Orchestrating Business Outcomes With AI Powered Enterprise

The author decodes how to build an AI powered enterprise that drives excellent business results

30 March, 2021 |  Manas Agrawal

There is some bit of AI in everything we do. However, leveraging AI in certain processes is way different from building an AI powered organization. First, the bits and pieces do not add up. Second and most importantly, in AI powered enterprise, AI is a means to deliver business vision. The remit flows from top and every function carries it forward through a streamlined ecosystem.

Many businesses make the mistake of building a siloed version of AI implementation and get frustrated when the outcomes are nowhere close to expectations.

Recently, I was speaking to a global manufacturing enterprise. Their vision was to meet the aggressive business goals with lean, streamlined and optimized ways of working – right from factory floors to supply chains and, marketing and distribution. In short, a complete overhaul driven by a comprehensive AI strategy.

This conversation got me ruminating, what does it take to build an AI powered enterprise? As I decode this jigsaw, the following emerged as the holy grail:

Defining The Business Strategy & Path To AI Driven Transformation

The path to AI driven transformation is not linear. For instance, if you are a manufacturer, factory floor efficiencies, health of equipment, inventory management and, supply chain effectiveness are major drivers to meet business objectives. The AI strategy must weave each function into a cohesive whole of business value chain, instead of piecemeal or patch up approach. For example, predictive maintenance of equipment is tied to production and inventory. A system failure will impact the entire chain. Hence intelligent automation and predictive analytics needs to inform every function for the wheel to run its course. A bot here, a diagnostic algorithm there, would not fix the entire system.

Contextualizing Framework For Customized Solutions

Frameworks and proven methodologies bring strength to AI implementations, but over reliance on templates can be fatal. What works for one enterprise may not fit the bill for the other. While best practices bring down the time and cost of implementation, aligning the frameworks for specific context is crucial. AI algorithms are trained for specific purposes. The objective in each scenario differs and so must the approach to creating the solution architecture and implementation.

Eliminating Data Bias

Data engineering capabilities including data classification, simplification and real time processing are key to AI strategy work. After all, data is the fuel for any AI algorithm.

Quality of data informs the outcome and when there are inherent biases, the results can hardly be neutral. There are innumerable instances of gender prejudices in onboarding female candidates in the manufacturing sector, for instance. That is because our historic data sets are skewed. A conscious and concerted effort to eliminate such bias, setting the right foundations on ethical grounds is critical. This calls for alignment to industry specific regulations and standards.

Shaping Mindsets For AI Adoption

As I mentioned earlier, AI strategy takes its time to bear fruit. Do not expect instant gratification. Successful AI implementations have always been top-down mandate to which each business function must confirm and align. An overarching system that connects different functions through intelligent processes must be adopted by teams, across the organization for measurable impact.

Adequate Provisioning Of Infrastructure For Scale & Elasticity

When an organization moves inventory management to cloud, it assumes easy scalability and agility. Expectations are for each and every application to perform at peak despite a surge or ebb in demand. The solution architecture thus needs to provision the infrastructure adequately to meet the data engineering requirements. And, the system must be alert for course correction as per changing business environment for consistent outcomes.

The Devil Lies In The Detail

It is not the technology that fails. When the expectations are not clearly defined and technology is called in to do a quick fix, the outcomes are less than desirable. The devil lies in the detail. Success of AI strategy depends on its holistic approach.

The fate of a ship is in the hands of the captain, it will take you where you want to. Decide on the destination before you set out on the journey. I can assure you; the experience is nothing less than exhilarating!

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