Artificial Intelligence (AI) and Machine Learning (ML) are the keystones of every data-driven organization today. These technologies have already changed our everyday lives in countless ways. While we envision a fully AI-powered world of self-driven cars and self-ordering refrigerators, we have yet to leverage AI techniques’ true potential entirely.
Cognitive technologies have disrupted the world of work. AI adoption more than tripled in the last year, with organizations across industries exploring actual use cases of cognitive technologies.
However, if we delve deeper into the scenario, we have some equally exciting numbers. More than 50 percent of AI leaders don’t clearly understand 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 relatively 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, scalable infrastructure to support these demanding workloads.
Naturally, many businesses exploring AI techniques are increasingly moving these workloads to the cloud for obvious reasons. However, increasing such projects’ relevance and success rates depends on many factors.
Will AI or ML remains a Gordian Knot? It doesn’t have to. Many organizations start their AL/ML journey through suitable pilot projects before moving to production. A good start is thus highly 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 undoubtedly 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 for solving that business problem using technology—working backward. 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 businesses.
Start small: Initial excitements aside, trying to solve a large-scale problem through the AI pilot project may be risky. Pilots’ projects are all about experimentation and prototypes. Limiting the project’s scope gives you better control over execution and 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 automate ‘jobs.’
Define and measure the outcome: Set clear metrics to assess the progress and performance of the project. Define the desired state of affairs well in advance 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 specific to your industry. 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 a more long-term impact on your organization.
Collaborate with credible partners: AI resources are expensive and 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 a large team might eventually backfire before you are sure about the RoI.
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 up-and-coming applications across industries—from manufacturing to healthcare to retail and banking. Nevertheless, every organization must find its unique components while crafting a successful AI strategy.
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