Almost Timely News, October 8, 2023: How To Pilot an AI Deployment

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Almost Timely News: How To Pilot an AI Deployment

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What’s On My Mind: How to Pilot an AI Deployment

This past week, I had the pleasure and the privilege of speaking at the MarketingProfs B2B Forum in Boston, Massachusetts. One of the questions that consistently comes up time and time again when I’m doing my talk about artificial intelligence is how to get started using artificial intelligence, how to pilot an AI project. So today let’s look at the three-step process for piloting an AI project at your company, organization, or team.

Step 1: Understand The Use Case Matrix

The first part of developing a pilot project for AI is having an idea of even what kinds of tasks you’d want AI help with. You can’t undertake a pilot successfully if it’s solving a problem no one has; solutions in search of a problem never go well. So let’s start here, with the Use Case Matrix:

The Trust Insights Use Case Matrix

What have here is a straightforward matrix with two axes – internal vs external, and innovation vs optimization. First, internal vs external: for any given task, is it internal or external, meaning within your organization or customer-facing? This is an important consideration because, at least when it comes to AI, customer-facing tasks tend to be higher priority and the aversion to risk is (sensibly) higher.

Second, innovation vs optimization: for any given task, is the task one in which you want to make incremental optimizations, or something net new? For example, if you’re an insurance company, a large language model that can answer customer questions would be innovation; a large language model to scan in claims and categorize them for speeding up claim prioritization would be an optimization.

Think through all the problems that your company is facing right now, and see where they fit in. For example, the other week, Katie and I were discussing some shortcomings in Hubspot’s built-in reporting. I used ChatGPT’s code generation capabilities to write new code that performed the reporting tasks exactly the way we wanted them done. That is an example of an optimization that’s internal. Customers won’t see it, and it’s not breaking new ground, but it does make an existing internal process much better.

Sit down with your stakeholders and jot down all the processes which are problems you face with your company onto this 2×2 matrix so that you can start to assess which ones AI could help you with.

Step 2: Understanding The Use Cases

One of the challenges of artificial intelligence, particularly generative AI, is that generative AI uses language and imagery. In turn, that means any task which uses language and imagery could potentially be a task suited for generative AI. Think about it for a moment. Can you name a single function in your company that does not use language or imagery as part of the core tasks that you perform? I certainly can’t.

So a part of the challenge of determining where to pilot AI is to look at the categories of use cases for generative AI. I break them up into six broad categories: generation, extraction, summarization, rewriting, classification, and question answering. Let’s talk through these relatively quickly.

Trust Insights Use Cases of Generative AI

Generation is very straightforward. Everyone understands generation; write me a blog post, write me an email, create an image of a dog on a skateboard – generation is intuitive for us and doesn’t require a lot of explanation.

Extraction is the process of taking data out of provided data. For example, if I give a generative AI system a PDF that has some tables in it, I can use large language models to extract those tables from the PDF and convert them into a CSV file for Microsoft Excel.

Summarization is when we take big text and turn it into small text. Take a transcript from a conference call and extract out the meeting notes and action items, take a long YouTube video and convert into a bullet point summary, etc. We summarize things all the time and language models are capable of summarizing things quickly, and very capably.

Rewriting is equally straightforward. Take this English language paragraph and rewrite it in Swahili, explain the concept of quantum superposition in terms of pizza, convert this profanity-laced diatribe into a professional memo – that’s rewriting.

Classification is when we apply language models to understanding a body of text and what categories it fits into. Think about a pile of blog posts from your company blog; a large language model could automatically assign topics and categories for every post so you wouldn’t have to do that manually.

Finally, question answering is when we ask language models questions from a body of knowledge we want answers to. For example, imagine loading up an RFP response to a language model and then asking it the five conditions you care about most, and whether or not the RFP meets those conditions. That’s question answering.

Once we understand the core use case categories, we can examine all the ways we’d like to apply AI and see if the tasks which make up our use case fit in these categories or not. This is an essential step because it helps us to understand whether generative AI is the right fit or not; if a task doesn’t fit in one of these six use case categories, either you haven’t broken down the task enough into its component tasks, or you have a task that isn’t suited for generative AI.

So you would take the results of your 2×2 Use Case Matrix, and then tag each of the tasks with one of the six use case categories. If a task doesn’t fit into a use case category, then AI probably isn’t the right choice and it should be removed from the matrix.

Step 3: Applying User Stories

Once you’ve laid out your use cases from your matrix and tagged each use case with which of the six use case categories are the best fit, the final step is to assess priority. Assessing priority isn’t just a gut feeling; we want to clearly articulate the use case’s solution so that the priority becomes evident. We do this through user stories.

Here’s the basic template for a user story:

As a {role} I need to {task} so that {result}.

In the example above, Katie’s user story would be:

As a CEO, I need to know what prospects in our Hubspot CRM haven’t interacted with us in 30 days so that I can commission an outreach process for reactivating prospects to increase the sales pipeline.

In this user story, it’s clear what the ask is, what the outcome is, and how the outcome matters to the company. This use case is all about revenue generation, and it would be wise for us to prioritize if it we want more sales pipeline.

Your next step would be to write out clear user stories for each of the tasks in the Use Case Matrix, as clearly as the one above. We want that clarity so that we can prioritize and organize all the ways we COULD use AI into a coherent list of what we SHOULD use AI to accomplish.

The final step in this example would be for the stakeholders to review all the user stories in detail and decide, based on what those stories focus on, which tasks you should pilot AI with first. If you follow these steps, you’re much more likely to see positive and meaningful results from generative AI than if you just wing it or create solutions in search of a problem.

And shameless plug, my company, Trust Insights, does exactly this kind of evaluation for companies. From basic strategic consultation to workshops to having us sitting in the room with you, guiding you through more advanced versions of this exercises, if you want help with this, let us know.

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See you next week,

Christopher S. Penn

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