Artificial Intelligence (AI) and Machine Learning (ML) are the keystones for every data-driven organizations today. These technologies have already changed our everyday lives in countless ways. While we are envisioning a fully AI-powered world of self-driven cars and self-ordering refrigerators, we are yet to entirely leverage the true potential of AI techniques.
Cognitive technologies have literally disrupted the world of work. AI adoption more than tripled in the last one year, with organizations across industries exploring real use cases of cognitive technologies.
However, we have some equally interesting numbers if delved a little deeper into the scenario. More than 50 percent of AI leaders don’t have a clear understanding about the business benefits of AL or ML. Studies indicate that about 85 percent of AI projects eventually fail!
Regardless of how reliable these numbers are, the probability of failure is quite high for a first time AI or machine learning project. This is primarily because of the lack of alignment with business requirements and most importantly, the lack of a scalable infrastructure to support these demanding workloads.
Quite naturally, many businesses that are exploring AI techniques extensively are increasingly moving these workloads to cloud for obvious reasons. Increasing relevance and success rates of such projects, however, depends on a lot of factors.
Will AI or ML remain a Gordian Knot? It doesn’t have to. Many organizations start their AL/ML journey through the right pilot projects, before moving to production. A good start is thus extremely critical in defining the success of the whole journey.
Here’s how businesses can make that great start:
Start with a business problem: Technology for the sake of technology is certainly a bad strategy. If you’re experimenting with AI techniques, identify a compelling business use case—no matter how small it may seem. Set clear goals on how that particular business problem can be solved using technology—working backwards. This is very critical to ensure stakeholder buy-in to start with. Most importantly, take constant inputs from the business before and during the project execution. All successful AI projects are done in close collaboration with business.
Start small: Initial excitements aside, trying to solve a large-scale problem through the AI pilot project may turn out to be risky. Pilots projects are all about experimentations and prototypes. By limiting the scope of the project, you can have better control over execution and end result. It helps to go after specific problems, rather than broad-based goals. If automation is the ultimate goal, aim to automate ‘tasks’ rather than automating ‘jobs’.
Define and measure the outcome: Set clear metrics to assess the progress and performance of the project. Define well in advance the desired state of affairs, to avoid any expectation mismatch among stakeholders. Work closely with business stakeholders and other leaders within the organization to lay out the expectations and measurable gains.
It’s also important to translate the results into business language. Talk in terms of business goals—how the project improved retention and reduced churn, how cash flow is improved etc.
Start from the comfort zone: It might be a good idea to choose a project that is specific to the industry you operate in. This way you can ensure confidence across the board and ensure that the value of the project is quite visible. Not just that, such a project will have more long-term impact on your organization.
Collaborate with credible partners: AI resources are expensive and also hard to find. For pilot projects, it hence makes sense to start with a small team internally and involve a third-party expert as you go along. Setting up large team, before you are sure about the RoI, might eventually backfire.
AI is predicted to be the most transformative technology of the new decade, with its market value about to reach a massive $70 billion in 2020. AI technologies have highly promising applications across industries—from manufacturing to healthcare to retail and banking. Nevertheless, every organization needs to find its unique components while crafting a successful AI strategy.
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