The struggle to implement AI during digital transformation

By William Smith
Digital transformation is a multifaceted beast. While the implementation of more bog-standard items like ERP systems are well understood and fairly easi...

Digital transformation is a multifaceted beast. While the implementation of more bog-standard items like ERP systems are well understood and fairly easily achieved, where does the enterprise stand when implementing emerging technologies such as artificial intelligence (AI) or machine learning? 

Such concerns are leading governments to increasingly step in. One of the major perils lies in overreaching; in implementing too much, too fast and being left with solutions for problems that don’t exist.

Helpfully, analytics firm EXL released its ‘best practices for orchestrating AI solutions’ white paper in November 2019, which recommended a number of methods to best implement AI, including a four stage process. The four, from first to last are: ‘envision and define’; ‘solution orchestration’; ‘operationalization’; and ‘shaping and scaling for the future’.

Succinctly, the first step involves identifying and limiting the scope of any implementation, with the report reading: “Long-term AI strategies are vital, but, the best results come from narrowing that vision so execution can occur in an iterative, agile manner.” 

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The second involves identifying the ‘real-world factors’ that could have a potential impact on the implementation: existing infrastructure, the state of data and the talent present at the company.

Third is related to properly rolling it out across the enterprise, what the report terms as ensuring “[the solution] solves the business problem or delivers the desired outcomes.” That includes determining the method of execution, ensuring change management procedures are in place and identifying areas where the solution can be reused with minimal alteration. 

The final stage, meanwhile, is about continually evolving the AI strategy with an eye to the future, to avoid being left behind; as the report reads: “Organizations should continually evaluate what they want their operations to look like in the future, and how they can leverage their existing AI investment to shape and scale for that vision.”

Whether enterprises will heed such suggestions is yet to be seen. What is certain, however, is that, as as the technology becomes more realistically understood, 2020 represents something of a reckoning for the relationship of AI and business, as a PwC report outlined.

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