The real reason businesses are failing at AI

While the majority of businesses have a data strategy, many still fail to successfully yield tangible results. Here’s why.

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At the 2019 MIT CIO Symposium, Jeanne Ross discussed where companies stand when it comes to digital transformation.

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Artificial intelligence (AI) investments are so crucial for business that they now determine the success of organizations. AI optimization allows businesses to generate data insights at a lower cost, expedite the hiring process, customized consumer experiences, and improve security tactics, reported TechRepublic’s Tom Merritt in his Top 5: Ways AI will change business.

All of these benefits are reasons behind why AI will create $2.9 trillion in business value by 2021, as noted in a previous Gartner report, Leverage Augmented Intelligence to Win With AI. Currently 77% of global organizations have some AI-related technologies implemented in the workplace, a Mindtree Study reported on Wednesday.

SEE: Special report: Managing AI and ML in the enterprise (free PDF) (TechRepublic)

The report surveyed 650 global IT leaders to determine how they reach success with AI, and where they need improvement. AI is as popular as ever, with 85% of organizations saying they have a data strategy in place, and 31% reportedly seeing major business value from these AI efforts.

However, one of the biggest mistakes companies can make is implementing technology only for the sake of having the technology—but companies continue doing so, the report found. Only 16% of organizations are focusing on pain points and defining use cases prior to AI deployment, which is a quick way to not only fail at AI initiatives, but also waste time and money.

“Business and technology leaders are increasingly expected to prove business value, unlock the power of their data, and define their AI strategy and roadmap,” Suman Nambiar, head of strategy, partners, and offering for digital at Mindtree, said in a press release. “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.”

To execute successful AI projects, organizations must be willing to both establish use cases, experiment with multiple use cases, and develop agile and rapid innovation methodologies, the report noted. Only 29% of the organizations surveyed said they feel agile enough to quickly experiment with AI, indicating a need for better global business agility ONMA.

The business functions yielding the most value from AI included sales (35%) and marketing (32%); most organizations are taking advantage of AI via machine learning (34%), chatbots (34%), and robotics (28%).

Data continues driving AI use in the enterprise, the report found, but a knowledge gap exists between having a data strategy and understanding one. As previously mentioned, 85% off enterprises have a data strategy, but more than half (51%) of large enterprises and 74% of smaller enterprises said they don’t understand the data infrastructures necessary to deliver AI use cases.

This lack of familiarity with data and AI architectures means organizations need to retrain current staff members with adequate skills. These necessary skills include design thinking (58%), data engineering (58%), and data science (54%), the report found. While 47% of organizations said they are in the process of retraining current staff members, more than half of companies still need to reskill.

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