When it comes to business activities, figuring out which AI software serves exactly which purpose can be a bit murky. From companies advertising AI in recruiting to AI in healthcare, it’s a Wild West of ideas and applications. But what’s the biggest takeaway for a CEO or business owner?
What’s the single, big idea on why organization leaders need to act now to integrate AI into their business process?
AI presents an economically transformative process because it does one thing incredibly well: it decreases the cost of prediction.
Ajay Agrawal, a professor at the University of Toronto's Rotman School of Management and Geoffrey Taber Chair in Entrepreneurship and Innovation, co-authored a book explaining the economic changes artificial intelligence provides, Prediction Machines: The Simple Economics of Artificial Intelligence.
In this book, Agrawal shows how business leaders can leverage AI’s capabilities to add value to their own business goals.
Agrawal’s response: it drastically reduces cost.
CEOs and other C-Suite executives should take note how drastic these cost-savings are. It’s similar to other kinds of technology, cut costs and invented industries. Electricity, for example, didn’t occur at large scale until the late 19th century. Large-scale electric power distribution occurred when water flowing over Niagara Falls was harnessed to locally manufacture aluminum and carborundum. Soon it was transmitted to a nearby city, used for lighting and public transportation. Then, it gained commercial traction amongst international researchers who began using it for experiments and scientific discoveries. Businessmen used electricity to accelerate their own production and selling of goods.
Similarly AI presents the opportunity for leaders to integrate the technology into their own business processes, accelerating growth and saving costs.
But the question may be: what does it exactly reduce the cost of?
Where electricity not only reduced the cost of production, but accelerated it, artificial intelligence cuts the cost of the first step in any business or organizational process: prediction
Prediction is an expensive process. Think of inventory management. The problems that pervade within the space include excessive inventory gathering dust in warehouses, tying up millions of dollars in working capital. The entire chain---planning, procurement, production, distribution and fulfillment---must somehow hit the balance of right product, right place, and right time. It’s a challenge.
AI gives businesses the tools the ability to predict scenarios, recommend actions, and take action, either with human approval or autonomously.
Amazon is an early-adopter of AI and automation. It’s used to improve its customer experience and search capabilities. It’s become the underpinning technology of the billion dollar company. From predicting the number of customers willing to buy a certain product to managing inventory for its grocery stores, Amazon’s AI provides customized predictions to its internal team. Arguably, its AI-powered recommendation engine, drives 35% of its total sales.
With the machine doing the time-consuming and data-heavy challenge of prediction, it creates a distinct role for itself. Human prediction becomes less dependable---but the value of sound human judgement drastically escalates.
This is the important distinction: an AI does not judge; it only makes predictions and recommendations. It’s up to the human to make a call of judgement to determine what to do with those predictions.
Where can I integrate AI into my own organization to reap benefits and stay ahead of the game?
It may be tempting to do a Google search and learn what other companies are doing. This is a helpful step. However it may lead leaders down a rabbit hole of confusion. Before searching for solutions, company decision makers need to review the challenges they’re experiencing in their organizational processes.
AI is incredibly effective at receiving and parsing through enormous amounts of data. Find workflows that receive plenty of inputs---like inventory, logistics tracking, hiring and recruitment practices, data collection, etc. Then think about what tasks need to be executed with these inputs. Is it reviewing job applications and/or job descriptions? Tracking customer reviews on products? After reviewing tasks, focus on the task that needs an element of prediction. Maybe you need to learn more about how customers enjoyed which products to help predict how certain products move along the supply chain.
One of the most costly--and ubiquitous---challenges every organization faces is finding high-quality talent and continuing to develop talent in-house. There’s plenty of inputs: job description, application submissions, skill evaluation, certifications, and personal values aligning with a team or company culture. But these are only a handful of tasks during the hiring and recruitment process. Once a candidate becomes an employee, the real costs of poor fit or low employee engagement kick in.
Low employee engagement costs businesses $4,129 on average to hire new talent, and around $986 to onboard the new hire. In general, $5,000 is lost each time an employee leaves. Additionally, it takes an average of 52 days to fill a position. For specialized or in-demand talent, the wait time can be longer. Using AI in recruiting can be a solid proposal in increasing employee fit.
Additionally, AI in recruiting and talent management can be used for identifying gaps in training. Employees want to develop their careers and skill set. This is one way human resources teams keep engagement high. 3/4 of employees that work for companies with solid financial performance feel moderately or highly engaged in the workplace. By learning new skills and receiving tailored training, organizations can help their talent feel their growth and capabilities. To experience true cost-savings and productivity, implementing AI at the nexus of recruiting and talent development is a solid first step in creating a competitive company.
By implementing AI in recruiting, or at another workflow juncture, a company turns up their prediction accuracy.
One question for business leaders: if AI is a valuable application in my sector, how long will take to accelerate changes in my industry? If you see those changes happening decades down the road versus 24 months, your decisions today --- and actions---will be very different.
Action today could be the difference between surviving and thriving.
AI for business does not have to be a complex undertaking. It’s about identifying flow points within your business that regularly receive an onslaught of information and the result needs to be accurate---or else. With the power of prediction, you’ll be able to forecast internal trends and goals faster; organizations will be able to understand their workforce on a deeper level; and you’ll be achieving the balance of machine and human collaboration before competitors.