How can you use AI and BI trends to stay ahead?

One of the things that both artificial intelligence and business intelligence practices share is the need for clean data inputs. Before considering the idea of integrating both, it pays off to consider the effort of investing in data quality. Start by defining an action plan to attack inaccurate, incomplete, inconsistent and repetitive data points. Once you feel like your data streams are in good place, move on and consider the AI + BI integration.

To keep abreast of using AI and BI trends, actively engage in communities and forums focusing on these technologies' ethical aspects. Regularly attending webinars, conferences, and other learning opportunities can enhance your understanding of evolving ethical standards in AI and BI. Leverage this knowledge to perform ethical audits of your AI and BI tools, ensuring they adhere to current industry standards and societal expectations. This proactive approach helps avoid potential pitfalls and positions your business as a responsible and forward-thinking enterprise in the eyes of customers and partners. Continuous learning and adherence to ethical standards will empower your business to utilize AI and BI while staying ahead effectively.

Given context around your database schema, state-of-the-art LLMs like GPT-4 can write robust SQL queries to answer questions about your business data. This means teams need to consider how much of their BI/analytics they should be writing manually. It may soon be more efficient to build a data science agent within the firm, a chatbot that receives instructions in natural language to write queries and perform analysis on the fly, instead of manually. This pattern will extend to most areas of the software and product/project management stacks.

I think you can feed your machine learning algorithms with data and the intelligence extracted and learned by your BI. Your AI will mimic a Product Manager looking at a dashboard and having insights and new ideias.

AI and BI are enablers of completely new business models, just like the internet, mobile and cheap cloud computing were in their early days too. If your team is already well versed in utilizing frameworks like the Business Model Canvas for discovery, you can further the practice by considering AI to be inputs into the "Value Propositions" section. AI and BI can also be applied for efficiency on the "Key Activities" section.

When entering a new knowledge field, start by acquiring the vocabulary that is specific to that new practice. Keep a list of AI terms that you are coming across in your readings, and invest time in unpacking the meaning of each one. Continue probing further until you have understood how these terms are linked to each other, and how they differ. This is one of the quickest paths to getting a quick sense of a new industry like this one.

The #1 tip I give to people that want to learn more about how to use AI in business is to use it in their normal lives. Use it to create a workout plan or write unique thank you notes after your wedding. Becoming familiar with how the tools work on a daily basis, both in and out of the office, will substantially improve your understanding.

Having a well-defined strategy is essential in these rapidly changing times. You need to ground yourself in customer need, core value proposition, and key insights. New AI technologies can help you deliver on your strategy, but just remember that they are only tools. Don't let hype distract you from your ultimate goals. If you think a technology will materially change the landscape of your problem space, go back to the drawing board. Otherwise, figure out how it can help you, and get back to work.

Build a chatbot. Give that chatbot context around your business objectives. Then ask the chatbot what you could do next. Have it generate variations of different strategies aligned with your goals and capabilities. You'll learn first-hand what these LLMs can do, building an organizational muscle around operationalizing the technology. In the future much of strategy and business intelligence, including visualization will be derived from multi-modal natural language agents.

Don't let the potential pitfalls scare you. Yes, AI and BI come with their own set of challenges, but that's true for so many things in business. The goal isn't to avoid all hurdles, but to find smart, ethical ways to get over them.

It's important to distinguish between short-term opportunities and long-term opportunities enabled by new AI technologies. A few months back, our team at Voxel51 went viral (100k+ views) for turning our documentation into a searchable database with OpenAI. We were approached by a lot people asking us to turn this into its own business. Within a few months, "chatting with your data" has almost become table stakes. There are a lot people looking to make a quick buck, and ultimately the proliferation of A tooling will lead to a lot of races to the bottom. Know when you have (or can build!) a defensible long-term strategy, and seize these opportunities!