A Leadership Primer on Generative AI

Educate your team on how you might approach artificial intelligence.

Hey y’all!

This week has been a fun one with new advancements in genAI every single day! For instance, Google’s release of Lumiere yesterday took me down a massive rabbit hole (or “space-time” as they call it) of video generation:

I’ll admit that I’m not as experienced with generative video mostly because I haven’t really seen super-compelling applications for it that are practical but it’s only a matter of time, I’m sure of it. If you have examples then please make sure to comment and let me know what you’ve seen!

Have a great week and I’ll see you tomorrow!

✌(-‿-)✌

— Summer

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Preamble: I shared this with a few friends who are looking to pivot their projects and companies into generative AI and I thought it would be worth to share it here with my readers. Please consider sharing this with others who would need this!

A Leadership Primer on Generative AI

It's been almost 70 years since John McCarthy first mentioned "artificial intelligence." Since then we've seen many tech advancements like personal computers, the internet, smartphones, cloud computing, and now, the exciting world of generative AI.

Each new technological innovation brings opportunities and big questions for leaders. Things like, how can these help our organization take advantage of these tools? What cool new things can they do? How do we organize our business and organization to get the most out of it? And perhaps most importantly, how do we use these in ways that make our customers, the public, and everyone else trust and buy from us?

In this quick primer I’m going to share some thoughts and ideas on how to get started! I broke this down into five key areas focused on building lasting value with AI:

1. Business Strategy

2. Technology Strategy

3. AI Experience and Strategy

4. Organizational Culture

5. AI Governance

Naturally, I like to stay pragmatic with some simple next steps, no matter if you're just starting out, leveling-up your organization's know-how, or already deep into AI.

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A Few Definitions

Let’s start with a few simple high-level definitions and a timeline:

1. Artificial Intelligence (AI) (1950s): The development and theory of computer systems and sciences that are able to perform tasks that usually require human intelligence. Simple examples are visual perception, speech recognition, decision-making, and language translation.

2. Machine Learning (1990s): ML is a subset of AI and computer science where algorithmic models ingest data and then are trained to learn from that data in order to manipulate the models, make (better) decisions, and/or predictions.

3. Deep Learning (2010s): DL is a ML technique that uses neural networks to process and interpret data in order to make decisions.

4. Generative AI (2020s): GenAI is a type of AI technology that leverages algorithmic data and models to create novel written, visual, and auditory content when given prompts via existing data.

Now let’s take a look at the five key areas for a business and leadership.

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5 Key Leadership and Business Areas of AI

Since the 1950s, when "artificial intelligence" was first coined, things have only accelerated (and it’s not stopping). The reasons for this are simple: More data, more powerful computers (and compute), and more talented folks moving into the space who deeply care about this technology and want to develop responsible, smarter, and more useful AI.

A unique characteristic that isn’t talked about nearly enough is the fact that artificial intelligence is less about “rules” and hardcoded logic and rather “chance” and discovery. Older technology and computers were built around the former while the newer computing logic and algorithms focus on probability and randomness within their variables. Consequently, AI behaves much more like humans than classically-understood computers. Understanding the properties of an image or picture, recognizing not just speech but patterns within and even making decisions on a very little amount of data is now possible.

Generative AI (or GenAI) accelerates all of this by allowing humans through prompt and input interfaces to create unique content, summarizing mountains of text with different tonalities, answering questions, and even making images and video from simple natural language text inputs without the need for code. In fact, a child can create dramatic outcomes by simply asking the GenAI tool to do it.

But what does this mean for the professional and business leader? Here are the 5 key areas that you’ll want to get your head around for a starting point.

1. Business Strategy: A great business ultimately measures the input and output of their business, products, and services. Having clear goals and priorities establishes a baseline of how you can measure that value, especially when it comes to revenue. Once you know these things you can then figure out ways in which to implement AI that has real outcomes that grow the business.

2. Technology Strategy: Being “AI Ready” is something that most leaders are thinking through in a serious fashion. To do that it’s worth reviewing the present solutions that the business makes and operates around and exploring how artificial intelligence can leverage the existing data (layers) that power them. Then you can make the determination as to whether you’re going to “build or buy” (future) solutions and, most importantly, how you’re going to leverage and store your data in a way that’s secure, private, and profitable.

3. AI Experience and Strategy: Generative AI in particular has grown tremendously through the user of builders and creators who are producing results that are customer-facing and customer-focused. The best approach is to consider how each AI model works for each situation, whether that’s internal tooling or customer-centric experiences. Test-drive liberally and rigorously different toolsets, APIs, and services to understand the best solutions for the business.

4. Organizational Culture: AI is already helping your team save time and money but success will require clarity of leadership and focus to have a lasting impact. Think through how you not only implement but socialize these upcoming changes to the entire organization and offer fun opportunities for (continuing) education for your staff. Most of all, stay positive about these changes and reduce any “doomer” perspectives about how it might blow up the business or reduce jobs.

5. AI Governance: Finally, it’s worth considering to establish some baseline thoughts on how to responsible implement and use AI in the organization and for your customers. Security, privacy, and metric-driven outcomes will help alleviate the stress and burden of implementation. Your team doesn’t have to be experts but they should be all knowledgable about these exciting changes.

If you were to layout a pathway to introducing the following high-level concepts into your organization, here’s one simple 3-step framework that might be useful:

1. Step One: Explore and experiment with AI in one smaller part of the organization. Socialize this process liberally within the larger business.

2. Step Two: Assess and define the high-level strategy from the learnings of your explorations. Share these findings across the business.

3. Step Three: Expand and execute the positive AI outcomes across other business areas while measuring value at all times.

Then you can rinse and repeat! I hope this is helpful! Please consider sharing this with folks who could use this high-level primer. Thanks!

✌(-‿-)✌

— Summer