The fact that we live in a digital age today has been, for the most part, varying shades of cool and convenient. We can be thankful for the many innovations in technology from better video games and online learning experiences to multi-purpose smartphones, smart homes, and virtual assistants.
Yet, there’s one frontier in tech that has been around for a long time but has never been fully adopted or even accepted on a large scale in business. Robotics, automated workflows, machine learning all fall under the umbrella of AI. And while some industries have embraced them wholeheartedly, many remain rooted in human-based processes that tech advocates view as inefficient and outdated.
Does this resistance to AI need to undergo a greater shift in favor of its adoption? And if so, what are business leaders waiting for?
A disappointing pandemic
Technologists may have been unduly excited by the pandemic. It’s not because they have any reason to want people to get sick or economies to suffer. Rather, the upheaval it caused has given various forms of technology a chance to rise to the occasion and solve emerging problems.
We witnessed this as early as the spring of 2020 when the first lockdown measures were enforced across many countries. Employers who had been reluctant to allow their people to work from home on a full-time basis made such arrangements mandatory for the entire workforce. Simultaneously, cybersecurity professionals, platform developers, and others involved in making the necessary tools stepped up to improve in response.
Our newfound aversion to contact also fast-tracked the use of self-driving vehicles and robot cleaners, for instance. There has been an uptick in the use of AI, but it hasn’t been as significant as might be hoped for. 70% of companies already implementing some form of AI said that the pandemic did not affect, while only 20% reported it would accelerate their plans.
The root of the problem
Most business leaders surveyed continue to cite budgetary issues and a lack of buy-in from management as the reasons for not making greater use of AI’s potential. In the same survey, technologists tended to disagree, citing skills and inadequate deployment data as the major limiting factors.
It seems like a paradox because technology is more sophisticated and accessible than ever. The barriers to skill and cost are also dropping with each year. You could begin developing a prototype robot using second-hand electronic manufacturing equipment for sale online.
However, the real challenge lies beneath the surface of these commonly cited barriers to AI’s adoption. Every business has its unique needs and priorities, and ‘implementing AI’ is really a catch-all term referencing myriad, complex process changes.
Business leaders, and humans in general, find it easier to think by way of analogy. This is why most companies end up imitating the best practices of industry leaders and competitors. If something has been proven to succeed, it’s not a stretch to apply the same to your operations.
Yet, the application of AI is highly dependent on context. There really isn’t a standard template you can use to copy-and-paste from what other companies might be doing.
Before you can actually harness AI’s potential, you need to build a business case for it. And that’s a far more lengthy, nuanced, and rigorous process than most leaders anticipate.
Changing leadership understanding
If there’s a useful analogy for any C-suite decision-maker to grasp this issue, it can be found in a dilemma faced by many beginning programmers.
Recent years have seen a disproportionate focus on coding as an essential skill for the next generation. But this ignores the fact that coding has no value in and of itself. You must write code to solve a problem.
Likewise, you don’t adopt AI for its own sake. Or because it’s trendy, your competitors have used it successfully, you fear getting left behind, and so on.
Get down to work on the specifics of your use case. Find a problem to tackle. Thankfully, you can use familiar techniques like the 5 Whys, fishbone analysis, and the Pareto principle to identify high-impact areas to address. And strip the catch-all AI down to the core element you really need to solve the problem.
In this way, changing the attitudes and understanding of leaders to AI implementation can be adopted further and more effectively. At the same time, the simple, specific use case also overcomes barriers to cost and skill.