/The actual cause companies are failing at AI

The actual cause companies are failing at AI


The Actual Cause Companies Are Failing At AI

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

The Actual Cause Companies Are Failing At AI

5 building blocks your company needs for successful digital transformation

The Actual Cause Companies Are Failing At AI

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

The Actual Cause Companies Are Failing At AI

The Actual Cause Companies Are Failing At AI

The Actual Cause Companies Are Failing At AI

More about artificial intelligence

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

The Actual Cause Companies Are Failing At AI

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. 

The Actual Cause Companies Are Failing At AI

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.  

“Data is the neglected x-factor that hinders enterprises in moving from successful experimentation to making AI-led business a way of life,” said Suman Nambiar, head of strategy, partner alliances, and offerings at Mindtree. “Many enterprises now appreciate the need for data to train AI models, but a majority say they still don’t understand the data infrastructures and architectures required to ‘industrialize’ AI at scale.”

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.

“Take ‘Fail Fast, Fail Early’ seriously as a motto when it comes to AI. As any data scientist will tell you, you have to experiment, test hypotheses, go down some blind alleys, learn from them and use these to find success,” Nambiar noted. “Having processes and methodologies for agile, rapid experimentation that delivers results quickly and at a relatively low cost is critical for success. Do not expect to draw up the perfect AI strategy for your business at the start and to execute it—start with the assumption that you will have to evolve and change this as you progress.”

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. 

“Focus on data strategy, infrastructure, architecture and governance—these get almost no mention in the media, but in our experience, these make the difference between successful proofs of concept that do not scale vs enterprises that are cognitive and truly AI-led,” Nambiar said. 

For more, check out Why employees must evolve to keep up with the digital workforce on TechRepublic. 

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