear boundaries and measurable results may be a useful initial test.
A technically simple activity with little commercial significance may not warrant investment.
These judgments don’t require an elaborate scoring model. They require transparent reasoning about value, readiness, and consequences.
Consider a hypothetical professional services firm evaluating three AI opportunities.
| Opportunity | Business objective | Key consideration |
|---|---|---|
| Assist with recurring client reporting | Improve engagement profitability and client retention | Could reduced delivery effort or improved responsiveness produce measurable value? |
| Support analysis of sensitive client files | Improve engagement profitability and increase revenue from additional client work | Can confidentiality, reliability, and professional oversight be managed appropriately? |
| Generate ideas for occasional marketing campaigns | Strengthen brand performance and qualified lead generation | Could the activity materially improve business-development outcomes? |
The first opportunity may be a reasonable initial candidate if the firm can define its inputs, review requirements, and expected contribution to profitability.
The second could offer greater commercial value but requires more careful evaluation of confidentiality and professional judgment.
The third may be easy to demonstrate while offering limited business value unless it connects to a substantive marketing or growth initiative.
Different firms could reasonably prioritize these opportunities differently.
After identifying promising opportunities, the firm needs an intentional sequence.
Launching several unrelated initiatives simultaneously can fragment resources and make results difficult to interpret.
A more disciplined approach is to select a limited number of workflows with:
The first initiative doesn’t necessarily need to be the simplest. It should provide sufficient value and clarity to justify a controlled test.
As evidence develops, the firm can strengthen its approach to information access, oversight, training, and implementation before expanding into more demanding workflows.
An AI adoption framework helps connect those individual decisions to an intentional adoption sequence.
For a related perspective on what happens when those decisions are missing, read why AI pilots stall in professional services.
Begin with the business objectives that matter most.
Identify a manageable shortlist of workflows that could materially contribute to those objectives, then evaluate their value, feasibility, ownership, information requirements, and measurement approach.
Some opportunities may be ready for testing. Others will require additional preparation or won’t justify investment.
The result should be a defensible set of priorities rather than another collection of disconnected AI experiments.
The objective isn’t to find the greatest number of places to use AI. It’s to identify where AI could advance the firm’s business objectives and invest where the evidence justifies it.
If your firm has several AI ideas but no agreed way to determine which should come first, the next step is to establish a clearer starting position.
The AI Adoption Clarity Diagnostic is a 10-question, multiple-choice questionnaire that takes less than five minutes to complete. It helps surface the priorities, constraints, and decisions that deserve attention before further commitment.
Complete the Diagnostic to receive your Clarity Brief by email within minutes — a structured starting point for deciding what should happen next.