Good morning. I spoke with Sol Rashidi, an AI expert who helped IBM launch Watson back in 2011, about strategies and challenges for AI deployment in the enterprise.

She said that the field has changed a lot in the past 10 years; the biggest change, of course, was the launch of ChatGPT, which made generative AI accessible in a way it’s never been before.

Read on for the full story.

— Ian Krietzberg, Editor-in-Chief, The Deep View

In today’s newsletter:

AI for Good: The United Nations’ World Environment Situation Room

In 2022, the United Nations Environmental Program (UNEP) launched its World Environment Situation Room (WESR), a digital platform that leverages AI to collate and organize climate-related data. 

The details: This system enables both (near) real-time analysis and future predictions of sea level rise, changes in glacier mass and the concentration of carbon dioxide in the atmosphere, among other things. 

The organization added that AI tools can be used to track the environmental footprints of specific products and their supply chains. 

Why it matters: “Reducing the energy sector’s methane emissions is one of the quickest, most feasible and cost-effective ways to limit the impacts of climate warming, and reliable data-driven action will play a big role in achieving these reductions,” David Jensen, coordinator of the UNEP’s digital transformation’s taskforce, said.

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GenAI adoption slows amid enterprise concerns over inaccurate responses 

Cost and accuracy, according to a new Lucidworks study, are among the more significant barriers to generative AI adoption in the enterprise. 

The details: Lucidworks’ second annual Generative AI Global Benchmark Study found that 44% of manufacturers are increasingly worried about the accuracy of GenAI output, something Lucidworks called a “legitimate concern with potentially disastrous consequences.” 

Why it matters: This — cost and accuracy — is the fundamental challenge of the business of AI. VCs are betting that it will be solved, and in glorious fashion, as funding to AI startups spiked heavily in the second quarter of the year. 

But an increasing number of analysts and investors are concerned about those two points — cost and accuracy — which could be the two-pronged maneuver that brings the hype-inflated industry crashing back to earth. 

Vimeo, Etsy and the AI wars

Earlier this week, Etsy updated its policies to account for generative AI, saying in a blog post that sellers will be allowed to sell AI-generated art, so long as they disclose that it was AI-generated. 

This move comes as the major question of whether it is a legal violation of existing copyright law to train AI models on art, photos and text without artists’ knowledge, permission or compensation remains unanswered (and in litigation); Etsy acknowledged this, saying it will be “closely monitoring developments” in this arena. 

Now, just a few weeks ago, video-sharing platform Vimeo — also in a blog post — published its stance on AI, saying that it won’t allow AI models to be trained using the videos hosted on its platform without users’ “explicit consent.” 

Vimeo added that it prohibits unauthorized content scraping by model developers and implements “security protocols designed to protect user-generated content.” 

Why it matters: There are lines being drawn — as we are about to get into below — between the companies who have decided to fully embrace AI, period, and the companies who have deemed it a smarter strategy to build user trust by not embracing AI. 

This tale of Vimeo and Etsy exemplifies those different approaches. 

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In conversation with Sol Rashidi: The human problem with AI deployment 

In the enterprise, generative AI is beginning to move out of the experimentation phase and into the real-world deployment phase. Deloitte’s second-quarter State of Generative AI in the Enterprise report — which surveyed nearly 2,000 executives around the world — found that businesses are focused now on deriving actual, tangible value from their genAI experiments.

But what the report found is that organizations with “very high” levels of expertise in AI are moving much more aggressively on AI. And much higher percentages of these organizations are achieving the kind of tangible benefits their leaders have deemed so important. 

The core of the divide between the companies having success with AI and those that are not has to do with corporate strategies and the humans that create them, according to Sol Rashidi, an AI expert who has achieved numerous accolades in the space, including inclusion in 2022’s “50 Most Powerful Women in Tech.” 

I sat down with Rashidi — who helped IBM launch Watson in 2011 — to discuss the challenges of deployment in today’s environment. 

How AI deployment has changed in the past 10 years: Artificial intelligence is not a new technology. It got its start back in the ‘50s and experienced a surge of popularity in the ‘70s (which is when the term “AI” was coined). IBM’s launch of Watson in 2011, according to Rashidi, marked the “first time” that these academic concepts entered the corporate world. 

The release of ChatGPT at the end of 2022 made it accessible to everyone. 

“I think the biggest pivot is it's no longer focused on enterprise, but now everyone can leverage it,” she said. 

Deployment is a human problem, not a technical one: And while ChatGPT might have solved the problem of accessibility, the means of adoption come down to human strategy — and not all strategies should be made alike when it comes to AI. 

She said that a common mistake companies make is to forcibly attempt to apply AI to create business value when it can instead be applied in targeted internal ways. Using AI to save a given organization money by making internal processes less costly, according to Rashidi, creates a “tremendous amount of business value.” 

It’s not all about outward-facing deployment.  

Sometimes the best AI strategy is no AI strategy: And in the midst of this feverish push toward more and more AI, there has been a rising backlash against it. Artists don’t want their content to be used to train AI systems, and many consumers are disinterested in using systems that are untrustworthy and biased. 

While many organizations are pushing harder on the genAI hype train, this surge has led others to go the opposite direction; Dove in April pledged to “never use AI to represent real women in its advertising.” 

Rashidi said that sometimes, the best AI strategy is no AI strategy. 

“Why use a sledgehammer when a hammer does the trick? AI can be overkill and sometimes AI is not the answer,” she said. “That's just the simple reality of it.”

A poll before you go

Thanks for reading today’s edition of The Deep View!

We’ll see you in the next one.

Your view on whether AI progress should slow down:

Around 35% of you agreed with the idea that a cautious approach is preferable to being the first country to develop more powerful AI systems.

And around 30% of you said the opposite.

Agree:

Disagree:

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