HFS’ 2019 State of Industry survey results portray total confusion surrounding cognitive technologies and machine learning (ML) in the “industrial” space of manufacturing and energy (including oil and gas):

• The C-suite is investing in and paying attention to cognitive and ML technologies, but it’s not prioritizing them when pitted against other incentives like reducing operating costs; in fact, cognitive and ML technologies are the bottom priority for most.
• The technologies are clearly nudging their way up the list for executives, who want to invest even in the midst of their confusion.
• Our survey also highlights that uncertainty surrounding financial investments is a major barrier to the industrial sector achieving digital transformation outcomes, which is unsurprising considering the confusing aura surrounding cognitive and ML.

It’s time to give these technologies a dedicated place at the industrial C-suite’s table and in firms’ holistic strategies, or else risk failed projects, sunk investments, and falling behind the industry’s pioneers.   

Cognitive and ML technologies receive little C-suite priority, which is worrying considering executives’ desire to invest

Driving down operating costs remains a firm focus in manufacturing, with over half of executives ranking it as their number one priority. Adopting cognitive and ML technologies, however, remains low on the priority list, with only 10% keeping it on that pedestal. Forty percent (40%) see it as their lowest out of 7 priorities. This also holds true in the energy space, where 50% rank it lowest; see Exhibit 1.

It’s unlikely that this mindset will change without a push; 90% of energy executives in the same survey are happy (70% very satisfied and 20% satisfied) with their state of cognitive and ML priority, and a similarly large majority are happy with their mindset of driving down operating costs. Manufacturing exhibits the same state of contentedness with their cost and cognitive priorities.

Exhibit 1: Driving down operating costs dominates manufacturing, while both manufacturing and energy place little interest in cognitive technologies and ML

Source: HFS Research, State of Industry 2019

Despite the apparent lack of priority, there is also an apparent lack of clarity among industrial C-suites around cognitive and ML technology

In both energy and manufacturing, large majorities of the same executives cite significant or some focus and investment (see Exhibit 2). Adding to the picture, HFS’ 2019 Business and IT Industry survey found that the two industries are very much in the implementation phase when it comes to the same technology.  

Industrial strategists seem to be confused about their direction, priority, and confidence in these cognitive and ML technologies. They need to re-think their holistic strategies with far greater clarity; cognitive and ML technology must have a place at the highest level of industrial enterprises’ minds.

Leading players in the industrial sector are pulling ahead with their cognitive adoption. Proactive cement manufacturers are ingraining AI throughout their core operations and supply chains in view of the existential threat to their business posed by both damning environmental concerns about the cement industry’s emissions and by alternative materials development. At a high level, these applications extend through both process (such as cement) and discrete manufacturing (car or electronics production, for example).

Also, in the process industry, Azure AI is facilitating predictive modeling of equipment like heat exchangers and pumps. On a novel tangent to improving industrial operations, Eightfold’s ML is aiming to solve manufacturing’s talent shortage through retraining and retaining insight.

Exhibit 2: Across energy and manufacturing, the vast majority of executives are putting significant, or at least some, investment and focus on AI and ML, despite Exhibit 1 showing a lack of priority…

Source: HFS Research, State of Industry 2019

The Bottom Line: Confusion and investment uncertainty is a major barrier to the industrial sector leveraging cognitive and ML technologies. Executives must build a concrete, enterprise-level strategy that incorporates the true value of these technologies.

Fifty-four percent (54%) of manufacturing executives cited uncertainty about the necessary financial investment as a barrier to achieving their digital transformation objectives—the biggest barrier among all possible factors; 34% feel the same way in the energy industry.

• Industrial enterprise executives must revitalize their conversations with each other and between them and their vendors and service providers; they must redefine holistic strategies to give cognitive and ML technologies a place at the table.
• The industry must clarify the strategic outcomes and a picture of the real business value on offer from these technologies if it ever hopes to leverage their potential.
• Strategic roadmapping must ingrain cognitive technology in activity throughout the value chain, stakeholders, and wider ecosystem (as we see in the cement industry) at both the enterprise and ground levels and in the parallel adoption of emerging technology like the IoT.

The manufacturing and energy sectors need to move away from the idea that these technologies alone are the answer to their problems. HFS has said it before, and we’ll say it again: Start with the target outcome or start with a business problem, and let the vendors sort out the technology that will get you there. Industrial sectors are no different from any vertical in this respect.