A DeWitt Insight

Leveraging AI and Automation for Productivity and Growth

Leveraging AI and Automation for Productivity and Growth

A recent report by Market Research Future (MRFR) expects factory automation growth to maintain a CACR of at least 8% between 2016-2020, and with a valuation that is expected to exceed $240 billion. A 2019 report from the World Economic Forum shows similar high growth for transportation and storage, manufacturing, and wholesale and retail trade.

Going from a vision to a full implementation that is producing value is fraught with pitfalls.

There’s no doubt that Artificial Intelligence (AI) and automation are benefiting many industries. But going from a vision to a full implementation that is producing value is fraught with pitfalls. In this article, we’ll look at what it takes to leverage AI and automation for productivity and growth.

Be Bold and Risk Tolerant

Being bold means taking advantage of opportunities that can have a great payoff. However, that should be balanced with risk tolerance. While those may sound like competing goals, they are two sides of the same coin. It’s a matter of calculated risk.

Knowing which AI and automation initiatives to pursue is different from chasing the latest technologies just to stay on the cutting edge.

Knowing which AI and automation initiatives to pursue is different from chasing the latest technologies just to stay on the cutting edge. Technology that will help a company become more productive and grow is a worthy pursuit, even if it doesn’t have a short-term payoff. In such a scenario, short-term gains are absent, but understanding where break-even is along with the trajectory of potential profits helps in creating a practical timeline for implementation and alignment of projects.

Recruit and Train the Right People

Hiring people well-trained and versed in AI and automation has become extremely difficult and competitive. At the end of 2017, Tencent, A Chinese technology company, put out a report that found AI talent (i.e., “AI researchers and practitioners”) worldwide was only 300,000, but demand was in the millions. Since 2017, demand for AI talent has grown, leaving companies competing for a tiny pool of eligible candidates. However, those companies that wish to get the full benefit of AI and automation have no choice but to compete.

“We hired some data scientists, and it was difficult,” Jehiel Oliver, CEO of Washington, D.C.-based Hello Tractor, said to CIO Magazine. “It’s a highly competitive market for talent. Not only did we have to find folks who were comfortable working with these advanced technologies, but also have to be comfortable with the business models and the locations we’re operating in. We’re not promising cushy offices in Silicon Valley. We work in agriculture in emerging markets.”

Companies should search their existing talent for those who have experience or knowledge in technologies that fit into the company’s strategy set.

While recruiting for talent that is well-versed in the latest technologies is far from easy, companies may find that they already have a few gems under their roof. Companies should search their existing talent for those who have experience or knowledge in technologies that fit into the company’s strategy set. These employees may need a little training to put them on par with talent that the company is fishing for. Additionally, such employees will already be up to speed on company products and processes.

In the interim, there are always consultants who can help with technology implementations, development of products using those technologies, and even finding talent.

Design Automation to Scale

Starting with a small team of AI developers will only get you so far. As the company grows, it will become clear that more is needed to fully take advantage of what AI has to offer. At some point, data scientists will be required to ensure the company is extracting as much value as possible from its data.

Data scientists will be required to ensure the company is extracting as much value as possible from its data.

Additionally, projects utilizing the latest technologies shouldn’t remain in silos. These initiatives should be company-wide. A 2019 report from KPMG titled ‘Easing the pressure points: The state of intelligent automation’, found that out of seven functional areas, only one has a double-digit (12% in IT/Digital) focus on AI initiatives as a company-wide approach rather than process. The lack of a company-wide initiative resulted in lower confidence in visionary leadership.

For those companies that have implemented AI and automation, a Forrester report found that most were not leveraging these technologies to their fullest extinct. “Today, enterprises use machine learning models to identify customer churn, suggest upsell/cross-sell, reroute logistics bottlenecks, predict manufacturing machine failure, and make other predictions,” Forrester analyst Mike Gualtieri and Kjell Carlsson wrote in a report titled Forrester Wave: Multimodal Predictive Analytics and Machine Learning Solutions, Q3 2018.

“A few models here and there are valuable and significant, but they are a mere drop in the bucket compared to what is possible,” both analysts wrote. “Enterprises have dozens, hundreds, and even thousands of applications and business processes that could, but do not currently benefit from predictive models.”

As companies move into implementation, the choice of programming languages, libraries, and frameworks become critical. Choosing obscure, open-source, non-popular languages can mean little to no support. Without support, the company’s technology initiatives will struggle to get off the ground. Languages such as Python and R are open source but also have large communities of support.

Systematically Capture Value

Once the technology has been implemented, it should be able to regularly extract value from company data with little to no intervention. This is a systematic approach. If employees have to continually step in because certain parts of the process are missing, the workflow should be re-evaluated so it can be better streamlined.

Once the technology has been implemented, it should be able to regularly extract value from company data with little to no intervention. This is a systematic approach.

More complex scenarios can be difficult to hand-off to automation. But those that are repetitive and relatively straightforward are prime for automation and AI.

“We still remain far from general AI that can wholly take over complex tasks, but we have now entered the realm of AI-augmented work and decision science — what we call ‘augmented intelligence. If you are a CIO and your organization doesn’t use AI, chances are high that your competitors do, and this should be a concern.” Chris Howard, Chief of Research at Gartner, said to Enterprise AI.

If you are a CIO and your organization doesn’t use AI, chances are high that your competitors do, and this should be a concern.

Please note: Sources are provided for informational and reference purposes only. DeWitt has no vendor affiliations, offers no products, and has no conflicts of interest.

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