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Demystifying AI Strategy: How to Start Simple and Scale Up


Who needs an AI strategy? What does it look like? Do I really need one? It sounds like I need to hire lots of PhDs and enter an entirely unfamiliar and very fast-moving space…Yikes!

Our position is twofold: 


  1. Everyone (almost everyone) needs an AI strategy today. 

  2. An AI strategy can be simple and easy


An AI strategy can be very simple. It can also be complex, but our main contention is that it really doesn’t have to be. The main thing is to start now, if you haven’t already. And, if you have started, keep reading to either affirm or adjust your strategy - we have some suggestions for how to simplify thinking about AI strategy. 


We believe that AI is the next phase of the information age. As personal and desktop computers came, followed by the internet,  AI is the next phase, and its nature is quite different from previous phases.  It is here, but it is also still forming and evolving, and there are huge opportunities for the right type of adoption and strategy. So the ‘why’ of AI strategy is that AI is coming very quickly, and that a certain amount of AI expertise will very quickly (if not already) become table stakes for almost all businesses and industries. The other ‘why’ of AI strategy is that it may end up being more personalized and fragmented than previous rounds of information technologies. Of course, big players will have offerings, and may end up having the best and cheapest ones. But, it would be unwise to just wait and see…Taking ownership and control of your AI strategy, even if it is a simple one, or one based on existing platforms, will put you in a much better position. 


What is AI strategy? Is it complex and expensive? Does it mean lots of PhDs and data analysis and virtual-reality headsets? A simple suggestion to start…AI strategy means understanding conceptual AI capabilities and learning to match them to business opportunities (and challenges). 


Your initial AI strategy may be as simple as just trying out some “canned” AI tools like ChatGPT or Gemini if you haven’t already. Taking things a bit further, you could include trying some tools on your company data to get a sense of what’s possible and what’s coming. If you don’t think you have relevant company data, or don’t think you can get it easily, then try with some of your personal data. We’ll make a recommendation below. Seeing this in action even on your own data can provide a lot of hints as to what’s possible. The main idea is to start to build confidence and familiarity with the abstractions around AI and LLMs beyond simply reading about them in popular business and tech press. 


Take Search & Recommendation as an example


You may think Search is a solved problem - we’re already on to the next thing. Google solved Search for the internet, and AI is beyond Search. But, the reality is that lots of today’s AI (by which we mean deep learning AI like LLMs and image captioning) is still ultimately about better (smarter) search. The thing that’s changed is that search systems have gotten much better - specifically, they understand what you’re looking for much better than before, and they understand the content you’re looking for much better than before. Most AI systems today are about much better matching of your search intent and content, whether that’s a webpage, a photo, a full-length movie or your email. And, another theme that will just continue to repeat itself is AI learning much more complex relationships than the previous generation’s search systems…Better email or photo search is enabled by learning much richer relationships between the words or words and images, and making these learned systems easy to query. Indeed, early Google search really was a form of AI and language modeling. So, our first suggestion is to think of AI as advanced search that understands human language better. We sometimes (always?) forget how Google trained us to search using keywords like “best gifts golfer men 2023”. What is this language? We certainly don’t talk to people this way.  Getting computers to understand questions (and their answers) that are much closer to the nuance and manner in which we communicate with each other is a major key to understanding today’s AI capabilities (including those that can be applied to your business, products, data or personal life).


“think of AI as advanced search”

Imagine all the things you could do with advanced search and you’ve made a huge step forward in having a conceptual understanding of deep learning AI and its applications. “Find me all the highest value sales leads that resulted in closed sales over the past 5 years” or “Give me a list of all the products we made in the past 10 years that had the lowest return rate and highest review ratings”. This might just sound like “Business Intelligence” that was all the rage 5-10 years ago. Except that today’s AI systems (search systems) can now handle many of these types of analyses without investing in huge systems, hiring teams of consultants to scrub and host databases or specialists to build and manage these systems. What kind of search system would you love to have for your business or customers? If you don’t have business data to test with or it’s not easy to get, Dropbox makes a product that allows you to use AI-based search across all your personal content: email, bookmarks, documents, etc. 


Recommendation is another capability that is suddenly very powerful and accessible and can match many business tasks that you might not have considered. In a way, recommendation is just another type of search, but a specialized one. Yes, sophisticated product and content recommendation systems are now much easier to build from free or packaged AI software offerings, and you might have considered these beyond your reach or too complex to deploy. But, the abstraction of recommendation systems may suggest other applications. Where might you have opportunities to find similarities between customer types? You may currently class customer types by their industry (education, government, manufacturing). But, recommendation and similarity systems might find a better classification of clients that can be used for smarter or higher-conversion upsell. An example might be that your clients are better grouped by the features they prioritize or value. Recommendation can be used to find these hidden relationships, but also to recommend upsell offerings to the “right” customers. Deep-learning-based semantic clustering and classification on data that’s never been analyzed before might contain gold in terms of customer insights and opportunities. 


AI strategy is largely about understanding the building blocks of AI systems at the right level of abstraction, and then looking for places where you can apply these capabilities. AI strategy also involves making hard decisions about investment timelines and costs/benefit analysis. Given the extremely fast pace of change in not only the underlying technologies, but also the startup and software landscape of providers, this can be an overwhelming task, but we believe it’s an important area to spend time on. We’ll have more to say about this in the future, but you’ll be reassured to know that very few people are confident in this area right now. The most important thing you can do is start to build some experience and confidence at the lowest cost and risk that you can. As you start to develop familiarity with the tools, capabilities and abstractions, you’ll be able to make better and bigger investment decisions more confidently. There are so many tools and options with lots of great documentation. That’s a blessing and a curse as the pure volume and hype can make it hard to make a decision. Getting help from other companies and products should definitely be in consideration, but ultimately, how you decide to employ AI in your business strategy should be your decision - taking ownership of this decision confidently is one of the most important things you can do right now.

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