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Businesses Still Lack Understanding Of AI Use Cases, Finds Study

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Many businesses are well underway with AI experimentation, but many still lack an understanding of the use cases to deliver business value and the data infrastructures for making AI a success across the enterprise on a sustainable basis. This is according to a Mindtree report, which surveyed over 650 global IT leaders from key business markets.

On a positive note, the study found 85% of organizations have a data strategy and 77% have implemented some AI-related technologies in the workplace, with 31% already seeing major business value from their AI efforts. From that sense, they are closer to achieving their objectives to industrialize AI, but many can do more to gain real business value, the study noted.

Greater focus is needed on use cases that deliver business value

When implementing an AI strategy, there’s a pressing need for use cases to demonstrate business value. The survey revealed that 16% of enterprises globally focus on a pain point and then define a use case, with smaller organisations (13%) being less likely to focus on the business impact compared with their larger counterparts (18%).

With all the pressure to harness AI, many organisations are experimenting but not all have found the formula to deploy at scale and add significant value, the report said.

The survey found there are certain business functions such as sales (35%) and marketing (32%) gaining the most value from AI, as it accelerates the delivery of improved customer experiences. The most popular technologies deployed by global organisations are machine learning (34%), chatbots (34%), and robotics (28%).

Success with AI: Merging of experimentation, agility and skills

AI is already delivering measurable business benefits, but the majority of enterprises have yet to find a formula for repeatable success. An important requirement for enterprises to successfully start their AI journey is to experiment with different use cases and technologies with agile and rapid innovation methodologies. Just over a quarter (29%) of the enterprises surveyed said they are agile enough to rapidly experiment with AI, with large organisations (39%) having an edge compared to their smaller counterparts at 19%.

Progressive enterprises are spending 25% of their IT budget on digital innovations with a focus on use case definition, experimentation, and operationalization for scale. Businesses also understand the need to upgrade and refresh their skills to capitalize on AI. 44% say they will hire the best talent available externally, 30% have partnerships with academia, and 22% run hackathons to solve data challenges and identify potential candidates.

The AI-led enterprise – it’s all about data

Finding the right use cases and building alignment and support for AI initiatives are critical, but data is the make-or-break variable when it comes to scaling AI across the enterprise. Businesses need to modernize their data infrastructure, architectures and systems, along with an overarching data strategy and robust data governance processes.

The survey revealed more than half (51%) of large enterprises say they don’t fully understand the data infrastructure needed to implement AI at scale, while 6 out of 10 organisations believe their data infrastructure and architectures are immature and not well positioned to deliver business value.

“The potential of AI to disrupt, transform and rebuild businesses is clearly felt in the C-Suite, even if it is not yet fully understood,” said Suman Nambiar, Head of Strategy, Partners and Offering for Digital at Mindtree.

“Business and technology leaders are increasingly expected to prove business value, unlock the power of their data, and define their AI strategy and roadmap. To thrive in the Digital Age, businesses must be agile and unafraid of failure. They must also constantly refine their understanding of how AI will give them a competitive edge and deliver real and measurable business value to maximize their investment in these disruptive and powerful technologies,” he concluded.

  

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