Developing An AI Strategy For Innovation At Scale

Hong Cao, Director, Consulting, at Ernst & Young (EY)

Despite the significant impact, the COVID-19 pandemic accelerated the world’s digital transformation and changed the way that we live, work, learn and play.

During the pandemic, it was evident that countries and industrial sectors that harnessed digital technologies better were more resilient to the lockdowns than others that did not do so. Countries that innovated more quickly also found themselves in a better position to manage their pandemic logistics, such as virus tracing, responding to outbreaks more quickly and having better enforcement of social-distancing policies. People also spent more time on digital technologies and interacted more often through digital channels; they also became more receptive toward new digital solutions, apps and cloud technologies.

These changes paved the way for the next big opportunity for organizations to fast-track their digital transformation post-pandemic.

However, for digital transformation to be truly high impact, organizations need to relook at their artificial intelligence (AI) strategy, which holds the promise to bring the right insights to the right people and at the right time for their informed decision-making.

According to the EY enterprise AI survey 2022, which covered over 1,700 executives around the world, 15% of the companies surveyed expect AI adoption to be scaled across their organizations. Further, 96% of the respondents believed that their organization’s AI expenditure will increase in the next 18 months, with 45% expecting their annual AI expenditure to increase by 50% or more. These findings underscore the potential for organizations to leverage and scale up their use of AI .

However, to ensure that the AI innovation is focused and has good return of investment, there is a need for an actionable AI strategy. This strategy needs to be created, communicated, endorsed by the C-level leaders and supported by the business function leads. The AI strategy should encompass four key areas: the roadmap, infrastructure, governance and organization.

AI Roadmap

The roadmap provides a pathway to identify the most promising AI use cases and incorporates them into future development cycles. Often, most of the use cases are contributed bottom-up from various functions across the organization, with the key attributes of each use case documented.

Some of the core attributes are around the business context, description of the current challenge or opportunity, description of how AI can help and the data sets needed. Each use case is then evaluated for its potential business impact and ease of implementation. For business impact, the use cases are assessed on their alignment to business strategy and the quantum of positive impact to key business metrics upon successful development. For ease of implementation, several aspects like complexity of algorithm, availability of data and technology, ease of integrating into the business process are considered.

An implementation roadmap prioritizes the development of use cases that score high on ease of implementation and have a high business impact. It is always good to start with low-hanging and high-impact use cases for implementation. These quick wins create confidence and awareness within the organization to sustain the AI initiative and attract more people to be involved in the AI journey.

AI Infrastructure

This entails a plan to identify IT infrastructure gaps and bridge them to support the AI development. Data platforms should support not just AI development, but also deployment and ongoing monitoring. Without the foundation of a realistic deployment and monitoring strategy, most AI solutions do not deliver the impact they were designed for and stop early at their research phase. Additionally, there are other common issues that a data platform can fall short in supporting real-time streaming-based AI consumption, AI cataloging, AI collaboration, and AI model performance monitoring and supercomputing needs.