A promise of efficiency, productivity, cost reduction, and, perhaps, competitive advantage is driving the urgency for AI adoption in enterprises. An increase in exposure to AI technologies and usage has also brought awareness of potential misuse (intentional or inadvertent). “Responsible AI” is now at the discursive and regulatory forefront.
In this talk, A10 Networks VP of Product Ganesh Rajan will touch on being “confAident” with the key aspects of Responsible AI. He outlines some relevant regulations, and what to expect in the future with respect to Generative AI.
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On the statistical evidence of businesses adopting AI in recent years
“We hear a lot about the piece of AI adoption and the promises of rapid growth. I want to touch on some of the results of recent surveys on this. According to a survey by McKinsey and Company, the number of companies using AI has stayed approximately the same over the last five years, even though it has doubled since 2017.
“And just 50% of the survey respondents, and these are 1000 plus respondents worldwide, have admitted to having adopted AI. But notably, the companies that have adopted AI are investing more, hiring more expertise, and engaging in more advanced practices using AI. And by some business measures, are putting some distance between themselves and their competition.
“Now as far as they are in the U.S.A., the National Science Foundation study claims that their adoption remains fairly low. But big companies are now leading the way. The chart indicates the extent of AI usage and consideration across manufacturing industries and non-manufacturing industries. In terms of the percentages of AI in actual production, it’s pretty low compared to those under consideration or under evaluation.
“There’s yet another survey from March 2022 that showed only 31%, or at least 31% of the respondents, were not using AI at all. 43% were evaluating or considering AI, and only 26% of them had AI in production in one or more business units.”
On the challenges that come with AI implementation
“So what are the top challenges in getting up AI and running AI? First of all, there’s a lack of need for AI. And, there’s a lack of clarity in the understanding of the need for AI. That seems to be a big hurdle today to say, ‘Why do I really need AI?’ That leads to the inability to rally and convince stakeholders to invest without knowing the return on investment.
“The second piece is the lack of data. Companies do not have access to a sufficient amount of high-quality data. They are sometimes reliant on third parties, giving them data or relying on open source data. Or, they have to build systems to generate and capture the needed data.
“It sort of comes back to investments needed, that is, lack of AI skill sets in the house. These skill sets are very much in demand, and companies are struggling to hire data and machine-learning specialists. It requires a decent set of folks in the house to get started there.
“If you have inefficient or no cross-team collaborations or inefficiencies across the board, some solutions will be too complex. Despite getting them up and running, they may not deliver the anticipated results. This leads to imperfect implementations, unsatisfactory results, and all of that. All of these are fairly technical and they can be dealt with in time.”
On why there is a big push for AI regulations
“First of all, the trend is a rapid growth of AI consideration…more enterprises are considering investing in resources and using AI. There’s also an increase in daily use. AI could be driving decisions impacting our daily lives, cost of goods, healthcare, biometrics, and usage tracking.
“The 3rd is amongst enterprises the usage of AI in competition. It is driving urgencies and adoption with the promise of enabling faster and cheaper decision-making. The enterprise boards want to know the status of the AI and your enterprise.
“The concerns among users among customers are a violation of rights, restriction of free speech, algorithmic bias, unequal decisions, biometric surveillance, and improper usage of data. The aspect of security, who’s driving it? How is it working? How does it work?
“Then there’s the FUD: Will AI replace humans? This leads to the drive that says, I need to bring visibility and common understanding of the workings. In the past, there were uncovered shared responsibilities for decisions.
“That is a drive to push for regulations. This has brought various bodies, public sectors, private sectors, and national bodies together to say we need to bring some regulations.”
About the speaker
Ganesh is a Product Management Executive who brings both engineering development and product management experiences from his roles over the last 20+ years. His last 12+ years have been mostly in Product Management leadership roles. He has worked in established companies as well as in early-stage startups; in a couple of cases, he was one of the co-founders. Ganesh has had entrepreneurial and growth experiences in these companies, he has taken products from 0-1, in some cases turning around the technical direction. At present, Ganesh is in a startup, Konfer, Inc., that is building solutions to enable continuous AI governance, helping enterprises develop and deploy Responsible AI. Previously, he was at A10 Networks (ATEN), a public company, as the Vice President, Product Line Management.