It is the ultimate time to be working in AI.
If you’re an engineer or researching how to work in the field of AI, there are more opportunities available to you than ever before. Investment and innovation abound in every aspect of artificial intelligence, machine learning, and data management.
If you’re a researcher, you have the freedom to explore any aspect of artificial intelligence because someone out there will likely fund it or find a use for the product you develop. If you’re an AI professional—congratulations. You’ve landed a job in one of the highest-paying industries in the world.
If you’re running a business, there’s no time like the present to get involved. The market size value of the global AI industry was USD 136 billion in 2022 and is set to grow to over $1 trillion by the end of the decade. It’s becoming so ubiquitous that most of us interact with AI daily, like chatbots or Alexa, and don’t even realize it. But AI use in business extends far beyond intelligent search engines and digital assistants. It can change the way we work entirely.
There are multiple levels to AI
AI can now enter our workplaces via a few different routes. What path you choose depends on your operational needs and level of investment.
For example, if you are managing a small-to-mid-sized logistics business with localized operations, you may not need a fully-loaded analytical and predictive engine to generate forecast models. However, a more straightforward intelligent automation system may be the perfect fit to help make your processes more efficient. Such systems will handle mundane administrative tasks, like data entry, data backup, or first-contact customer care, leaving your employees free to handle more important tasks.
Chinese retail company Alibaba is a good example of intelligent retail automation at work. Using Natural Language Processing, Alibaba’s AI can automatically generate product descriptions for their customers and translate them into various languages. The best part is this type of automation is highly scalable. This means whether you’re a small-sized local logistics company or a global conglomerate, the software is essentially the same.
The function of your AI system should be determined by your needs first, and then the size and budget of your operations.
(Thinking of taking the next step and building your own AI? Check out our article Building Responsible AI for 2023 and Beyond, for further insights)
Automation
The most widespread use of AI at work is also the simplest—automation. By handing over repetitive and time-consuming tasks to AI, we can devote our attention to work that requires human insight and input. As I illustrated in the example above, automation is a highly scalable use for AI that has proved to be effective across multiple industries. Retail and logistics are probably the most prominent. Amazon, for example, uses AI across multiple parallels including customer care, e-commerce, warehousing, and store-front operations.
But the use of automation in business extends into every industry today. In recruitment, for example, automation has started to play a very important role. When PTP started developing our proprietary recruiting AI, Gabi, I wanted to find a way to solve three challenges every recruiter labors under—applicant overload, lack of communication, and inefficient talent pooling. With automation, natural language processing, and data-management tools, we were able to meet all our objectives and go beyond my expectations.
Gabi uses NLP and intelligent automation to review thousands of resumes at the click of a button and score the applicants across multiple metrics. It can also automate candidate communication to schedule interviews with successful candidates. Finally, Gabi gathers all these applicants in one place so you can revisit your talent pool and reach out to old candidates when new opportunities crop up. Gabi has completely changed the way we work and made us faster and more efficient.
Equally, AI has changed the way the recruitment industry as a whole, works.
Diagnostics and Analytics
This is, for some operations, the holy grail of AI in business. Thanks to the power of data and the genius of data science, we are well on our way toward building AI which can not only differentiate between useful and non-useful data but can also parse that data to provide diagnostics and make accurate predictions.
Diagnostic AI systems can analyze existing data and ongoing trends to see identify areas of deficiency in your current operations. In other words, the AI looks at where you are, and where you want to be, and tells you what you need to improve.
For example, an AI-based system called Alex recently analyzed the latest employee climate study and identified, using machine learning techniques, the multiple jobs feature combinations and company environment conditions that make current employees happy in their job. Using such analysis, businesses can identify what strategy to adopt to attract the best talent under current conditions and how to build a work environment best suited towards talent retention.
Anticipatory or predictive AI analytics uses large amounts of data and machine learning to make accurate guesses about how your operations will be affected at a given point in the future.
Think of the value this could have on resource allocation, budgeting, and operations management. The most powerful tool a business can have is to be able to accurately predict the trends that will affect operations in the future with enough time to pivot accordingly. If, for example, you’re running a retail business, and you know 5 years in advance what your customers are likely to need, you can use the intervening time to make sure you have ample stock of that commodity.
Strategy
“Because strategic decisions have significant consequences, you need to understand why AI is making a certain prediction and what extrapolations it’s making from which information.”- Yuval Atsmon
AI use in strategic thinking is still in its infancy, more conceptual than practical. But we’re not far from the time when AI can not only predict future trends but also formulate strategies to profit from them.
If you’re a high-level executive, strategic decision-making is your biggest responsibility. But the use of AI technology in strategic thinking is still underutilized.
That being said we’re still not quite at the point where AI can formulate a strategy for us. My advice to executives looking at employing an AI-driven strategy is—don’t wait.
Don’t wait for AI technology to get over that final hurdle. Start using automation, predictive, and diagnostic AI as stepping stones toward creating more efficient strategies, today.
(As you ramp up your AI onboarding strategy, one important step will be to get your workforce prepared for the upgrade. Check out AI/ML and Robotics Are Here to Stay. Is Your Workforce Ready? to learn how to begin your AI-focused L & D)
Conclusion
We are entering the age of the AI-native workplace. That means our offices and workspaces will be enriched through AI-powered systems that will work in the background to make our process more efficient, our workloads more manageable, and our organization as a whole, more productive.
If you’re running a business, now is the time to start investing in the AI ecosystem. AI is going to play a more important role in offices and workplaces around the world.
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Nick Shah is the Founder and President of Peterson Technology Partners (PTP), Chicago’s premiere IT staff augmentation agency. With his relationship-focused mentality and technical expertise, Nick has earned the trust of Chicago-based Fortune 100 companies for their technical staffing needs.
