At the heart of all great businesses are domain experts who are excellent at execution. Regardless of whether the business remains highly analog (increasingly rare in today’s marketplace) or is the latest emerging digital start-up domain expertise and execution wins over technology disruption alone.
So when you’re considering investments AI and its related technologies you have to start with your business. Do you really understand how your business runs? Not just overall, but the details of all the various players and moving parts in each domain. If not, you may struggle to solve problems with AI.
Seek first to understand
The times in my career where I’ve felt most uncertain about success was when I was moved into a role where my domain expertise was weak. I’ve always been fairly aware of my blinds spots but far too often I see people who lacked a sense of their gaps get in trouble when they tried to deploy a technology in an area they don’t understand.
So how did I turn a weak understanding of the domain into a success story? It starts with seeking the expertise that exists within the organization. Find the person in your organization who has it. Again, this doesn’t have to be the most tech-minded person out there. They don’t need to understand machine learning, AI, or natural language processing. What they need to know is: What is currently running well and where is the pain? What processes struggle? Where is the data found? Technology solutions cannot help improve outcomes unless the proper tools are applied against problems that are meaningful to contributing to results.
Then seek to be understood
One of the challenges in implementing AI-driven solutions is that business people often don’t understand how these technologies contribute to better outcomes. Often magical powers are attributed to things like machine learning. They’re not magical. They’re simply additional tools in our toolkit that help us put data to work.
Helping practitioners gain an understanding of the technology will help them frame problems and identify data that can be applied to those challenges. What is critical to building an early understanding of how this tech is creating simple frameworks to help people who are not data scientists understand how these technologies work.
For example, when working with a marketing team to implement content recommendation I framed the problem as a very large and complex A/B testing problem. It was a framework they all understood. Of course, machine learning allows those tests to be round thousands of times to build an effective model and then that model can both evaluate content context in real-time to provide recommendations as well as gather additional data that can be used to refine the predictive ability of the model.
If you understand your domain and your data well enough, you can explain the context of the business problem, in ways that domain subject matter experts will understand. That’s important to getting early traction. These are the people who are going to help you identify the key problems to solve, define targets, and allocate resources
Domain expertise superpowers success
Technology alone is not the solution to the most challenging business problems. A deep understanding of the problem space coupled with awareness of the key data is always the starting point. The application of various technologies can then be tested to see which are the best tools to be applied.
As marketers, we need to be thoughtful about avoiding the impulse to chase the latest technology. While it’s exciting to apply new tech to our pervasive challenges it’s only technology in the context of process, data, and people that make things better. Understand those things first and the right technology will reveal itself.
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