September 11, 2018
All global industries, from retail to finance and pharma to healthcare, are feeling the reverberating effects AI is having on productivity.
Technological advances and the rise of machine learning allow us to automate previously human tasks, like harvesting and analyzing vast amounts of data.
Moreover, intelligent machines can identify patterns which would be invisible to us and translate them into optimal insights for businesses to act upon.
The potential benefits of AI for productivity are considerable.
Accenture predicts a global increase in productivity of 40% or more by 2035—a prediction that’s not at all far fetched, at least in the US, if the country does indeed follow through with its estimated $35 trillion investment in AI tools.
But while predictions of a productivity surge chime well with most, others are skeptical about the possibility of such a dramatic rate of increase, giving rise to what one economist has dubbed the “productivity paradox.”
Indeed, while sectors like wholesale, retail, and recreational services have already benefited (and stand to benefit considerably more) from AI, other areas, like labor productivity, have yet to yield significant statistical growth.
Part of the problem, according to a recent report by productivity expert Prithwiraj Choudhury, may be the training people using AI tools receive.
What led Choudhury’s team to this conclusion was an experiment with the US Patent and Trademark Office, an organization of 100,000 employees which lies at the heart of the country’s innovation system.
The team took 221 MBA students from Harvard Business School and gave each a patent application to review along with five rather obscure claims for the existence of prior art (which would mean the invention was already known).
To search through previous patents and either validate or invalidate each claim, half were given the company’s new AI (Sigma-AI) and half the organization’s traditional Boolean technology, which operates more like Google’s search engine. The results, published by Harvard Business School, were surprising.
Choudhury’s team found that people with a computer science and engineering (CS&E) background performed much better with machine-learning technology, while people without a CS&E background did much better using the old Boolean technology.
More importantly, even with the data-crunching power of the AI, only those given human expert advice arrived at the right answer
The suggestion which emerged from the report was that employers wishing to capitalize on their investment in AI should think long and hard about the skills of the people they employ.
This is sage advice, especially if the AI tools are complex and those using them are left to their own devices. It’s less applicable, however, when the tools are designed to be user friendly.
Evo’s solution to the productivity paradox is simplicity.
Evo values efficiency and embraces modernity. Where some businesses still let people do the job of software, we let software and algorithms take the lead. This means smarter automation and increased productivity.
Our tools take your company data, which means product range and availability, pricing and promotions, transactions, and returns, and feed them into our machine learning software, along with market signals like competitor ranges and prices, social media trends, and the weather.
We then establish your data within a framework, shaped by your strategic goals and business rules.
What you get out of this is simple, intuitive, and tailored: an Excel add-in, real-time access to the multi-format web portal (with sandbox testing), and—the all-important human element—your own dedicated Evo business scientist to offer strategic advice.
The human element is key to our philosophy and, with our $200 million+ margin, the secret of our success. Rather than obfuscating the results, we believe in having an expert on hand who can explain them in a way managers can understand.
This is exactly the deal-clinching “expert advice” that surfaced in Choudhury’s report. And, using the insights gleaned from our vast reserves of data combined with human intuition, it lets you set dynamic pricing, based on the future outcome, rather than making minute-to-minute decisions based mainly on intuition.
The science of our software is complex; the package it comes in is simple
Whatever the solution you’re looking for, whether a pricing model for business or a new smartphone for communication, what you want to know is not how the software works, it’s that the software works.
That’s why, despite the complexity of our data science, we’ve simplified our interface, making our solutions simple to understand and easy to implement.
AI’s still evolving, so the productivity paradox isn’t going to go away any time soon. But as Evo’s success spreads across a number of industries, we’re proving that the simplest solutions produce the best results.
If you want to learn more about how Evo’s pricing, promotions, forecasts, and replenishment solutions can benefit your business and protect your productivity, visit our website or contact us to schedule a consultation.
About the author
Alexander Meddings is Evo’s content expert on artificial intelligence, machine learning, and related topics.
He is an experienced journalist who covers branding, social media, marketing, and technology, with degrees from the University of Exeter and University of Oxford.