Think through your customer and supplier relationships, as well as your operational efficiency to identify the best areas for artificial intelligence.
Like many organizations, your company may be pouring resources into the development of artificial intelligence. Investment in the space is skyrocketing. IDC predicts that by the end of 2018, companies will have spent 54% more on than they did in 2017.
However, for many, that investment has yet to pay off. A recent survey from MIT Sloan Management Review and Boston Consulting Group found that less than 5% of organizations have incorporated extensively into their processes. That’s no surprise, given how difficult a successful implementation can be. Creating and iterating on a solution, adapting your business processes to utilize it, getting buy-in from employees; each step in the process is challenging, and any mistake can be costly.
On top of that, many executives don’t understand what really is in a business context. When businesses talk about deploying today, they’re usually talking about building mathematical models that can be trained on data to make decisions. That’s a specific subset of called machine , and it’s far from the sort of generalized intelligence the term “” implies. Each machine model is specialized to make a certain set of decisions based on certain sets of data.
If you’re going to invest the resources necessary to make your models a success, you want to make sure that investment is going where it’ll have the most impact. Today’s leaders didn’t start by deploying models willy-nilly. They started where they knew machine could make the biggest difference.
Analysts usually apply the Five Forces concept to determine the overall competitiveness of an industry. However, that can also be a guide for businesses looking for where to test out model-driven solutions. By examining the competitive forces at play in your industry, you can decide which competitive “levers” are most likely to increase profitability, and how you can deploy models to help.
The bargaining power of buyers tends to be high in industries where products are undifferentiated and switching costs are low. Companies can deploy machine models to create differentiation and also make changing loyalties inconvenient, decreasing the bargaining power of buyers.[…]