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Management ‘Bought the AI Hype’, but Organizational Readiness a Hurdle

IFS commissioned research shows value creation lags AI promise without the right planning and application and AI hype considered to be too high 

New research from IFS, has found that executive and board leadership have ‘bought the AI hype’ but organizations are unable to deliver operationally on expectations. The new global study of 1,700 senior decision makers, Industrial AI: the new frontier for productivity, innovation and competition, found that the promise of AI is being held back by technology, processes and skills. Half of respondents remain optimistic that with the right AI strategy, value can be realized in the next two years, and a quarter believe in the next year. 

Expectations failing to meet reality 

84% of executives anticipate massive organizational benefits from AI, with the top three areas AI is expected to deliver value in being high-impact: product & service innovation, improved internal & external data availability, and cost reductions & margin gains. The AI hype has become so high that 82% of senior decision-makers acknowledge that there is significant pressure to adopt AI quickly. However, this same group of respondents state that they are concerned that a failure to plan, implement and communicate properly means AI projects will stall in pilot stage.  

Many organizations have not prioritized elements of development, nor have the infrastructure required to reap the rewards or the skills to deliver on that promise. The study found that over a third (34%) of businesses had not moved to the cloud. While this is not essential to AI adoption, it is indicative of an unprepared enterprise unlikely to be able to scale AI across their business. According to IFS, a robust Industrial AI strategy requires a potent combination of cloud, data, processes, and skills. 80% of respondents agree that the lack of a strategic approach means they have insufficient skills in-house to successfully adopt AI. This sentiment is seen elsewhere in the research with 43% of respondents rating the quality of AI resources in their business, in terms of human skills, as passable and not where it needs to be.  

Outlook optimistic but planning needed  

The unfortunate reality of the skills gap means that in terms of AI readiness, many businesses are falling behind. IFS found that nearly half of respondents (48%) were most likely to say that they are gathering proposals and were much less likely to have a clear strategy and perceivable results (27%). A fifth of respondents are in the research phase, with uncontrolled tests taking place and a further 5% are lacking a coordinated approach and do not have anything in motion yet. Despite initial challenges, there is still optimism with respondents most likely to feel AI could make a significant difference to their business in 1-2 years (47%), and a further quarter (24%) believe it could be within a year. 

In particular, respondents are most optimistic about the impact of AI in smart production and/or service delivery on effectiveness & business and operational management (22%) in the future. One fifth see the biggest impact being on innovation with new products and services (20%), growth & business model decision-making (20%), empowering people and increasing talent retention (19%), and customer experience and customer service (19%). 

Action needed on data readiness 

To reap these benefits, enterprises need to leverage the most strategic asset they have – their data. The right data volume and quality is critical for the success of AI applications. Respondents recognize how important real-time data is to successful AI projects, with over 4 in 5 (86%) stating this. Yet despite this recognition, less than a quarter (23%) of respondents have completed their data foundation with it supporting both data-driven business decision making and real time response to changes, suggesting that more work needs to be done to get data AI ready. Moreover, under half (43%) of respondents have majority structured data, with some unstructured. 

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