Addressing the challenges of industrial AI implementation
A recent report by Reuters in cooperation with Siemens described the potential for industrial-grade artificial intelligence (AI) to accelerate the transition to sustainability. As discussed in a previous blog, AI’s wide range of applications and ability to process significant quantities of data at such rapid speeds offers ways to reduce the complexity that comes with transforming industrial processes in a holistic manner. The possibilities of AI are numerous, ranging from reducing the emissions of supply chains to increasing the energy efficiency of its own data centers.
Yet the report also describes the most pressing challenges its respondents face in the implementation of industrial AI. Organizations have expressed concerns about seeing the return on investment (ROI) of industrial AI, as well as finding reliable partners for such ventures and lacking the appropriate skills to successfully carry out implementation. Fortunately solutions to these concerns do exist. By adopting mindful scaling strategies, an ecosystem-centric approach to collaboration, and reskilling initiatives, organizations can be in a better position to unlock AI’s full potential.
Reducing costs with precise scaling
The largest concern respondents in the report state was the expenses of adopting industrial AI. According to the report, 38 percent of respondents cite a difficulty in projecting a specific ROI, and an additional 26 percent difficulty with the costs of implementation itself. Industrial AI has shown potential in improving many areas such as productivity, efficiency, and sustainability, but as with any new technology, some organizations can still be wary of whether it can be profitable in the long run.
One way to ensure the costs of industrial AI do not outweigh its benefits and increase the chances of a better ROI is implement it in strategic applications and scale from there. With so many capabilities, it can be easy to see AI as “a hammer looking for a nail” and apply it in as many places as possible. Instead, organizations can identify key use cases, starting with smaller data applications that advance the organization at large and lead to shorter term gains. This strategy helps build the foundation for the data infrastructure AI can leverage, producing immediate ROI that can further incentivize the growth of AI within an organization.
Building an ecosystem of partners
Another difficulty respondents say they are having is finding the right partnerships to build their AI efforts with. Around 33 percent of respondents state their trouble finding reliable solutions, vendors, and partners, while 22 percent say there was a lack of solution providers that are mature enough for their respective industries. Addressing this issue is critical, as partners are needed in the adoption of industrial AI to fill skill gaps and ensure AI’s interoperability with other technologies.
The solution here is to take an open, ecosystem-centric approach to building partnerships. Clear communication and expectations must be defined an aligned as early as possible so potential partners can know how best they can apply their expertise. Additionally, finding partners with specific domain knowledge related to the industry AI is being applied to would help the AI become more proficient in its assigned tasks. These partners can come from anywhere, whether they are large or small companies, or from government or academia. AI is hardly a one-company show, and can improve greatly with open collaboration from multiple organizations.
AI, reskilling, and upskilling
The last major challenge respondents declare in the report is a lack of AI-centered skills. A full 26 percent of respondents cite this difficulty, holding it responsible for slowing down their adoption of AI. However, this is not simply the lack of data science capabilities to build the algorithms AI runs on, but rather a lack in the combining of skill sets for high-impact use cases, including data science, data engineering, and domain expertise.
Investing in educational initiatives to generate a general understanding of AI and its use cases throughout the workforce. This helps align their skills to match AI’s development, bridging the gaps between skillsets and expediting AI’s adoption. That said, AI itself can also be valuable to build employees’ skills. It is already automating repetitive tasks that require expert knowledge, as well as making human-machine interaction much easier by enabling staff to speak to a machine’s copilot with natural language, removing the need to learn complex machine languages. Both organizational initiatives and AI itself can help equip the workforce for new ways to work, powered by AI.
Transforming industry with AI
Adopting AI into industrial practices will not be without its challenges, but the solutions are there for those who look. Careful, precise application strategies can provide the early gains necessary to scale up later, open partnerships can fill the gaps between domain and technical knowledge, and AI itself can be combined with reskilling initiatives to turn today’s workforce into AI experts. AI is going to transform industry, and with these strategies, organizations can ensure they utilize its maximum potential.
Be sure to check out the report by Reuters and Siemens for more information on adopting AI for sustainability and industry.
Siemens Digital Industries Software helps organizations of all sizes digitally transform using software, hardware and services from the Siemens Xcelerator business platform. Siemens’ software and the comprehensive digital twin enable companies to optimize their design, engineering and manufacturing processes to turn today’s ideas into the sustainable products of the future. From chips to entire systems, from product to process, across all industries. Siemens Digital Industries Software – Accelerating transformation.
