Feels like you missed the generative AI train? 5 steps for speeding ahead in 90 days

I’ve been talking to founders across the Global South about generative AI (GAI) as often as I can since early 2023. The founders in our portfolio of 350+ companies are generative AI users, not creators. As with any other disruptive situation, these founders can be divided into three groups:

• Ahead of the Curve: companies that have already shipped something.
• Fast Followers: watching and prototyping but have not shipped yet.
• Late for the Train: don’t yet know how to get on the train/don’t have any resources to apply now.

This article is for any founder who feels like they’re late for the train — or is all aboard, but not going fast enough.

Reviewing examples of all three groups will help founders know where they really stand. Those who are Ahead of the Curve had at least three things going for them: They saw the opportunity early, they had ready-made situations to which they could apply generative AI, and they had engineering talent available to get something prototyped and into production in a timely way.

One example is a farming e-commerce company that has already taken 30% out of its customer service costs by putting a farmer-lingo-capable chatbot in front of its customer service agents and expects to get savings to 50% over the next quarter or so.

A Fast Follower has prototyped means to cut costs and increase the speed of recruiting blue-collar workers by adding generative AI–driven steps to its interview and candidate engagement workflow. Because they have a complex workflow with high throughput, they must be careful about how quickly they deploy; initial testing is showing massive improvements in multiple dimensions.

Finally, a Late for the Train startup provides solutions for call centers and has done some initial evaluation and planning, but has not yet determined how/when to best add generative AI to its product roadmap, which is already stressed with demands from existing customers.

Here are five clear steps to move from being late for the train to speeding ahead in much less time than you’d think:

1. Adopt a simple language so everyone can communicate clearly about this disruptive tech.
2. Get your entire team onboard at the high level (many of them may already be there without your knowledge).
3. Ensure that you are not letting cloud LLMs “hoover up” your data in ways that expose it to competitors or bad actors.
4. Establish a Red Team to be disruptive internally.
5. Measure progress on generative AI adoption and communicate it to the company on a consistent basis.

1. Type 1 and Type 2 generative AI applications

There are plenty of new technical words and concepts around AI, and many have written about them, so you don’t need more from me, except this one concept: From an adoption perspective, there are broadly two paths you can be going down, which are not in any way exclusive.

The first is using generative AI to enhance what you’re already doing by increasing productivity or quality of operations or existing customer interactions. Let’s call this a Type 1 application.

The Ahead of the Curve example cited above is Type 1: Companies using generative AI to improve sales communications or help with market research are doing Type 1 work. Type 1 projects can be implemented on an individual or departmental level. And most importantly, they are table stakes for every startup these days — must-do activities. If you want to get funded and can’t show clear adoption of Type 1 applications, you’re in trouble. But Type 1 initiatives alone will not make you an AI company from a VC perspective.

Type 2 efforts are bigger, riskier, and much more important to your survival and to your ability to attract capital. With Type 2, you are looking to create entirely new ways of approaching a vital aspect of your business, or potentially your entire business, building on generative AI.

The upside from Type 1 is a reduction in cost and increased speed/productivity — everyone is doing or will soon be doing these. The upside from Type 2 is potentially unlimited, as you are creating new ways to create and deliver value that might get you access to new customers or gain substantial competitive advantages over others who are not deeply embracing generative AI.

An example of a Type 2 innovation might be a regional B2B marketplace that currently publishes information only in English as it’s the common denominator language in the region. That marketplace now can use generative AI to cost-effectively publish information simultaneously in four local languages and enable its customers to find products/services with a conversational interface (rather than cumbersome search queries and complex filtering) using their language of choice. This Type 2 innovation opens the market to untold numbers of non–English speaking customers and also makes it faster for all customers to find what they are looking for and close the deal.