Dear Springboard:

Last month I wrote to ask who is getting the most out of generative AI and what’s the overall impact.  

You said studies had shown that 95% of companies reported that they had not seen measurable ROI.  You focused on the cost and impact of lazy employees producing “workslop.”  

I understand workslop is the output when an employee asks AI to do their work for them. You wrote, “Workslop is incomplete, unhelpful or otherwise inadequate so that the recipient has to “ ‘. . . interpret, correct, or redo the work.’ “

So, if AI isn’t delivering, is it a bust?

Sign me,

Now a Skeptic

 

Dear NS:

No, AI is not a bust.

The main point about workslop is that employees cannot expect to outsource all effort and critical thinking to AI and still be effective and trustworthy coworkers.

This statistic of 95% not having measurable ROI caught the attention of a trio of leaders at ghSMART, a high-end management consultancy.

They decided to focus on the 5% that are getting results to see what they could learn.

Ren van den Broek, a partner in the firm, and two colleagues polled senior leaders at Fortune 50, private equity portfolio companies and nonprofits.  They shared their results in an article in Harvard Business Review.

The authors found there was a theme among the companies that are getting a good ROI on their AI activity.

While many companies hire a superstar technologist to lead the AI initiative, they found that technical experts alone did not make the biggest difference.

Instead, ”47% ranked leadership effectiveness as the single biggest driver of AI ROI” and it was by far the biggest factor with workflow integrations at 15%, organizational culture at 11% and engineering talent at 8%.

What’s more, less than half of the respondents said they felt they had the right people on board to join the 5% getting positive results.

The authors propose that the companies with the most success with AI focus on leadership and “. . . have developed organizational capabilities to amplify their technical talent in ways that are aligned with core strategy.”

Image AI Generated

The authors found there was a theme among the companies that are getting a good ROI on their AI activity.

While many companies hire a superstar technologist to lead the AI initiative, they found that technical experts alone did not make the biggest difference.

Instead, ”47% ranked leadership effectiveness as the single biggest driver of AI ROI” and it was by far the biggest factor with workflow integrations at 15%, organizational culture at 11% and engineering talent at 8%.

What’s more, less than half of the respondents said they felt they had the right people on board to join the 5% getting positive results.

The authors propose that the companies with the most success with AI focus on leadership and “. . . have developed organizational capabilities to amplify their technical talent in ways that are aligned with core strategy.”

A key distinction for success is to design AI experiments that are focused on material business outcomes.

The authors stress the importance of being strategic; of going beyond looking for only efficiency and connecting tools to larger goals; and, focusing on business value, not just novelty.

They noted being strategic was ranked first or second in importance by 65% by those interviewed.

Applied curiosity was the second most important characteristic at 47%. Leaders are encouraged to have clear learning objectives, disciplined experiments and to look beyond just activity metrics to business outcomes.

It’s a best practice to have leaders role model participation in pilot programs and not to rely only on others to explore and learn for them. They also warn not to conflate effort with impact.

Trust is another key factor. Leaders should avoid rolling out changes top-down with little input and pitting AI against people.

AI is a tool to enhance an employee’s capacity. Look for ways AI can elevate contribution, not just drive efficiency. “Actively address employees’ fears, don’t assume adoption will take care of itself,” the authors wrote.

Personally, I think psychological safety is always important and especially so to support creativity and innovation.  It will also be important to encourage enthusiasm rather than fear about change.

In this vein, oversight, active management and transparency are all a plus. Encourage leaders to be proactive and anticipate ethical issues and be accountable.

Performance is really the bottom line. Focus on what’s making a difference and opportunities to scale. Be willing to let go of what isn’t working to make room better solutions.

With an emphasis on leadership managing the process of experimentation and adoption, leadership can be an accelerant that supports transformation at scale.

Adoption is a shared priority.

I’m a believer that leadership can come from any chair.  AI presents a huge opportunity for current and aspiring leaders.

With the recent news of big corporate layoffs and delayed hiring attributed to AI efficiency, I see an invitation to be the leaders who harness this powerful tool.