This is Part III of a 4-part series. For a comprehensive understanding, read the full whitepaper here.
The role and responsibilities of a Chief (Gen) AI Officer (CAIO/CGAIO)
The CAIO/CGAIO has different profile and responsibilities compared to ‘traditional’ Chief Data and Analytics Officer. Perhaps as a sign of the times, on March 28 2024, even the White House made an announcement mandating federal agencies are required to designate Chief AI Officers “to ensure accountability, leadership, and oversight” of the technology.
The CAIO/CGAIO should have the following profile and skillset:
a. affinity to culture and people topics because of the transformation that GenAI will bring.
b. much deeper AI and data skills (both traditional AI and Generative AI).
c. experience on topics like trust & ethics, legal & social considerations, regulations, governance, and risk management.
d. business strategy and commercial acumen, because business models will be impacted.
e. ability to influence, build consensus, and partner across an organization.
f. technology and partner ecosystem know-how.
Consequently, CAIOs/CGAIOsare typically TTO or CEO-reporting senior executives with frequent Board engagement and with additional responsibilities towards regulators and industry bodies.
Further Background and Context
In 2023, AI was mentioned in nearly 80% of all Fortune 500 companies’ earnings, a notable increase from 266 mentions in 2022. Numerous studies estimate that AI could add between $10 and $16 trillion to the global economy by 2030. No wonder that since 2018, mentions of AI in Fortune 500 earnings calls have nearly doubled. And, whilst only 13% of S&P 500 have AI expertise on their Board, this is not simply empty market positioning. For example, capital market investments octuplet from 2022 to reach $25.2 billion in 2023. The investments seem justified: 42% of recently surveyed organizations report cost reductions from implementing AI, and 59% report revenue increases.
Extensive research and benchmarking has also found that productivity of workers augmented by AI increased by up to 40%. But the impact will be more widespread than many may expect: 44% of all working hours can be automated or augmented by GenAI (with several industries like financial services, comms & media, life sciences, high tech, public service, health, etc) being in the high 60s-80s percentage.
This trajectory is likely to continue: in the last 12 months, AI has surpassed human performance on several benchmarks, including some in image classification, visual reasoning, and English understanding, yet we’re at the beginning of this journey as AI is approaching, and will likely surpass, human-like performance for more complex tasks like competition-level mathematics, visual commonsense reasoning and planning. On the other hand, costs have significantly increased too, especially for state-of-the-art AI models. For example, OpenAI’s GPT-4 used an estimated $78 million worth of compute to train, while Google’s Gemini Ultra cost $191 million for compute.
Whilst the usage of AI is potentially becoming democratized, the barriers to entry are higher than ever. And employees and customers have questions and doubts: a recent survey from Ipsos shows that the proportion of those who think AI will dramatically affect their lives is now 66%. In America, Pew data suggests that 52% of Americans report feeling more concerned than excited about AI, rising from 37% in 2022. And the topic is also increasingly regulated. EU recently passed a comprehensive AI act into law, with significant implications, requirements and restrictions for companies operating in the EU. And the regulations in United States increased sharply by over 50% in the last 12 months.
Author: Tony Leng
Please feel free to contact Tony Leng directly via email tleng@hiec.com should you have any questions or would like to discuss the above or anything else further.
This is Part III of a 4-part series. For a comprehensive understanding, read the full whitepaper here.