Assess and Prioritize Agentic AI Use Cases in Professional Services Organizations

Assess agentic AI use cases with a triage model that connects fit, guardrails, and value.

Analyst perspective

Deploy agentic AI where firms can gain leverage without creating unmanaged risk.

In professional services, agentic AI is not primarily a technology decision. It is a workflow governance decision. Professional services organizations (PSOs) do not struggle because they lack ideas on where to apply AI. They struggle because they have not yet standardized how to judge fit, assign accountability, and distinguish where autonomy is appropriate versus where Gen AI, workflow automation, or process redesign is the better solution path.

In client-facing, judgment-heavy workflow contexts, the real issue is not whether an agent can do the work but whether the firm can safely contain the agent work without disrupting its business model. Successful firms that move fastest will be the ones that anchor their AI decisions in business capabilities, apply explicit safety and suitability gates, and prioritize only the opportunities that improve delivery, economics, and trust.

In this research, we assess agentic AI through a professional services lens, using real workflow patterns, case examples, and a structured evaluation model to uncover viable opportunities. Agentic AI in PSOs should be viewed as a controlled redesign of professional workflows at a system level, not as a generic productivity experiment using point solutions. The key decision is where autonomy can be safely allowed without weakening accountability, client trust, or commercial fit. However, culture and governance capacity will dictate whether the initiatives will scale.

Kassim Dossa
Research Director, Retail Industry
Info-Tech Research Group

Executive summary

Your Challenge

CIOs are being asked to evaluate agentic AI before the boundaries of fit, autonomy, or business value are fully settled.

• Determine when judgment-heavy workflows require agentic AI versus Gen AI, RPA, or simply redesign.
• Understand where autonomy can be allowed and where human accountability must remain in workflows.
• Confirm that use cases fit the firm's economics, client expectations, and service model.

As a result, use case triage is inconsistent and PSOs struggle to approve agentic AI initiatives with confidence.

Common Obstacles

Most PSOs are not blocked by a lack of use case ideas but by inconsistent evaluation conditions.

• Use cases are framed as technology-first experiments rather than workflow decisions.
• Autonomy and oversight thresholds are not well defined.
• ROI, ownership, and client/service-model constraints are surfaced too late.

Until firms standardize their approach, autonomy thresholds, and authority controls, they will struggle to shift agentic AI into professional delivery contexts.

Info-Tech's Approach

When CIOs align teams to define practical AI use cases, they reduce risk and avoid stalled adoption. Info‑Tech helps you align teams around practical agentic AI use cases by:

• Tying all potential use cases in the reference architecture to value streams and capabilities.
• Clarifying where agentic AI fits in professional service workflows by confirming use case safety and agentic suitability.
• Assessing and prioritizing remaining use cases across business impact and implementation feasibility dimensions.

Info-Tech Insight

Agentic AI creates value in PSOs only when firms replace isolated assessments with a consistent use case triage model that links workflow fit, autonomy guardrails, and proven business value.

Your challenge

Agentic AI evaluation is difficult because PSOs must classify the right workflow, set acceptable autonomy limits, and justify business fit in parallel.

• Firms lack clear criteria to distinguish when judgment-heavy workflows are a fit for autonomous reasoning rather than Gen AI or rules-based automation.
• Evaluations remain tool-first instead of pressure testing agents to see if they can operate across realistic PSO workflows that span disparate silos like document management systems, CRM, and billing functions.
• The level of acceptable autonomy varies widely across tasks, professions, and client context, making a standard approval approach difficult to implement.
• Defining authority and accountability for outcomes of agents that work across systems and silos.
• Commercial fit of use cases is difficult to confirm, as use of agents has implications on service mix, pricing logic, and client expectations in parallel.

Less than 10% of professionals would be comfortable allowing AI to make final decisions on complex matters. This reality will bound the acceptable level of autonomy for agentic use cases under consideration.

40% of professionals have been told to both use and not use AI on client work. This level of conflict heightens the risk of agentic AI being used in contravention of client instructions.

Common obstacles

These barriers hinder the ability of firms to assess and approve agentic AI use cases on a consistent basis.

• Teams continue to conflate agentic AI with Gen AI, leading to vague proposals that fail to define specific professional workflow boundaries.
• A lack of firm-wide standards regarding autonomy thresholds based on work product type creates inconsistent, practice-area-specific risk tolerances.
• Use cases are often supported using proxy ROI data focused on internal efficiency metrics rather than decision-grade evidence that supports business impact. This is further compounded by the fact that the blank-check funding era is ending.
• It's difficult to manage agentic use cases that are operationally successful but commercially disruptive.
• Siloed, function-based agent creation (i.e. for marketing or legal) leads to AI agent sprawl.
• Data and AI literacy gaps hinder agent effectiveness and scalability.

82% of professionals say their organizations do not measure AI ROI or are unsure if they do. Your agentic AI selection criterion must force alignment to a specific, measurable commercial outcome.

Source: Reuters, 2026

Expect agentic AI to move quickly from exploration to workflow integration

The adoption curve is steepening.

Agentic AI is defined as an AI-enabled system that can plan, decide, and act across multistep workflows using tools and feedback to progress work toward a defined goal.

Build a triage model for PSO agentic AI use cases

Core Problem

PSOs need a structured way to determine which workflows are safe, suitable for agentic AI, and worth prioritizing.

KEY INSIGHT

In PSOs, agentic AI is primarily a workflow governance decision. Firms create value when they apply explicit gates for safety, suitability, and prioritization before scaling use cases into delivery.

Info-Tech's methodology for determining where agentic AI fits in PSO firms

Insight summary

Agentic AI in PSOs requires workflow discipline

The strongest use cases are grounded in real service workflows, bounded by review, and prioritized for business fit, delivery value, and firm readiness. However, it's also important to consider optimizing your current workflow designs for AI agent capabilities.

Map to real workflows

Use cases should be anchored in named value streams and business capabilities. This approach exposes where the workflow sits, who owns it, which systems and handoffs it touches, and how its value will be measured across delivery, growth, and operations.

Use agents to absorb coordination workload

Early agentic wins come from workflows that are cross-step, repetitive, and coordination-heavy, such as routing, retrieval, follow-up, exception handling, and documentation assembly. These use cases increase delivery leverage around professionals without automating judgment.

Gate autonomy by exposure

Client-facing outputs, regulatory exposure, reviewability, and workflow coupling should determine how much autonomy is allowed. In PSOs, acceptable autonomy varies by task, profession, and client context.

Measure value beyond productivity

Measure impact on delivery leverage, quality, trust, margin, and client outcomes, not just internal productivity signals.

Pressure test commercial fit

Confirm use case implications early for pricing, service mix, client expectations, and the broader firm business model before expanding scope.

Research deliverable

Each step of this research is accompanied by supporting deliverables to help you accomplish your goals:

Agentic AI Use Case Tool for Professional Services Organizations

This tool provides the structure to identify agentic AI use cases, score each one across business value and feasibility, and build a prioritized portfolio, enabling teams to confidently select the highest-impact opportunities ready to move into the Build Your Agentic AI Prototype phase.

Measure the value of this research

Leverage this research's approach to ensure your agentic AI use cases align with and support your key business drivers and accelerate time to value.

Staffing case study

Agentic AI candidate screening

INDUSTRY: Staffing & Recruiting
SOURCE: Adecco Group Website

Challenge

Adecco faced mounting pressure to manage a high-volume, time-sensitive recruiting workflow as candidate expectations increasingly demanded faster, more responsive engagement. At the same time, the organization needed to protect service quality and candidate experience, a challenge that required freeing recruiters from administrative burden without removing human judgment.

Solution

Adecco deployed an AI prescreening workflow that interviews candidates by SMS or email, records responses in Salesforce, summarizes the interactions, and routes completed cases back to recruiters. Recruiters review transcripts and decide who advances in the hiring process.

Results

Using this new workflow, Adecco helped 28 companies fill vacancies, contacted thousands of candidates, and achieved a 4.6/5 candidate satisfaction rating. It also reported that 57% of candidate conversations occurred outside standard working hours. In later scale disclosures, Adecco said the workflow delivered 15% time savings, significantly reduced time to fill, increased fill rates, and lowered cost to serve.

Law firm case study

INDUSTRY: Legal
SOURCE: A&O Shearman Website

Challenge

Major law firm A&O Shearman faced multihour, deadline-sensitive workflows in areas such as antitrust filings, fund formation, cybersecurity, and loan reviews. All of these high-value areas required deep legal expertise and multistep processes. These workflows were taking 5-10+ hours of senior lawyer time per matter.

Solution

Working in partnership with Harvey AI, A&O built a series of agents, embedding the firm's own lawyer's expertise. Each of the agents were configured for a specific, high-complexity practice domain. For all agent outputs, human review was part of the workflow. Additionally, A&O uses these tools internally and also sells them commercially to clients and peer firms, sharing software revenue with Harvey AI.

Results

A&O's Harvey-based agents are already reducing the time spent on deadline-sensitive legal workflows from hours to minutes. The initial rollout covers high-value work in antitrust, cybersecurity, fund formation, and loan review, and the firm has gone beyond internal use by selling these solutions to their clients and other law firms.

Workshop overview

Phase 1

Define & Anchor the Context

This phase will walk you through the following activities:

• Review your organization's business goals, key initiatives, and capability maps to anchor AI planning in real, firm-level priorities.
• Explore the purpose and structure of AI use cases to build a common understanding of how they support core processes and systems.

This phase involves the following participants:

• AI initiative lead
• CIO
• Other IT/practice leadership
• Senior business leader and managers accountable for AI initiatives

1.1 Align on fundamental agentic AI concepts

1-2 hours

1. Gather the AI strategy creation team and introduce the concept of agentic AI, systems that are goal driven, tool using, and adaptive, distinguishing them from simple automation and AI assistance.
2. Walk through the four agentic AI capability patterns that repeatedly deliver value across industries.
3. Discuss how real-world agents combine multiple patterns in their workflow and establish a shared vocabulary your team will use going forward.

Position agentic AI on the automation spectrum

From rules to agents

Agentic systems exist on a progression of autonomy

How much authority should your agent have?

Ensure agentic AI use cases meet the safety benchmark before prioritizing value

Before evaluating business impact or feasibility, organizations must first determine whether a use case is structurally appropriate for agentic AI within a professional services context. This step ensures that operational realities, safety constraints, and system dependencies are accounted for before any further evaluation occurs.

Not every workflow requires agentic AI

While agents offer powerful automation opportunities, they are not a universal solution.

After confirming the use case is structurally appropriate, assess whether introducing autonomy into a judgment-driven workflow is warranted or whether a workflow redesign, Gen AI, or traditional automation is the better approach.

Multistep Coordination

Does the workflow require orchestration across roles, systems, or client interactions?

Statefulness

Does the workflow require persistent context across interactions, files, or client history (vs. a single task or prompt)?

Dynamic Decisions

Does the workflow involve professional judgment, ambiguity, or context-driven decisions (vs. fixed rules)?

Actionability

Does the workflow require the system to take actions across tools or processes (vs. just generate outputs)?

Repeatable Triggers

Is the workflow economically meaningful and frequent/structured enough to initiate reliably?