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Everyone has an opinion on AI these days. You often hear conflicting arguments claiming “AI has arrived and it will be the big game changer” or how “AI is overhyped and the adoption is still in the early stages”. Now, whichever side you are on, it is prudent to start thinking about how AI can help you achieve your future business goals.
Here are 6 things to consider before you plan your AI strategy:
AI should always be a part of your overall business strategy: AI has the potential to fundamentally change the way we work and live but if an AI strategy is just an AI strategy it really won’t help you achieve much. Don’t make AI your business goal instead you need to think how AI will help you achieve your business goals. Before building a strategy start by asking what you want to accomplish with AI. Do you want to increase Sales Performance? Is it improve Customer Experience you want to improve or internal processes like Recruitment? Can AI provide you with insights to improve your product? Are there processes and tasks that AI can make less error-prone and by doing so improve the overall productivity levels of your employees? AI can help you accomplish this and much but for it to succeed AI should be a part of your overall business plan; AI should serve your business goals not the other way around.
Set realistic expectations: AI is not a magic wand that can make your problems vanish. You need to be realistic while setting your goals and assessing what it takes to get there. In order to do so, start by understanding what AI can or cannot do, the groundwork needed for the AI project to succeed and the challenges you will encounter while working on this. While AI can achieve great many things, it is important to remember the technology still has limitations: AI tools are good at categorizing images but they can’t tell much about what is in those pictures, which means, while the AI tool can gauge if the customers are happy with product design it still cannot answer a larger question like whether or not it will succeed.
Readying your data infrastructure to train your AI: Many companies with AI inspirations don’t have good data practices. One of the most common misconceptions that these companies have is that sophisticated AI algorithms alone can generate valuable insights even if there isn’t sufficient data. “A mistake we often see is that organizations don’t have the historical data required for the algorithms to extract patterns for robust predictions.” - Jacob Spoelstra, director of data science at Microsoft observes in ‘Reshaping Business With AI’, 2017 report by MIT. And even if there is historical data, there are hardly any recorded failures. Expecting an AI solution to predict failure when there are no examples to learn from is it bit of stretch really. No amount of algorithmic sophistication can replace the lack of data, furthermore organizations need to understand that recording positive results alone may not be enough for training AI.
Training your AI: Training your AI is mission critical to the success of your AI project. Generating business value from AI is directly connected to how effectively you train your AI algorithms. AI applications usually start with one or more algorithms that become intelligent only upon being trained using historical company data. And its success depends on having well-developed information systems that can pull together relevant training data and highly skilled domain experts who can train the AI systems.
View AI as tool to help people not to replace them: Businesses should view AI as a tool to augment human skills not as replacement for people for it to be effective. Organizations “that view smart machines purely as a cost-cutting opportunity are likely to insert them in all the wrong places and all the wrong ways. These firms will automate existing processes rather than imagine new ones. They will cut jobs rather than upgrade roles.” says Jeanne Ross, principal research scientist for MIT’s Center for Information Systems Research. Companies that view AI purely as a cost cutting tool are more likely to make poor decisions.
AI used in fraud detection for example will detect more number of cases – while you may not need as many people to dig through the data, you’ll still need people to solve these cases. As Ross further points out “like people, machine has natural limits, which tend to leave parts of the tasks — the parts that don’t fit the algorithms well — to people. When a machine detects fraud or predicts customer or employee churn with 90% accuracy, people must address the other 10% — and that will be the toughest 10%. ” Implementing AI may eliminate the need to do mundane and repetitive work but you still need people to do more complex work which requires highly skilled workforce.
Management Buy in: AI adoption has implications on for the management and organizational practices. The cultural change required to implement AI in organization can be daunting. And if you cannot identify solid business cases that can meet the management’s investment criteria it is unlikely that your AI project will take off the ground. Hence it is critical to get your leadership onboard and support it from the beginning.
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