Pop quiz: Can you define the differences between artificial intelligence and automation?
I won’t judge you if the answer is “no.” There's a blurry line between AI and automation, with the terms often used interchangeably, even in tech-forward professions. But there's a very real difference between the two — and it’s one that’s becoming ever-more critical for organizations to understand.
Both automation and AI play an increasing role in the modern workplace, thanks to rapid advancements and the massive amount of data at organizations’ disposal. But while more than one-third (37%) of organizations use AI in some form, this figure doesn’t account for the sophistication of each implementation.
A truly robust use of AI and automation in everyday work — not just narrow applications — will require both education and an open-minded approach.
That’s a current pain point at many companies. More than half of workers still fear AI and automation will put their jobs at risk. True, some current jobs might not exist in five years. But job loss is a natural societal evolution — after all, there are far fewer chimney sweeps in the world now that homes are heated by electricity and gas. And the evolved roles that can result from these technologies will be engaging and fulfilling in a way that was impossible before AI and automation entered the workplace.
Defining AI Versus Automation
Sometimes a complicated idea is best explained with a simple analogy. Try thinking about AI and automation from this perspective:
- AI: Refers to “smart” technology. AI can deal with conceptual ideas and uncertainty, and should analyze and apply new information to react to situations. Machine learning is one subset of AI.
- Automation: Refers to “dumb” technology powered by programmable bots. Automation follows rules to handle straightforward tasks, and can’t react to new situations.
Despite — and sometimes due to — their differences, AI and automation are increasingly integrated. Users are injecting automation into AI with the intent of automating a greater number of tasks, such as simplifying decision making. The ability to automate tasks intelligently is a major application of AI, but it’s just one application.
Related Article: Why AI and Business Process Automation Share a Bright Future
How AI and Automation Are Shaping the Workplace of the Future
AI and automation are positioned to have a huge impact on both the direction and bottom line of companies around the globe. Netflix alone saves an estimated $1 billion in annual customer churn by applying AI to improve search results. But even organizations applying these technologies in a far less complex way have an opportunity to achieve big results.
It starts with a shift in the way we talk about AI and automation in the workplace. Presently, about 60% of occupations could have 30% or more of their constituent activities automated. But that doesn’t mean all of those occupations will soon disappear. AI and automation eliminate tasks, the menial chores that prohibit real productivity, evolving the related jobs in the process. What’s left is an opportunity for growth and change.
Stone mills made it easier to turn wheat into flour, and advanced factories made way for the era of the office worker. Today, we’re nearing the era of the knowledge worker. Some of these roles will be closely intertwined with maintaining, programming and enabling AI and automation — after all, this technology isn’t autonomous. Meanwhile, other jobs will see a shift to more creative, empathetic tasks. The total hours worked involving social and emotional skills is set to increase 24% by 2030.
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Preparing for the Future
McKinsey reports nearly 20% of companies predict automation will create job growth, versus 6% that anticipate needing a smaller workforce. But don’t just start adding new roles and making a huge investment in new tech. Be sure to complete these tasks while moving forward:
- Break down data silos and move to a centralized storage model. AI can’t solve problems without an accurate picture of an organization’s functions — one that’s impossible to gain when operations and finance keep their data separate.
- Set the right tech foundation. Cloud-based automation services are increasing adoption by reducing the investment and expertise required to get off the ground. Organizations can launch multiple AI and automation projects at once, thanks to the cloud’s ability to manage large datasets.
- Be strategic about your applications. AI and automation aren’t right for every computer-based task. Engage in careful analysis to make the best use of machines, while maintaining your organization’s human touch.
- Invest in reskilling and upskilling — as well as new talent. While current workforces will need education and new core skills to effectively interact with AI and automation, organizations may also find some gaps, including data scientists and C-suite roles like a chief automation officer.
While many people still think of automation and AI as "techy" tools that require deep technical expertise, this misconception disappears when they gain a grasp of their practical application and value. It’s no longer practical to avoid AI and automation. Instead, it’s time to clarify the differences and benefits of these technologies, and look for ways to naturally integrate them into your operation.
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