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Extract from IISE (Joseph Byrum)
Making the most of the fourth industrial revolution
So how can managers make the most of nonevil smart technology that can surpass human capabilities in roles traditionally reserved for humans?
In many industrial systems, the human element can be the most difficult factor to plan for. We have memory lapses and rely on guesswork. We grow tired and make mistakes. AI excels at eliminating human factors to make decisions based on statistical probability. Just science and the best available data. Smart technology gathers available data from a business process and optimizes the data to provide the best outcome through superior decision-making.
Thus, harnessing smart technology in an enterprise will one day be as simple as going to a website and downloading the latest “AI” software (after paying a hefty licensing fee, of course). Unfortunately, such sophisticated learning capabilities are far from being off-the-shelf products for most industries. That means implementing AI in an organization at this early stage tends to be more involved, requiring AI solutions that have been custom built to address a specific problem using machine learning algorithms.
To create specific AI tools, managers must assemble a team that can identify the elements needed to address the business problem at hand. This will include gathering data and formulating the algorithms needed to make appropriate judgments based on statistical analysis. With these, the AI is able to conduct what are essentially an endless series of trial-and-error experiments to zero in on what works. Thanks to modern processing power, this happens in a fraction of the time it would take a human to complete the same task with less certain results.
Building an AI project requires subject-matter experts, software engineers and mathematicians who can work well together to create these needed elements. Such talent is often not available in-house. Existing staff has subject-matter expertise, but not every organization will have the specialists needed to create the algorithms that make the most of machine learning. It’s certainly possible to hire additional, full-time staff to fill these roles, but there could be cheaper alternatives. Thanks to open innovation, managers can build AI tools using crowdsourcing to tap into the desired skillsets on a per-project basis.
Patience is critical to the success in developing smart tools, because this process does not deliver results overnight. Rather, it takes substantial time and management resources to efficiently divide up the tasks before sending them out for crowdsourced solutions. Open innovation is a hands-on process that is guaranteed to disappoint anyone seeking a quick and easy fix. Quick and easy will have to wait until general AI is closer to reality.
Is it worth devoting the time to AI now, rather than waiting? To achieve smart technology’s benefit – resource optimization through improved decision-making – the answer is yes.
Those that fail to take advantage of smart technology are at risk of being overtaken by more forward-looking competitors.
Joseph Byrum is an accomplished executive leader, innovator, and cross-domain strategist with a proven track record of success across multiple industries. With a diverse background spanning biotech, finance, and data science, he has earned over 50 patents that have collectively generated more than $1 billion in revenue. Dr. Byrum’s groundbreaking contributions have been recognized with prestigious honors, including the INFORMS Franz Edelman Prize and the ANA Genius Award. His vision of the “intelligent enterprise” blends his scientific expertise with business acumen to help Fortune 500 companies transform their operations through his signature approach: “Unlearn, Transform, Reinvent.” Dr. Byrum earned a PhD in genetics from Iowa State University and an MBA from the Stephen M. Ross School of Business, University of Michigan.
