3 Considerations for Leaders Investing in AI
Compared to other industries, manufacturing is at the beginning of its AI journey.
While artificial intelligence (AI) has been prevalent in industries such as the financial sector, where algorithms and decision trees have long been used in approving or denying loan requests and insurance claims, the manufacturing industry is at the beginning of its AI journey.
Manufacturers are heavily investing in AI. A recent international IFS study polling 600 executives working with ERP, Enterprise Asset Management, and Field Service Management found more than 90% are planning AI investments.
Combined with other technologies such as 5G and the Internet of Things (IoT), AI has the potential to allow manufacturers to create new production rhythms and methodologies. Real-time communication between enterprise systems and automated equipment can enable companies to automate more challenging business models, including engineer-to-order or even custom manufacturing.
Here are the three considerations companies must weigh when making AI investments.
1. How will AI impact your workforce?
Recent IFS research points to an encouraging future for a harmonized AI and human workforce in manufacturing. The aforementioned IFS AI study revealed that respondents saw AI as a route to create, rather than cull, jobs: Around 45% of respondents stated they expect AI to increase headcount, while 24% believe it won’t impact workforce figures.
However, the closure of manufacturing organizations and reduction in operations during COVID-19 highlight that AI technology in the front office isn’t perhaps as readily available as desired, and that progress needs to be made before it can truly provide a level of operational support similar to humans.
Meanwhile, AI can play a strategic role in the back office, mapping different operational scenarios and aiding recovery planning from a finance standpoint.
Scenario planning will become increasingly important. This is relevant as governments around the world start lifting lockdown restrictions and businesses plan back-to-work strategies.
Those simulations require a lot of data but will be driven by optimization, data analysis and AI.
And of course, it is still relevant to use AI/machine learning to forecast cash. Cash is king in business right now. So, there will be an emphasis on working out cashflows, bringing in predictive techniques and scenario planning. Businesses will start to prepare ways to know cashflow with more certainty should the next pandemic or crisis occur.
For example, earlier in the year, the conversation centered on the ‘just-in-time’ scenarios, but now the focus is firmly on ‘what-if’ planning at the macro supply chain level—what if I were to switch my manufacturing from China, what if we experience supply chain disruption from a natural disaster in a certain county etc. For example, there may be a price impact generated by one scenario, but what if I did a 70/30 split? What would that do to my margin and cashflow?
2. Are you accurately assessing the potential productivity and profitability gains of AI?
It is easy for organizations to say they are digitally transforming. They have bought into the buzzwords, read the research, consulted the analysts, and seen the figures about the potential cost savings and revenue growth.
But digital transformation is no small change. It is a complete shift in how you select, implement and leverage technology, and it occurs company-wide. This means manufacturing executives must be transparent when assessing and communicating the productivity and profitability gains of AI against the cost of transformative business changes to significantly increase margin.
When businesses first invested in IT, they had to invent new metrics that were tied to benefits like faster process completion or inventory turns and higher order completion rates. But manufacturing is a complex territory. A combination of entrenched processes, stretched supply chains, depreciating assets and growing global pressures makes planning for improved outcomes alongside day-to-day requirements a challenging prospect. Executives and their software vendors must go through a rigorous and careful process to identify earned value opportunities.
Implementing new business strategies will require capital spending and investments in process change, which will need to be sold to boards of directors, investors and other stakeholders. As such, executives must avoid the temptation of overpromising when discussing AI. They must distinguish between the incremental results they can expect from implementing AI in a narrow or defined process as opposed to a systemic approach across their organization.
3. How will I take ownership of AI outcomes – both good and bad?
There can be intended and unintended consequences of AI-based outcomes, and organizations and decision makers must understand they will be held responsible for both. Look no further than tragedies from self-driving car accidents and the subsequent struggles that followed as liability is assigned not on the basis of the algorithm or the inputs to AI, but ultimately the underlying motivations and decisions made by humans.
This is where ‘explainable AI’ will be critical—AI which creates an audit path so both before and after the fact, there is a clear representation of the outcomes the algorithm is designed to achieve and the nature of the data sources it is working form. Kaminski asserts explainable AI decisions must be rigorously documented to satisfy different stakeholders—from attorneys to data scientists through to middle managers. “A lawyer may be interested in different kinds of explanation compared to a computer scientist, like an explanation that provides insights into whether a decision is justified, whether it is legal, or allows a person to challenge that decision in some way,” says Kaminksi.
Manufacturers will soon move past the point of trying to duplicate human intelligence using machines, and towards a world where machines behave in ways that the human mind is just not capable. True digital transformation through AI will see its influence across all processes within an organization, automating systems and making repetitive tasks a distant memory.
While this will reduce production costs and increase the value organizations are able to return given limited inputs, this shift will also change the way people contribute to the industry, and the role of labor and civil liability law. There will be challenges to overcome, but those organizations who strike the right balance between embracing AI and being realistic about its potential benefits – alongside keeping workers happy and, in turn, contributing to society – will reap rewards. Will you be one of them?