Even when they consider it to be a crucial tool for optimization and even radical change in their processes and business model, the truth is that companies still have a tough time figuring out the true weight and relevance of artificial intelligence (AI).
A recent survey by Gartner showed that, for CEOs, the two main barriers for the implementation of AI in their companies are: the inability to measure the value of the technology and a lack of understanding of its benefits and uses. Both situations were pointed out as a main hurdle by about a fifth of the respondents to the survey.
The responses show that, although businesses are deploying AI as a tool, they are having a hard time coming up with adequate key performance indicators (KPIs), explained Erick Brethenoux, Distinguished VP Analyst at Gartner, during the presentation of the survey results. Without proper KPIs, companies lack a clearer picture of the value and impact that can be attributed to the technology.
Although it’s been heralded as one of the defining technologies of the last five years –and expected to define untold years to come–, the truth is that most people are still trying to figure out AI. Even experts in the field still struggle to solve the puzzles posed by the technology.
“People think that AI is the silver bullet that’s gonna fix everything, and that’s never the case, and it never has been”—Dan McLean
This presents a problem for businesses who are hyped by the possibilities of the technology and are heavily invested in its deployment. Without the proper knowledge, how can they tell that their investment was actually worth it?
“People think that AI is the silver bullet that’s gonna fix everything, and that’s never the case, and it never has been”, commented Dan McLean, Senior VP of Business Strategy at Capmation, in an interview. “You have to understand your business problem and what you’re trying to solve. A lot of times people get into AI and they think, ‘Well, it’s just going to tell me everything’, but they really don’t understand how complicated it is”.
The proper solution for the moment, according to Superwise CEO Oren Razon, is to have model observability. While it’s fine to understand that the software is working from a functional perspective, companies need to recognize whether it is working as expected in order to use it as a successful driver of business.
Gartner itself has recommended that vendors focus not on explaining AI as a technology to potential customers, but on presenting concrete outcomes.
"Don't explain how your AI works … your buyers won't care."
A lack of #AI understanding prevents buyers from persuading others to make the investment. We recommend not focusing on explaining the AI, instead, talk about it in terms of use cases and outcomes. #GartnerSEC pic.twitter.com/DjhSBrmmWQ
— Gartner (@Gartner_inc) June 8, 2022
Good News (In a Way)
The fact that company executives see unmeasurability and a poor understanding of AI’s value for their businesses as top barriers for implementation is, in a way, good news.
Both top responses to Gartner’s survey show that businesses have been deploying AI projects and now are dealing with the particulars of evaluation. In the 2019 survey, the two main barriers for implementation were security/privacy concerns and the complexity of AI integration. A couple years later, those two responses didn’t crack the top 10.
“It means that organizations have accelerated the use of AI within themselves and now they are turning around and saying ‘Ok, we’ve done it. How can we best measure and make sure that the money we spend is well used?’,” Brethenoux pointed out. “Wondering and worrying about the value of AI and benefits of its uses is actually a sign of maturity.”
AI has been described by many as the future of business across a wide array of industries: from manufacturing, retail and telecommunications, to travel, beauty and food. This has made C-suite executives aware of the urgency of its implementation.
“Wondering and worrying about the value of AI and benefits of its uses is actually a sign of maturity”— Erick Brethenoux
Another survey –this one done by Baker McKenzie– puts AI as the third technology perceived by companies as “core” in their current digital transformation strategies, just behind cybersecurity and cloud computing. In that same survey, 80% of respondents said AI/Machine Learning is considered as a current investment priority, making it the second most popular response.
Most CEO’s told Gartner that they had deployed about half (47%) of the AI projects planned. In the 2019 survey, the percentage was 35%, and 29% in 2017.
“In spite of the health crisis, organizations have refocused their efforts to optimize what they’ve been doing, to try to become more flexible inside their operations and inside their different processes,” said Brethenoux. “Therefore, they have seen more practical implementation of AI projects in their organization.”
Where’s the Money?
Despite being characterized as a sure-to-be revolutionary technology, AI still faces an obstacle known by many other disruptors: money. Or, to be more precise, the lack thereof.
Almost 60% of the companies surveyed by BackerMckenzie said that budget is the main barrier for the scaling up of their digital transformation, making it the top response. The lack of software/equipement took second place with 43%, and the lack of skills/expertise ranked third with 43%.
This presents a problem for vendors of AI solutions. Under a tightening economic landscape, with capital expenses on the rise, the sell becomes even tougher.
Nevertheless, a lower budget for AI and other tools for digital transformation might be a blessing in disguise for vendors offering these sorts of solutions in the Nearshore, where costs are lower.
“There’s an expectation in the market that there will be investments in these technologies. At the same time, it is a very resource-constrained area. There will be an appetite for taking advantage of third parties that provide those sorts of services,” said Peter George, Partner at Baker McKenzie, during the presentation of survey results.
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