Workforces across all industries are undergoing major, tectonic shifts. This is especially true in the industrial sector, according to Bill Scudder, SVP and GM, AIoT Solutions, AspenTech.
Mr Scudder says veteran employees are hitting retirement age, the Great Resignation is affecting workers across many industries, and just filling the labour gap with new recruits fresh out of school won’t do the trick – often, these new graduates come to the job having learned technologies and concepts in school that don’t match the reality of how many organisations’ workflows and systems on the plant floor actually operate.
All of this will prompt a major acceleration of knowledge automation technologies and processes in 2022. Automated knowledge sharing and intelligence-rich applications close the skills gap emerging between departing workers and brand-new ones, by preserving historic domain knowledge and making it widely accessible across teams, regardless of silos. This has the dual benefit of serving as a recruiting tool, as well; the more that knowledge automation makes work easier and gives employees the tools they need to succeed, the more appealing the job becomes to potential recruits.
Industrial data scientists emerge to facilitate industrial AI strategy.
The generational churn occurring in the industrial workforce will inspire another trend: the widespread emergence of industrial data scientists as central figures in adopting and managing new technologies, like industrial AI – and just as importantly, the strategies for deploying and maximising these technologies to their full potential.
New research revealed that while 84% of key industrial decisionmakers accepted the need for an industrial AI strategy to drive a competitive advantage – and 98% acknowledged how failing to have one could present challenges to their business – only 35% had actually deployed such a strategy so far.
With one foot in traditional data science and the other in unique domain expertise, industrial data scientists will serve a critical role in being the ones to drive the creation and deployment of an industrial AI strategy.
AI investments shift from generic models to more precise industrial AI.
2022 will see AI’s maturation into industrial AI reach full bloom, graduating to real-world product deployments with concrete time-to-value. To achieve this, we’ll see more industrial organisations make a conscious shift from investments in generic AI models to more fit-for-purpose, precise industrial AI applications that help them achieve their profitability and sustainability goals. This means moving away from AI models that are trained on large volumes of plant data that can’t cover the full range of potential operations, to more specific industrial AI models that leverage domain expertise for interpreting and predicting with deep analytics and machine learning. Industrial data will be transformed into real business outcomes across the full asset lifecycle.
This shift will have the dual benefit of also facilitating new best-of-breed alliances built around industrial AI. Previously, partnerships were very tech-centric, driven by services or one large vendor. The more specialised focus of industrial AI will require a larger set of solutions providers, pooling together their independent and customised expertise. Not only does this help evolve partnerships away from more generic AI projects, it will also place a greater focus on time-to-value partnerships as opposed to do-it-yourself approaches, helping to lower the barrier to AI adoption more than ever.
Executive ownership and cultural change will accelerate industrial AI deployments
As industrial organisations scale up their deployments of enterprise-wide industrial AI strategies and applications, executive ownership and an investment in cultural change will be critical in reaping the benefits of digital transformation.
Digital executives like Chief Digital Officers will be crucial to overcoming these obstacles. CDOs will have a unique role to play in shepherding digital transformation and industrial AI through their organisation – bridging the gap between legacy systems and new technologies, fostering collaboration across silos, and shifting from mass data collection to strategic industrial data management.
All of these duties will be essential to ensure that an industrial organisation can execute a digital transformation plan that sees wider adoption of, and strategy around, fit-for-purpose industrial AI applications.
