5 best practices for scaling AI in the enterprise 5 best practices for scaling AI in the enterprise
AI has entered a new phase. The last few months have seen an explosion in generative AI. The ability to use text to automatically... 5 best practices for scaling AI in the enterprise

AI has entered a new phase. The last few months have seen an explosion in generative AI. The ability to use text to automatically write narratives and create art is maturing very fast. Early applications of these new capabilities in co-authoring software, writing news articles and business reports, and creating commercials are already emerging. We can expect entire industries — from software engineering to creative marketing — to be disrupted.

At its core, AI has become the best prediction machine possible. We have seen AI being built not only into large applications like autonomous driving, but also into hundreds of tools and utilities for everyday use. AI has reached the right inflection point on the maturity curve to drive mainstream, significant and varied enterprise applications. While AI is disrupting how we live and work, for most enterprises, true innovation comes not from experimentation but from industrializing AI at scale.

Here are five best practices for making the most of emerging AI capabilities across the enterprise.

Start with the question, not the answer

One of the most important challenges of implementing AI is defining the business problem the enterprise is trying to solve. As the saying goes, don’t end up with an answer that’s looking for a question. Simply deploying new forms of technology isn’t the right approach. 

Next, examine the issues and determine if AI is the best way to tackle the problem. There are other digital technologies well adapted to simple problems. To help ensure success, define the business issue clearly and determine what course to take at the outset — some may not need AI.

Plan for AI-based transformation to be different from automation

In automation, the end-to-end process is disaggregated and divided into smaller parts. Each part is then digitized, and the parts are then reaggregated into the value chain. Automation delivers efficiency, time to market, and scalability — but the underlying work and process remain the same.

On the other hand, when enterprises leverage AI to transform, entire value propositions are reimagined, the customer experience changes, the processes are redesigned end-to-end and the work remaining becomes fundamentally different from before.

So, AI-based transformation is as much about designing a new operating model, cross-skilling the workforce and integrating it into upstream and downstream processes as it is about neural nets and model management. It’s important to note that AI in the enterprise is 20% about technology and 80% about people, processes and data.

Create a foundation of data

We are moving from a world that is data-poor to one that is data-rich. We are embedding more and more telemetry and digital devices into our operating environments that open up new sources of data previously not available.

With AI, data that traditionally sat in unstructured formats are now easily extracted, converted and put to productive use. As a result, data that is now available to support business operations and decision-making is unlike anything we have ever had.

Building a foundation of data is critical to harvesting its benefits. Managing data not just in terms of the core data infrastructure but also with an eye to quality, security, permissible purpose and granular access is key.

Focus on digital ethics

With the expanding footprint of ambient intelligence comes the associated risk of security breaches, model drifts, unintentional bias and unethical use. As use cases of AI expand and proliferate and vast amounts of data are collected and managed centrally, it opens up ability for breaches in security.

Model drifts happen when AI models — as they are tuning themselves with new data — end up drifting away to lower accuracy results. If not purposefully designed, bias can often be unintentionally introduced into AI systems. AI’s use must be overseen to ensure it is used ethically.

Digital ethics must be included upfront in the design and architecture of the system. Adding it as an afterthought isn’t a comprehensive approach and leaves too much room for harmful exposure. Rearchitecting for ethics, in the end, can be a costly and wasteful exercise.

In the long run, companies that build and succeed with industrialized AI systems will not get there by chance but by focusing on building digital ethics and governance into their platforms right from the start. Many organizations will likely have a chief ethics officer or ethics subcommittees at a board level in the near future.

Change management and culture are key to success

With AI, we are driving business pivots, not simply increasing efficiencies or reducing costs.

The technology of AI itself is not difficult to implement. What is challenging is the significant integration, contextualization, governance and adoption necessary for success. Best-in-class AI projects in production require a thoughtful process of reimaging the business, seamless integration into upstream and downstream processes, a fundamental change in the way we work and user technology adoption. This requires a company culture of change, learning and agility.

In the end, culture will separate winners from losers in deploying AI.

Leveraging AI benefits everyone

Industrialization and automation have changed the way we work and live. The opportunity with AI is to go beyond the constraints of pre-defined and already-known rules-based automation. As we do that, AI will disrupt entire businesses, and new business models will emerge. AI will become critical to delivering sustainable business and durable advantages. 

By following these five best practices, enterprises can start their journey towards fully benefitting from the promise of AI.  

Sanjay Srivastava is chief digital strategist at Genpact


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