Adoption of AI in Industrial Companies: Challenges and Opportunities 

A recent study conducted by IFS, a global leader in cloud enterprise software, has highlighted a concerning trend: while executives and board members eagerly embrace the potential of Artificial Intelligence (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 worldwide. It revealed that despite the promises of AI, its full potential is hindered by technological constraints, inefficient processes, and a lack of skills. 

The Benefits and Barriers to Rapid AI Implementation 

84% of business leaders expect substantial benefits from AI for their organizations, highlighting three key areas where AI is anticipated to have a significant impact: 

• Driving innovation in products and services; 
• Enhancing access to both internal and external data; 
• Achieving cost reductions and margin gains. 

The widespread enthusiasm surrounding AI has led 82% of senior decision-makers to acknowledge considerable pressure to rapidly integrate AI technologies. However, these same individuals express concern that inadequate planning, implementation, and communication could cause AI projects to stall during the pilot phase. 

Additionally, many companies have not prioritized essential aspects of development, lacking both the infrastructure and the skills necessary to fully leverage AI’s potential benefits. 

The study revealed that over a third (34%) of companies have yet to transition to cloud-based systems, indicating a lack of readiness to effectively implement AI initiatives in their operations. 

According to IFS, a robust industrial AI strategy requires a comprehensive approach that includes cloud infrastructure, data management, efficient processes, and skills development. 

This sentiment is echoed by 80% of respondents who recognize the lack of strategic planning and insufficient internal expertise as barriers to successful AI adoption. Furthermore, 43% of respondents consider the quality of AI resources within their organizations, particularly in terms of human skills, to be mediocre and below the desired standard. 

Unfortunately, due to the skills gap, many companies are lagging in AI readiness. IFS found that nearly half of the respondents (48%) are primarily engaged in gathering proposals, indicating a lack of clear strategy and tangible outcomes, while only 27% report having a well-defined strategy with noticeable results. 

Additionally, one-fifth of respondents are still in the research phase, conducting uncontrolled tests, while another 5% lack a coordinated approach and have yet to take any action. Despite these initial challenges, a sense of optimism persists, with 47% of respondents believing that AI could bring significant benefits to their company within 1-2 years, and a further 24% expecting benefits within a year. 

Commentary by Christian Pedersen 

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 on the Benefits of AI in Manufacturing and Services

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

To unlock these benefits, companies must leverage their most valuable resource: data. 

A sufficient volume and quality of data are crucial for the success of AI applications in Enterprise Resource Planning and Enterprise Asset Management. Respondents recognize the importance of real-time data in AI projects, with over 86% emphasizing its significance. 

However, despite this recognition, 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 the need for further efforts to prepare data for AI integration. Additionally, less than 43% of respondents primarily have structured data, while some still deal with unstructured data. 

Data management is the basis for AI success  

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.”