Karima-Catherine Goundiam is the founder and CEO of digital strategy firm Red Dot Digital and business matching platform B2BeeMatch.
I’m a CEO in the technology industry and love the opportunities of artificial intelligence. But I’ve noticed many similarities between AI adoption efforts and the kinds of digital transformation (DT) efforts I’ve experienced over the past decade.
Similar to DT, if AI adoption falls into the wrong hands, the consequences can be dire, especially since many of the people implementing AI do not consider the workers they are being asked to use. So even as someone who is excited about AI, I, like many others, am sounding the alarm. We are repeating a decade of failed digital transformation, but faster and at greater cost.
People and process failure
DT can be as simple as changing systems from paper to digital, or as complex as seamlessly incorporating retail customer profiles across online and in-person shopping systems and across the complete customer journey (personalized marketing, purchases, returns, loyalty points, etc.).
DT also relates to integrated online learning platforms, all kinds of services (insurance, healthcare, fitness training), “Internet of Things” home technologies (such as self-driving vacuum cleaners and doorbells with cameras), and more.
However, according to a study published in the Journal of Business Research that looked at the past 10 years of DT, approximately 80% of DT projects fail. Failures occur when companies fail to consider the full scope of what their digital systems need to handle, or when they fail to implement internal change management to teach employees how to work with the systems. Famous failures include efforts by GE, Ford, and others.
In my experience in DT, I have always said that you have to address people, process, and technology. DT projects often fail because of people and processes rather than technology. Why do you think AI adoption will be different?
DT is more than just setting up a website. To embark on a DT process, you need to audit what your company is doing, which will show you all the areas that need improvement. For example, many of the audits I’ve conducted have shown vulnerabilities such as a lack of a “Plan B” for supply chain failures, or customer processes that require manual or in-person steps and cannot be performed completely online (which became highly relevant early in the pandemic). An audit may also reveal opportunities to attract more customers, streamline processes, or save money.
However, companies are finding it difficult to completely transform. In every sense of the word, they are not ready for investment. They don’t commit enough resources to truly transform. They do not or cannot make financial commitments. And they don’t know how to change processes or how to manage change with their staff. DT is difficult enough for large, well-funded companies, but it’s especially difficult for small and medium-sized enterprises (SMEs).
AI is essentially the next wave of this wave, and it could probably be worse than it. AI is emerging with speed and speed; We aim for large-scale automation. Big companies are spending huge amounts of money on it, but there are already a lot of discrepancies regarding return on investment. You’re not making money, you’re consuming resources.
According to Time magazine, the AI bubble is about to burst. Billions of dollars are being invested in infrastructure to develop AI. People and businesses are spending money to use AI. ”
In some ways, I think small businesses will win because they will necessarily have to adopt more slowly. But if you don’t approach it strategically, for example by putting money in the wrong place on an AI project, you can still be at risk.
Mixed messaging undermines trust
There is also a growing gap between how companies communicate about AI to the market and how those same companies present AI to their employees. Management is telling shareholders that AI will reduce costs and headcount, while telling employees that AI will support them, not replace them.
Employees are listening to both messages and making calculations. This raises huge trust issues that we should all be concerned about. Who wants to become more efficient right after finishing work?
As a result, employees are predictably resistant to AI adoption. Whereas DT has often been done primarily by technology departments, sometimes with the help of marketing, AI is even more resisting because it forces employees to use AI tools across the enterprise.
What about humans?
Too many AI-related decisions today are driven by the pressure to quickly show ROI, from improving revenue to increasing stock prices to reducing headcount, rather than being driven by the organization’s actual capabilities. However, this approach comes with significant risks.
For example, beyond the well-known environmental costs that should not be underestimated, companies cannibalize by eliminating the people who would otherwise buy the services they sell.
The stakes are high when it comes to AI, as concerns extend beyond workflow disruption to job security and long-term career survival. The development of AI is exciting, and even if AI creates some new jobs, the mass and rapid adoption of AI will put many people out of work. And it’s difficult to sell anything to people who are unemployed.
Companies also need to consider human resource development. Companies are automating roles faster than they can build the workforce to sustain their businesses over the long term. You can’t build the workforce of the future if you eliminate the learning layer that creates the workforce of the future.
old business strategy
I’m not trying to stifle innovation or say companies shouldn’t experiment with AI. But we need to return to prudent business strategy. Don’t just follow the crowd. Don’t water down your workforce with the promises of technology that is in its infancy and has not yet reached maturity. Please take a more balanced view.
As part of that, I caution against listening to people who spend all their time putting out content about AI. Those people are not working. If so, you don’t have time to post articles constantly.
The people who are working on AI implementation within companies, who look at things from an ethical perspective and think about the human side, are not the people you see on podcasts, texts, and panels. They are hunkering down and trying to fix the problem before it gets any worse. These people are watching the team, trying to find ways to align people and needs with the company’s goals and repair that disconnect, but their voices aren’t at the center of this discussion.
As you develop your strategy, reach out to these people who are true experts, not AI converts. Find people who are actually solving problems and listen to their advice. Please go back to basics. What does it actually make sense for your business? AI has some great use cases, but it doesn’t make sense in every situation.
At the moment, AI is a runaway train. I don’t know where it will end up. Organizations that align their messaging, expectations, and employee strategies will be able to navigate this situation without crashing.
This column is part of Globe Careers’ Leadership Lab series, where executives and experts share their insights and advice about the world of work. To find all Leadership Lab stories, please visit: tgam.ca/Leadership Lab and guidelines on how to submit to columns. here.
