AI Opportunity Evaluation Framework (Example)

An anonymized example of how I structure and communicate early AI opportunity assessments.

Context: Strategy & Innovation function at a leading annuity provider · Focus: Practical, low-risk AI enablement

Why This Framework Exists

One of the strongest contributions I bring to an AI strategy and innovation function is the ability to evaluate AI opportunities clearly and communicate that assessment in a way that aligns technical, business, and leadership perspectives.

The framework consistently answers questions like:

What problem are we solving, in plain language?
Who experiences the pain, and how often?
Is AI actually the right tool, or would a simpler workflow, template, or governance change solve the problem faster?
Is the data structured or unstructured, and how hard is it to access safely?
What is the smallest safe experiment that still produces real insight?
What risks or challenges exist, and how do we adjust scope accordingly?

The goal isn’t to deliver a verdict. It’s to create a transparent starting point for discussion. Once assumptions are visible, teams can challenge, refine, and improve them. That transparency lets each opportunity naturally settle into its proper place in the backlog as we learn more.

This example is hypothetical. It illustrates how I would evaluate and position a potential AI opportunity for a major annuity provider, not a record of a production implementation.

How the Evaluation Is Structured

When an opportunity comes in, I typically organize the evaluation into a few simple sections that business, technical, and leadership stakeholders can all recognize:

Use Case & Recommendation — Name the use case and give a clear, concise recommendation.
Problem — Describe the pain in terms of people, time, and risk.
Primary Option — Outline the simplest viable approach, not just the most complex.
Support / Enablement Plan — Show how teams will actually learn to use the solution.
Value & Cost — Use directional numbers to clarify the scale of impact.
Dependencies & Risks — Call out education, governance, and operational risks early.
Next Steps — Frame how this can scale or inform additional discovery.

Example Use Case Evaluation (Condensed)

Use Case

Persona-Based Messaging Enablement (Marketing)

Recommendation

Enablement pilot, not a full AI build.

Problem

Marketing teams need to generate audience-specific versions of product descriptions, emails, and internal announcements. Today, these rewrites are manual, repetitive, and slow — but they do not require specialized engineering or deep system integrations.

Primary Option

This use case is perfectly feasible today using off-the-shelf large language models (LLMs). It can be solved with:

Prompt templates tailored to specific audiences and communication types
Approved persona descriptions that capture tone, constraints, and key messages
Governed usage guidelines so teams know what is in-bounds and out-of-bounds

Most of the work can be done by Marketing with enablement from AI subject-matter experts. There is no immediate need for infrastructure or engineering investment.

How to Support the Primary Option

This becomes a perfect education and enablement pilot to help teams understand how AI can:

Adjust tone for different audiences (e.g., advisors, policyholders, internal leaders)
Vary messaging while staying on-brand and within compliance guardrails
Reduce the time spent on repetitive content drafting and refinement

Value, Cost, and Risk

Value

The expected value is significant time savings for each Marketing producer, at low technical complexity:

Assume 5 people saving ~10 hours/week
Roughly 2,600 hours/year recovered
At a fully loaded cost of $50/hour, that’s approximately $130,000/year

Cost

Direct costs are very low and do not require IT involvement for an initial pilot:

Generalized workshop/demo time: 1 presenter x 20 hours
Participant time: 40 people x 1 hour
Total time investment: ~60 hours, or about $3,000 at $50/hour

Dependencies

Basic AI education for everyday users: what to watch for, how to remain present as a human in the loop, and how to spot hallucinations or misaligned content.
Intermediate AI education for persona and content owners: how to define tone, constraints, and compliance-aware prompts that align with brand and regulatory expectations.

Risk

Risk is minimal with human review in place. The primary exposure is reputational (off-tone or off-brand content), which is manageable through education, approval workflows, and clear guardrails.

Why This Belongs in an AI Backlog

This type of use case is an “easy win” with the potential for a positive narrative:

Reduces tedious, repetitive work for Marketing teams
Improves time-to-delivery and reduces friction
Offers a low-risk way to build AI literacy and confidence

Offering this style of workshop across other functions—operations, distribution, product, training, and others—is a low-cost way to:

Spark additional AI use-case discovery
Accelerate AI literacy across the organization
Build cultural momentum behind the innovation program

In a broader AI strategy, this example would sit near the top of the backlog: high value, low risk, low complexity, and fast to learn from.