MMarketing Against The Grain
← All frameworks
Productivity

Impact × Speed AI Use-Case Matrix

Prioritize AI projects by business impact and time to launch

Difficulty
Easy
Time to result
~days to results
Steps
5
Confidence
99%

The matrix scores each proposed AI use case on two dimensions: expected business impact and speed to launch. Teams gather opportunities, estimate where each sits on both axes, and favor projects combining medium-to-high impact with medium-to-fast delivery. This priority zone matters because AI technology and user expectations are changing rapidly; launching sooner starts the feedback loop while the opportunity remains relevant. Low-impact ideas are filtered out even if they are easy, while slow projects must justify their delay with exceptional value. The framework is intentionally lightweight, allowing a team to reduce a backlog of dozens or hundreds of ideas to a focused portfolio. Scores should be revisited after each launch because actual user feedback improves future estimates.

Origin

HubSpot used this two-by-two approach after gathering more than one hundred potential AI use cases from across its marketing organization. Extracted from Marketing Against The Grain.

Core principles

  • 01Time to launch matters because AI and user behavior evolve quickly.
  • 02Fast feedback creates a competitive learning advantage.
  • 03Business impact must remain the primary value dimension.
  • 04Medium-to-high impact and medium-to-fast delivery define the priority zone.
  • 05A long list of possibilities is not a strategy.

How to run it

  1. 1

    Build the candidate list

    Gather AI opportunities from leaders, practitioners, customers, and existing workflow pain points.

    Pro tip Describe each use case as an outcome rather than a technology.

    Watch out Do not let duplicate formulations inflate the apparent backlog.

  2. 2

    Estimate business impact

    Rate the likely effect on revenue, conversion, customer value, cost, or strategic learning.

    Pro tip Use a consistent low, medium, and high scale.

    Watch out Novelty and executive excitement are not substitutes for impact.

  3. 3

    Estimate time to launch

    Assess how quickly a minimum useful version can reach real users and begin generating feedback.

    Pro tip Score the smallest viable implementation, not the imagined final system.

    Watch out Ignoring data, legal, and integration dependencies will produce false speed estimates.

  4. 4

    Plot the matrix

    Place each candidate according to impact and delivery speed, then identify the priority zone.

    Pro tip Highlight medium-to-high-impact candidates that can ship medium-to-fast.

    Watch out Do not fill the plan with easy but inconsequential work.

  5. 5

    Launch and re-score

    Ship the strongest candidates, measure user response, and update the matrix with what the team learns.

    Pro tip Treat learning speed as a secondary outcome of every launch.

    Watch out Static rankings decay quickly in a fast-moving technology environment.

In the wild

Reducing a hundred ideas to three

An AI owner collects more than one hundred ideas from a marketing organization. The team plots each by expected business impact and time to launch, then selects a personalized nurture experiment, a website chat improvement, and automatic form enrichment because each can reach users quickly and influence conversion.

The organization gains a focused roadmap and begins collecting evidence instead of debating an undifferentiated backlog.

Common mistakes

Prioritizing speed alone

Shipping trivial features quickly creates activity without meaningful business value.

Estimating the finished system

Scoring a fully developed vision instead of a minimum useful release makes promising experiments appear unnecessarily slow.

Never revisiting the matrix

New technology and user feedback can change both impact and delivery estimates.

Is it for you?

Best for

It is best for teams choosing a few projects from a large backlog of plausible AI use cases.

Not ideal for

It is not ideal when regulatory, safety, dependency, or strategic-fit constraints cannot be represented by impact and speed alone.

From the transcript

the time to go live on AI is everything.

Kipp Bodnar · 21:00

it's like how much business impact do we think they're gonna have and how quickly can we get them done?

Kipp Bodnar · 21:30

the way you can out compete everyone else is to learn faster than everyone else.

Kieran Flanagan · 22:00

From the episode

Use This A.I. Marketing Strategy To Grow Your Business In 2023 (#114)