By mid-2022, 80% of business executives already believed that AI could be used to automate any business decision. Later that year, with the explosive entry of ChatGPT onto the market, some companies got an opportunity to act on this instinct. While generative AI is a relatively new technology with many capabilities yet to be defined and explored, AI technology has long been used in making business decisions. Its power comes from its thoughtful application to the automation of a business’ processes, creating meaningful customer and employee engagement, and better and deeper analysis of data. As AI continues to evolve and become accessible to more businesses, it will serve both its primary role — decision-making — and take on a new one altogether — being a reliable co-pilot for those who apply it thoughtfully.
Five ways A.I. can help business leaders make better financial decisions
Currently, business leaders rely on AI to help make informed decisions on better pricing, marketing, and operational functions. As generative AI continues to rapidly evolve, this use will only grow further into, and transform areas like analyzing and answering questions regarding company data, customer service, and more. Any office activity, be it price-setting, accessing data, customer service, summarizing meetings, and more will become much more efficient, aiding in reducing costs and maximizing profits. But the first question for business leaders is which implementations to use. Here are five ways business leaders can implement A.I. that will positively impact the bottom line.
- Transforming pricing models from an art into a science
Price-setting for products in industries like retail, rental real estate, vacation getaways, and more has always been an art form and a gamble, with plenty to gain. Back in 2010, McKinsey predicted that a 1% increase in sales price could boost a company’s profits by 8.7%, but with a catch: it couldn’t result in lost sales. Predictive pricing leverages AI to combine and analyze data about demand, competition, and consumer behavior in different markets, and like an excellent co-pilot, respond immediately to changes or pressure.
- Reduce the costs of transportation and logistics through increased efficiency
Logistics management, particularly in retail, is at the mercy of unpredictable traffic patterns, weather, and even unexpected construction. Earlier this year, McKinsey reported that AI has already helped businesses improve transportation and logistics costs by roughly 15% by making fleet operations visible no matter where they are. AI-powered improvements and decisions in supply-chain management will amount to between $1.2 and $2 trillion in revenue generation over the next 20 years.
- Optimize product recommendations to maximize revenue
Customizing product recommendations to the preferences and propensities of customers is nothing new, but AI can analyze endless amounts of data about individuals to quickly identify their personal shopping preferences and trends, and even make predictions about what they need and want. For years, Amazon has made masterful use of AI to recommend products to customers based on their buy and search history, and the strategy is working. In 2021, McKinsey reported that 35% of Amazon’s $469.822 billion in revenue— or a cool $1.64 billion— was generated from product recommendations that were auto-generated by AI 24/7. That’s a co-pilot every business leader can leverage.
- Reduce labor costs by automating customer service and similar requests
There are more than 265 billion customer service calls each year, costing businesses more than $1 trillion. Business leaders who properly implement AI will save their businesses and consumers up to 2.5 billion customer service hours every year. What’s more, businesses may see a reduction in related customer service costs up to $11 billion annually. That’s probably why a recent study of American businessowners found that 73% of businesses either already use or plan to use AI for customer service roles. In this case, AI can be a co-pilot, answering customer service queries 24/7 and automating other labor-intensive tasks.
- Make data more accessible to executives
Businesses rely on structured data— data that is classified, quantified, or qualified in some way— to operate. But structured data accounts for only a small portion of the total data a business generates. Gartner estimates that between 80 and 90% of the world’s data is unstructured, meaning that it’s often locked away where it can’t be accessed, analyzed or otherwise made useful. And with total global data set to grow at a CAGR of 61% just two years from now, that’s a lot of untapped potential. As a co-pilot, properly implemented AI can help executives access that data without needing an analyst or other intermediary from finance or engineering teams to build complicated reports or to break it down.
The importance of thoughtfully integrating AI into business strategy
A recent survey of 1,000 business leaders found that more than 49% had already integrated ChatGPT into their business strategies, and another 30% had near-term plans to do so. That’s not surprising given those already using the generative AI software reported a short-term savings of $75,000 on average. But there’s more to AI implementation than the novelty and quick cost savings. Any business decision should be made keeping both financial impact— net present value, customer acquisition cost (CAC), incremental revenue generation, and cash flow, etc.— and user experience— churn rate, returning customers, increased engagement— in mind.
Some business decisions may have a greater financial impact in the short term — see the immediate cost-savings of ChatGPT— but spoil the user experience leading to long term harms— yet to be seen for early and less thoughtful adopters of the platform. Business leaders must use implementation strategies that both generate high financial returns and improve user experience.
How business leaders can thoughtfully integrate AI into their current strategies for maximum impact
To make a difference in business and financial decision-making, AI must be integrated through four critical steps.
- Use case identification
Collaboration across departments helps to identify areas where AI can be implemented with the greatest potential. Gartner estimates that by 2025, Chief Data Officers employing cross-functional collaboration ahead of AI implementation will outperform their peers, leading to higher value creation.
- Road mapping
That same Gartner study indicates that 40% of companies trying to tackle big data projects like AI have difficulty assigning explicit roles and responsibilities for managing change from beginning to end. That’s why business leaders must appoint engineering and business DRIs (directly responsible individuals) to create a roadmap and take responsibility for implementing AI solutions is crucial to success.
- Find the right solution for your business
Compare the benefits and drawbacks— speed, efficiency, cost— of building internal tools and systems with off-the-shelf solutions. If AI is needed to handle customer service requests, then an off-the-shelf product will be the best co-pilot possible. But if the goal is to define the company or change AI as we know it, it’s best to start from scratch.
- Launch and iterate rapidly
AI, and in particular generative AI, begins and ends with the customer. So, once you’ve decided on a product that suits your business’s needs, a swift launch with customer-oriented iterations is the key to success.
The big picture
It’s no longer a matter of if, but when business leaders will implement AI into their companies’ daily workflow. Rather than jumping on the train because it’s now-or-never, business leaders can use decisions about AI to drive business strategy now and for years to come, resulting in increased revenue, customer and employee satisfaction, and strength and longevity for their companies. And thoughtful implementation is what will launch some businesses above the rest.
Written by Abhinav Swarup.
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