Overcoming Obstacles: Bridging Organizational Readiness and AI Expectations

Recent research conducted by IFS, the global cloud enterprise software company, has highlighted a concerning trend: while executive and board leadership have enthusiastically embraced the potential of AI, many organizations struggle to meet operational expectations. The study, titled “Industrial AI: the new frontier for productivity, innovation, and competition,” surveyed 1,700 senior decision-makers globally. It revealed that despite the promise of AI, its realization is hindered by technological constraints, inefficient processes, and skill shortages.

Expectations diverging from reality

84% of corporate leaders foresee substantial benefits from AI for their organizations, highlighting three key areas where AI is expected to make a significant impact: driving innovation in products and services, enhancing the accessibility of internal and external data, and achieving cost reductions and margin gains. The heightened excitement surrounding AI has led 82% of senior decision-makers to acknowledge considerable pressure to rapidly integrate AI technologies. However, these same individuals express concerns that inadequate planning, implementation, and communication may lead to the stalling of AI projects during the pilot phase.

Furthermore, many businesses have not prioritized essential aspects of development, lacking both the necessary infrastructure and skills to fully leverage the potential benefits of AI. The study revealed that over a third (34%) of companies have yet to transition to cloud-based systems, signaling a lack of readiness to effectively implement AI initiatives across their operations. According to IFS, a robust Industrial AI strategy requires a comprehensive approach that encompasses cloud infrastructure, data management, efficient processes, and skill development. This sentiment is echoed by 80% of respondents who acknowledge a lack of strategic planning and insufficient in-house skills as barriers to successful AI adoption. Additionally, 43% of respondents rate the quality of AI resources within their organizations, particularly in terms of human skills, as passable and below the desired standard.

Christian Pedersen, Chief Product Officer, IFS, commented: “AI is poised to become the most transformational enterprise tool ever seen, but our research reveals that there are still fundamental misunderstandings about how to harness its power within an industrial setting. It is telling that AI is expected to significantly reduce costs and raise margins, but a lack of robust strategy means most businesses are under-skilled and under-prepared to achieve these ambitions. We built IFS.ai specifically with these challenges in mind. AI value simply will not be found in a single AI capability but instead by delivering AI across all products and business processes. This supports customers’ decision cycles and provides the data and AI services required to realize value faster.”

Pedersen continued: “Achieving this at scale needs a clear-eyed strategic focus, including the high-impact use cases specific to their industry, having a cloud-based infrastructure in place which has industrial AI embedded, and investing early in developing the skills needed. Adopting this approach will turn the tide of disillusionment and deliver the benefits that boards and the C suite are demanding.”

Optimism prevails, yet planning is essential

Regrettably, due to the skills gap, many businesses are falling behind in terms of their readiness for AI adoption. IFS discovered that nearly half of the respondents (48%) are primarily engaged in gathering proposals, indicating a lack of clear strategy and tangible outcomes, with only 27% reporting a well-defined strategy with perceivable results. Furthermore, one-fifth of respondents are still in the research phase, conducting uncontrolled tests, while an additional 5% lack a coordinated approach and have yet to initiate any action. Despite these initial challenges, there remains a sense of optimism, with 47% of respondents believing that AI could significantly benefit their business within 1-2 years, and a further 24% anticipating benefits within a year.

Particularly, respondents express optimism about the potential impact of AI on smart production and/or service delivery, which could enhance effectiveness and streamline business and operational management (22%). Additionally, one-fifth of respondents foresee significant impacts on innovation, growth, business model decision-making, talent retention, and customer experience and service.

Measures are necessary to address data readiness

To unlock these advantages, businesses must harness their most valuable asset: data. Adequate volume and quality of data are crucial for AI applications to succeed. Respondents acknowledge the significance of real-time data in AI projects, with over 86% emphasizing its importance. However, despite this acknowledgment, less than 23% of respondents have established a solid data foundation capable of supporting both data-driven decision-making and real-time responsiveness to changes, indicating a need for further efforts to prepare data for AI integration. Furthermore, fewer than 43% of respondents possess predominantly structured data, with some remaining unstructured.

Pedersen commented: “The lack of maturity at the data foundation layer needs to be addressed as part of an overall AI strategy, otherwise AI simply will never be the magic bullet that can turbocharge the enterprise. Clearly enterprises need support on data management and migration. While AI is seen as a shiny new tool that will revolutionize business, like all technology, it is never that simple. The power of Industrial AI is that it can touch all facets of a business from product innovation and customer experience to productivity and ESG. Its potential is massive if executives and organizations can combine vision, strategy, technology and skills. Now is the time to step back, take stock, and build a true Industrial AI plan and turn the hype into reality.”

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