Welcome to Fall!

After the action-packed Summer of identifying and implementing use-cases, Fall is the time to assess the impact of those efforts.

It's the time to determine - was this just a Summer love... or is it marriage material?

In Fall, we focus on Measuring Impact, and I break it down into three key steps:

1. Choose How to Partner

Before diving into measurement, it’s important to revisit your AI partnership strategy, as we covered on a previous newsletter.

• For Buy: If you’re buying solutions, start small. Test your chosen tools with limited use-cases before scaling. Find a few champions, that help spread excitement and capability across the company.
• For Build/Borrow: If you’re going for BUILD or BORROW, go big. Invest in solutions that can be broadly integrated and scaled within the organization.

Objective: The goal is to choose the right approach - if you go too small, you won't prove impact. If you go too big, too fast - you will fall flat.

2. Measure Impact: Leading & Lagging Indicators

Normally we rely almost exclusively on Lagging Indicators. But this is such a disruptive technology, that I believe we need to give it time and also measure Leading Indicators.

Leading Indicators: Some examples

Here's how you can grasp initial signs that show how AI is performing across your organization:

1. Adoption Rate

- Speed of AI solution adoption within the company.

- Metrics: Number of users or departments starting to use the AI tool over time.

2. User Engagement

- Frequency and manner of user interactions with the AI solution.

- Metrics: Usage frequency, tasks completed using AI, user feedback scores.

3. Operational Efficiency

- Improvements in processes targeted by the AI solution.

- Metrics: Reduced processing times, increased throughput, enhanced accuracy.

Lagging Indicators: Some examples

1. Cost Savings

- Reduction in costs related to the improved processes.

- Metrics: Comparison of operational costs before and after AI implementation.

2. Return on Investment (ROI)

- Financial returns generated by the AI project relative to its cost.

- Metrics: Revenue growth, market share expansion, competitive advantages.

3. Productivity Gains

- Increase in efficiency and output.

- Metrics: Time saved by employees, increase in production levels.

Objective: Leading and lagging indicators help you understand both the early success and the long-term impact of your AI initiatives - the key is balancing both.

3. Create a Roadmap for Future Growth

As you assess the impact of your AI efforts, it's important also to keep assessing - as the technology developments are incredibly fast.

So create 3-month, 6-month, and 1-year Plans. Regularly reassess your AI strategy based on tech developments and feedback from teams. Adjust your goals, refine use-cases, and plan for further AI integration. T

Objective: A roadmap ensures that your AI initiatives stay relevant and aligned with the organization’s evolving needs, as well as the developments in tech.

Bringing Fall to Life

In Fall, the focus is on measuring impact and refining your AI strategy. You’ve implemented use-cases in Summer, and now it’s time to determine what’s working and what needs adjustment.

Leading and lagging indicators will give you a full picture of AI’s value, and with the right roadmap in place, you’ll be well-prepared for the next stages of growth.

Next up? Winter—the season of governance and responsible AI, where we’ll focus on scaling AI safely and ethically.

Until then, I have to say it: "Winter is coming".

I'll see you next week.

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