Taking a New Approach to AI Projects for Real Business Applications

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In this special guest feature, Dr Catherine Havasi, AI Science Lead at Agorai (a global platform which provides the turnkey software solutions, technology and data needed for any company to join the AI economy), discusses the need for a more pragmatic approach to AI that firms must take to ensure real business results are achieved. In this piece, Dr. Havasi provides her advice on what businesses should look for in order to start a new AI project, what kind of evaluations they should make and what measurements they should consider. Dr. Havasi is an MIT scientist and leading expert around the fields of natural language processing, machine learning and AI. For over a decade, she has spearheaded projects and research initiatives at the MIT Media Lab that have been seminal in the development of AI for real-life applications. In particular, she is known for her work alongside Dr. Marvin Minsky at MIT to create the Open Mind Common Sense Project, one of the largest common-sense knowledge bases in the world.

It can be really frustrating to run a successful pilot or implement an AI system without it getting widespread adoption through your organization. Operationalizing AI is a really common problem. It may seem that everyone else is using AI to make a huge difference in their business while you’re struggling to figure out how to operationalize the results you’ve gotten from trying a few AI systems.

There has been so much advancement in AI so how can you make this great technology actually translate into actionable business results?

This is a real common problem that has been touching enterprises of all kind, from the biggest companies to mid-sized businesses.

Here are a few quick pointers on how to turn your explorations in AI into AI practices leading to real results from investments.

Pragmatic AI

Firstly, focus on what gets called “Pragmatic AI” – practical AI that has obvious business applications. It’s going to be a long time before we have “strong AI” so look for solutions that were made by examining problems that businesses deal with every day and then decide to use artificial intelligence to solve the problem. It’s great that your probabilistic Bayesian system is thinking of the world differently or that a company feels like they’ve taken a shortcut around some of the things that make Deep Learning systems slow to train – what does that mean for the end user of the artificial intelligence? When you’re looking for a practical solution, look for companies who are always trying to improve their user experience and where a PhD in machine learning isn’t needed to write the code.

Internal valuations

Similarly, change the way you are considering bringing an AI solution into your company. AI works best when the company isn’t trying to do a science fair project but is trying to solve a real business problem. Before evaluating vendors in any particular AI solution or going out to see how RPA solutions really work, talk to users around your business. Listen to the problems they have and think about what kind of solutions would make a huge difference. By making sure that the first AI solution you bring into your organization aligns to business goals, you are much more likely to succeed in getting widespread adoption and a green light to try additional new technologies when it comes time to review budgets.

And no matter how technology-forward your organization is, AI adoption works best when everyone can understand the results. Pick a KPI focused problem like conversion, customers service, or NPS where the results can be understood without thinking about technology. This helps get others outside of the science project mentality to open their minds on how AI can be used through the business.

Finally, don’t forget that AI can help in a wide variety of ways. Automation is a great place to use AI within an organization but remember that in many use cases, humans and computers do more together than separately and great uses for AI technology help your company’s employees do their job better or focus on the right pieces of data. These solutions often provide as much value as pure automation!

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