Most artificial intelligence (AI) attempts fail. They do not fail because of the tool, the core software, or bad data. They fail because they do not integrate into business and are more of a hindrance than an aid.
This is not just an AI problem; it is true for most types of automation. Projects fail because the people building the solution have no idea about the actual goal, nature and dependencies of their current operations, or even whether those operations are optimized. (In some ways suggesting that AI failure may be more beneficial – if you have a bad process in place, the last thing you want to do is speed it up!)
To achieve success, you must first set the process or operation, fully define it, set a set of achievable goals for the AI project and team, and then execute it. That’s why I’m interested BCG, a more AI – oriented consultancy; his tactics arose out of efforts to help companies improve operations with a strategic goal in mind.
We talk about how to do AI projects properly by using BCG’s focus on whatever process you plan to improve with fixing and optimizing AI.
Speed vs direction
While doing competitive analysis, I attended a lecture at the annual meeting of the Association of Competitive Information Professionals (SCIP) that impressed me. The speaker set up an X / Y chart showing speed vs. direction and argued that most companies focus on speed first – often leading a company to go in the wrong direction faster. It may seem obvious, but he insisted that you must be clear on the direction you want to take before you can accelerate.
AI has an advantage of speed, and any kind of automation. It can greatly speed up whatever is being modified. If it changes good practice, you will get more good results. If it changes to a bad one, you will get worse results faster, which can lead to disaster.
The historical IT problem
The first time I experienced an example of this was when I specified one of the first CRM programs at IBM. When IT came back with the results, not only did they fail to meet my specifications, they solved almost every problem that was more difficult for me. IT often did not fundamentally understand the process we were trying to automate and did not require input outside of the initial application. These problems were common. Sometimes things were so bad that jokes were running around about them having to sacrifice chickens to get projects that did what they intended to do.
Since then, IT has become better integrated with other areas or serves as more of a large – scale operations company – where software development or solutions can be created closer to, and often in, the operating units. (This is why operating units have also learned the technology.) With AI, because it’s still new, project teams tend to stay isolated and focus only on rapid deployment. That brings us straight back to that speed vs. direction issue: speed does not guarantee direction or quality result.
BCG demonstrates a best practice approach. First, understand the nature of the process being automated with AI. Then, ensure the optimization and efficiency of that process. Then, once everyone has understood the problem, the tools, the goals, and the best way to achieve them – that’s when you team up and figure out a solution that you can roll out. BCG also understands the skills you need for today’s merged human – AI partnerships; it can provide frontline staff and line up qualified workers to ensure their success. (One recurring issue: if you do not understand technology, you do not know what types of skills are best to support it.)
As a result, BCG AI projects rarely fail. That’s because speed isn’t BCG’s main focus, it’s quality. BCG ensures the result before implementing any AI solution, and clients get the results they want. (Note: BCG’s specialist areas are retail, transport, healthcare, energy, industrial goods, and manufacturing; as well as any consultancy, BCG will perform best in its target areas.)
AI is still evolving, and many people who should know better are spending millions of dollars trying – and failing – to use it successfully. No matter how cheap an attempt like this, if it fails, it was too expensive. And even if the implementation of AI runs over budget, if it succeeds, that overspend can be ignored.
BCG’s focus on quality and understanding of speed – and the way it mixes people and AI for higher productivity – could greatly enhance the success of these high-cost efforts if applied generally. And they would avoid the promise of AI instead of turning it into the train wreck that it all too often does.
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BCG shows how to use AI the right way
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