No new technology investments needed to succeed with AI

You probably hear almost every day about the fastest, the biggest, and the most powerful AI chatbot. Most of the time, this is about a new Large Language Model (LLM) that has been trained on more data and can process more information in a shorter amount of time.

But so what?

These chatbot enhancements may be relevant for about 1% of organizations whose core business is to create content, e.g., movie studios, media publishers, and marketing agencies. For almost everybody else, companies already have enough technology to succeed with AI today. Unfortunately, many believe they need more.

Why do companies believe they need more technology to succeed with AI?

This misperception is driven by three factors:

1️⃣ Propaganda: AI vendors make most companies believe that they need the latest chatbot to succeed with AI. Tech companies race against each other to develop the best LLM and imply that you need to use the latest version to be successful.

2️⃣ FOMO (Fear Of Missing Out): Some companies have a low understanding of what is possible with data & AI today. They believe that they will be missing out if they don’t use the latest technology. The latest hype about AI agents is an example of this FOMO, where some companies think they need AI agents too.

3️⃣ Numbers, facts, figures: It’s easy to quantity technology. You can identify the product specs and its price. Usually, the more the better. AI adoption, on the other hand, is difficult to measure. It’s driven by people who can’t be put into numbers, facts, and figures.

Recent announcements from several pharma companies are great examples of this misperception.

Novo Nordisk, Roche, BMS, and Eli Lilly recently announced partnerships with Nvidia or hyperscalers with big numbers accompanied by pomp and circumstance. Apparently the more technology they have the more of an AI company they will become. Since these companies haven’t been known for leveraging data & AI before the ChatGPT hype started four years ago, announcements like these feel like AI washing. Even these companies have had sufficient technology to use AI for years, but haven’t done so. Why should that be different now?

Do technology improvements matter for AI?

Technology usually improves over time. But for AI, it’s not the mathematical algorithms behind AI that improve, it’s the computing power to process data with these algorithms.

After all, many algorithms behind AI solutions have been defined in the 1950s. Some are even over 200 years old, including the method of least squares outlined by the German mathematician Carl Friedrich Gauss or the Bayes' theorem that predicts probabilities described by the English statistician Thomas Bayes.

In addition to computing power, technology progress is also driven by physics and the real world environments where these AI solutions are being used. Autonomous driving is a great example. This is, to the best of my knowledge, the only formalized and structured definition of an AI system used in the physical world with well-defined progress stages.

For autonomous driving there are clear definitions of the six levels of AI maturity. It starts with Level 0 with no automation at all and the human fully in charge of all the driving. Automation starts with L1, including basic help in some situations, and continues over five levels to full automation at L5 with no human driver required.

Today, aside from a few thousand fully autonomous cars operating in a dozen cities, the most autonomous vehicles are at Level 3 (conditional automation). At this level, the machine can take full control under certain conditions while the human must always be ready to take over. Almost every new car sold today has at least Level 1 automation (driver assistance) at the basic configuration.

Technology improvements matter also for LLMs. LLMs can get better, faster, and more accurate with better technology. But, for most organizations, already basic LLMs are sufficient.

And, LLMs, which are Generative AI solutions, provide only 20% of the business value that AI can deliver. According to McKinsey, 80% of the business value that AI delivers comes from Analytical AI. These are the machine learning and deep learning solutions that have been used by innovative companies for over twenty years. Sufficient technology for these solutions has been available for years.

Which AI can be used with the technology already available today?

Most companies already have the required technology for almost all AI.

You may not need new technology to develop a recommendation engine for sales reps, so they can be more productive and successful when engaging with customers. You may not need new technology for a predictive maintenance system that reduces expenses by suggesting servicing a machine earlier than normally scheduled to avoid costly failures. And for sure, you don’t need new technology to increase revenues with the same marketing investments by optimizing your marketing mix.

These are just a few examples of AI solutions that most likely can be implemented with the technology you already have. However, investments are required, but not investments in new technology. You need to invest in internal technical experts, including data scientists, data engineers, and machine learning engineers, to develop these AI solutions. Investments in data may be required if you don’t have a solid data infrastructure and data governance in place, which are needed for AI to function.

And you need to invest in data & AI literacy of non-technical employees. Don’t expect them to use AI just because you make it available. The right data & AI mindset is needed to ensure that AI solutions are adopted.

If the new partnerships between pharma companies with Nvidia and hyperscalers will be successful will depend on these non-technology investments. It will depend on whether these companies digitize their business processes like innovative companies have done thirty years ago. It will depend on whether they embed data in the decision making process like innovative companies have done twenty years ago. And it will depend on whether they build internal data science teams like innovative companies have done ten years ago.

Sometimes I hear from executives that they are not sure where to start with AI or whether they should wait until AI is more advanced. But many innovative companies have been successfully using AI to grow profit already for years or even decades. They have not been waiting for the latest technology to emerge. And they know that people are the biggest driver of success with AI.

The best time to start using AI was yesterday. The second best time is now.