These are the 3 common ways businesses fail when implementing AI. Are you facing the same?
Feb 10 • 2 min read
From HR to marketing, from finding disease symptoms to helping patients with their daily routine, from providing frictionless banking experience to customers to sharing financial advice, from recommending content to offering personalised playlist, implementation of AI is changing industries by presenting business leaders a chance at extensive automation, innovation and cost reduction.
Everything mentioned above is achievable until you get stuck between expectation and reality. Why are we saying this? Because we want you to get the best outcomes of your expectation.
The more we try to understand the problems faced by different industries, the more we find a gap between how AI tools should be used and how they are being implemented and used.
The poor integration of AI into a larger ecosystem, the lack of transparency and the over-reliance on tech is ruining the effortlessness that comes with artificial intelligence.
Let's dig deeper into the 3 areas where most organisations are failing when implementing AI solutions.
1. The First Level- Integration of AI
The problem begins when the AI solution starts taking up more of the resources than expected. Also, you need to understand though they can scale quickly and swiftly move from one area of business to another, ultimately it is the quality of data that helps make the final decision. It is data that powers AI solutions, and if the data quality is not clean, you can't expect it to excel.
Let's take a look at an example. You want the AI system to help you decide between Rose and Lily. The definition of Rose and Lily is fuzzy with each employee having a different opinion about them. What do you think would the outcome be?
2. The Second Level- Employee Awareness
We are witnessing a gap between employees and solutions as not many of us understand how AI works. Not understanding is one facet of the problem, and the other being people not trusting the output of the AI systems.
As a business leader, you not only need to implement right AI solutions but also fill the knowledge gap by preparing your workforce to work with AI systems. Also, if required, put them through the necessary training to make sure the people who operate these AI solutions manage them well. Only then the output could be used in the right way.
3. The Third Level- Data Transparency
We are poor at communication, our language is not perfect, and machines face difficulty understanding it. As a business leader, you need to look into how your data is processed and understand how the algorithm is making decisions. The data needs to be less opaque, and before you deploy an AI solution, as an organisation, you need to have some serious discussion around the expectation and value of the solution.
Let's together look into how we can make AI implementation a success for your organisation.