The Playbook for Auditing AI Opportunities (Q2 2025 Edition)

In Arete Coach’s article “The Playbook for Auditing AI Opportunities (Q2 2025 Edition),” author Severin Sorensen offers a practical framework for leaders who want to move beyond AI curiosity and identify where AI can create measurable value now. Published April 4, 2025 and updated April 12, 2025, the article argues that companies should stop chasing every new AI trend and instead run a structured audit of business processes, friction points, automation opportunities, pilots, ownership, and monthly review habits.

For business leaders, the piece is a useful operating guide: AI adoption should begin with work, not tools. Sorensen recommends mapping recurring processes, identifying bottlenecks, spotting high-leverage use cases for AI agents, testing off-the-shelf tools by function, running focused pilots, upskilling employees, assigning ownership, and revisiting progress monthly.

Executive summary for business leaders

Overarching theme: AI opportunity is best discovered through disciplined operational inquiry. The article’s central message is that companies do not need the largest AI budgets to benefit from AI; they need the right questions, rapid experiments, and a 90-day cadence to test what actually improves the business. Sorensen frames AI not as a one-time transformation but as a quarterly habit of finding inefficiency, testing use cases, and scaling what works.

The article is especially relevant for CEOs, COOs, CIOs, CFOs, CHROs, and transformation leaders who are overwhelmed by AI vendors and unsure where to begin. Its playbook helps leaders move from broad AI enthusiasm to a practical operating rhythm: inventory work, identify friction points, prioritize use cases, pilot tools, develop AI fluency, and establish accountability.

Major takeaways for auditing AI Opportunities

1. Start with business processes, not AI tools

Sorensen’s first step is to inventory core business processes across functions such as sales, marketing, finance, HR, customer support, and operations. Leaders are encouraged to capture recurring tasks, frequency, time spent, pain points, tools used, and the potential for AI or automation.

Business implication: AI strategy should begin with a map of where work actually happens. Without that, leaders risk buying tools before understanding the workflows, bottlenecks, and value pools they are trying to improve.

2. Friction zones reveal the best AI opportunities

The article identifies prime AI candidates as repetitive, rules-based, time-consuming, and text-heavy tasks that do not require much human nuance. Examples include manual data entry, repeated reporting, communication delays, knowledge silos, decision bottlenecks, manual admin work, customer service inefficiencies, HR gaps, and disconnected systems.

Business implication: Leaders should look for places where employees lose time, errors repeat, customers wait, information gets duplicated, or decisions stall. These are often better starting points than flashy AI demos.

3. AI agents are becoming practical digital coworkers

Sorensen describes AI agents as “digital workers” that can pull data from multiple systems, apply predefined logic, and complete tasks such as sending emails, updating CRM records, or creating reports. He suggests asking where an AI agent could act like a junior assistant, analyst, or coordinator.

Business implication: Executives should think in terms of role-based assistance rather than just chatbots. Strong use cases may include lead nurturing, invoice processing, inventory monitoring, onboarding support, customer feedback summaries, and HR screening.

4. Off-the-shelf tools may be enough

The article emphasizes that companies do not necessarily need custom AI development. Sorensen recommends selecting one tool per function and testing it for 30 days with clear before-and-after metrics. He lists functional categories such as sales, marketing, customer support, HR, operations, admin, and finance.

Business implication: Leaders should avoid overengineering the first wave of AI adoption. A practical pilot using existing tools can lead to faster learning than a long custom build.

5. Pilots should be small, measurable, and owned

Sorensen recommends selecting one to three high-impact use cases to test during the quarter. Each pilot should have a clear goal, a success metric, and a named champion who owns rollout and feedback.

Business implication: AI pilots fail when they are vague experiments. Every pilot should answer: What problem are we solving, how will we measure success, who owns it, and what will we do if it works?

6. AI is a capability, not just a tool

The article argues that teams must learn to think in “AI-first” terms: what can be automated, where the human-AI handoff belongs, and who owns AI systems in each department. Sorensen recommends short-form training, lunch-and-learns, and AI champions inside each team.

Business implication: Training should not stop at prompt tips. Companies need role-specific AI fluency, workflow redesign, ownership clarity, and shared standards for responsible use.

7. Prompts should connect AI use to business goals

Sorensen provides practical prompts for aligning AI with goals, strategic thinking, workflow innovation, customer-centric execution, and internal alignment. Examples include using AI to analyze customer survey themes, identify automation opportunities, assess second-order effects, improve onboarding emails, and connect team projects to strategic objectives.

Business implication: AI becomes more valuable when prompts are tied to business priorities. Leaders should teach teams to ask questions that support strategy, margin improvement, customer retention, growth, and execution.

8. Monthly review keeps AI adoption current

The final step in auditing AI opportunities is to hold a 30-minute monthly AI review with the leadership team to ask which pilots are working, what new tools have emerged, and what new pain points are appearing. Sorensen compares this process to managing tech debt: regularly chipping away at inefficiency rather than treating AI as a one-time initiative.

Business implication: AI governance should include momentum, not only risk control. A monthly review cadence helps leaders learn faster, kill weak pilots, scale useful ones, and maintain visibility into employee experimentation.

Leadership talking points

AI adoption should begin with workflow pain, not vendor excitement.

The best first AI use cases are often boring, repetitive, rules-based, and measurable.

A 30-day pilot with a clear owner and metric is more useful than a broad AI strategy with no operational test.

AI agents should be evaluated as digital assistants, analysts, coordinators, and workflow accelerators.

AI capability building must include ownership, training, experimentation, and monthly leadership review.

The companies that win with AI will not simply buy tools; they will build a disciplined habit of finding better ways to run the business.

Reflection questions

Where is our organization losing the most time to repetitive work, manual handoffs, or data re-entry?

Which weekly or monthly processes consume significant hours but require limited judgment?

Where are customers, employees, or leaders waiting because information, approvals, or responses are delayed?

Which AI pilots could produce measurable value within 30 days?

Do we have a named AI owner or champion in each major function?

Are employees experimenting with AI safely and productively, or informally and invisibly?

How often does the leadership team review AI pilots, new use cases, risks, and lessons learned?

Where could AI help us improve strategy execution, not just productivity?

Potential action items of auditing AI Opportunities

Create an AI opportunity inventory across sales, marketing, finance, HR, operations, customer support, and administration.

Add columns for task frequency, time spent, pain points, current tools, error rates, bottlenecks, and estimated AI potential.

Identify the top friction zones: repeated reporting, manual data entry, support ticket routing, invoice processing, onboarding, scheduling, approval delays, and knowledge retrieval.

Select one to three AI use cases for a 30-day pilot, each with a specific target such as hours saved, faster response time, fewer errors, better lead conversion, or reduced manual entry.

Assign a business champion and technical support owner for each pilot.

Test off-the-shelf AI tools before commissioning custom builds.

Create a short “AI-first thinking” training series for managers and employees.

Establish a monthly leadership AI review to decide what to scale, stop, improve, or investigate next.

Document successful prompts, workflows, controls, and lessons in a shared AI playbook.

Recommended similar articles

“The Conductor’s Imperative: What the New Era of AI Means for Executive Leaders” — A strong companion piece on why leaders must learn to orchestrate human-AI collaboration rather than simply manage software. Sorensen argues that executives need judgment, synthesis, accountability, and the ability to create conditions for productive human-AI teams.

“Claude Conductor: Agentic AI Orchestration with Cowork” — Useful for leaders who want to go deeper into agentic AI as a leadership practice. Sorensen frames AI specification as governance and design work, not merely prompting or technical execution.

“When Your AI Vendor Becomes a Risk” — A practical next read for executives concerned about intellectual property, AI vendor dependency, exportability, and stewardship of work created inside AI platforms.

“The Pre-Mortem, Accelerated: Using AI to Kill Your Plan Before It Kills You” — Relevant for leadership teams that want to use AI to surface hidden risks, failure modes, execution gaps, market assumptions, and capacity constraints before committing to a major initiative.

“How AI Exposes the Assumptions You Don’t Know You’re Making” — A useful companion for executives who want AI to help reveal untested assumptions, inference gaps, and invisible reasoning patterns in decision-making.

“How Executives Are Using AI to Gain Organizational Visibility” — A related article on using AI to detect transparency gaps, commitment drift, workload strain, bottlenecks, priority misalignment, and early signs of disengagement.