It takes not just one blow, but many, for a bat to become a Batman. And it all boils down to the tasks they do.
In the wise and penetrative words of Alan Perlis, “A year spent in artificial intelligence is enough to make one believe in God.” Let’s raise a toast to Alan and chat more about artificial intelligence (AI) in the same vein. AI looks like a miracle. And it’s a miracle created by humans – to a large extent. That’s why AI is becoming as powerful and mysterious as a Batman. It has so many shades, and it can fly to unimaginable heights. If we could look at some initial encounters, that should be enough to make us utter a big ‘Wow’.
AI- sliced by tasks
Let’s just take the tasks it does today. We can slice AI into Mundane, Formal, and Expert tasks based on this characteristic. The Mundane ones incorporate areas like perception, vision, speech, natural languages, understanding, reasoning, translation, and robot control. The Formal ones comprise complex mathematical, logical, and geometrical areas. The Expert ones are where we can bring in financial analysis, diagnosis, engineering, simulation, and scientific experiments.
AI – in the expert slice
Now, while AI is getting faster and more profound in all kinds of tasks, what’s interesting to note is its swift maturity curve in expert systems. The adoption curve here is quite fascinating and explosive.
As per the State of AI Report 2021, AI is stepping up in areas as complex as mission-critical infrastructure like national electric grids and automated supermarket warehousing optimization during pandemics. In addition, it is getting its roots deep into faster simulations of humans’ cellular machinery (proteins and RNA).
The McKinsey State of AI 2021 survey shows that AI adoption continues to grow and that the benefits remain significant— though, in the COVID-19 pandemic’s first year, they were felt more intensely on the cost-savings front than on the top line. Moreover, as AI’s use in business becomes more common, the tools and best practices to make the most out of AI have also become more sophisticated. Fifty-six percent of all respondents report AI adoption in at least one function, up from 50 percent in 2020. Notably, AI adoption increased most at companies headquartered in emerging economies, including China, the Middle East, and North Africa: 57 percent of respondents signified strong adoption, up from 45 percent in 2020. And across regions, the adoption rate is quite huge at Indian companies, followed closely by those in Asia–Pacific.
AI adoption is becoming common in areas like service operations, product and service development, and marketing and sales. Popular use cases are service-operations optimization, AI-based enhancement of products, and contact-center automation – do note that the most significant percentage-point jump has been in the use of AI in marketing-budget allocation and spending effectiveness.
AI- not so mundane anymore
AI is being used in service operations optimization (27 percent), contact-center automation (22 percent), AI-based product enhancements (22 percent), and product feature optimization (20 percent). In marketing areas, we see the use of AI in customer service analytics. It is also being used in customer segmentation (16 percent). In supply chain areas, too, AI is expanding its impact on logistics network optimization and sales-demand forecasting (11 percent). Not just that, AI is now being used confidently in risk modeling (16 percent) and fraud and debt analysis (14 percent). In factories, we see AI being deployed for predictive maintenance (12 percent) and for yield/throughput optimization (11 percent). AI is also becoming handy for top strategic tasks like capital allocation (seven percent) and treasury management (six percent). And all of this translates into bottom-line impact for sure. As much as 27 percent were seen reporting at least 5 percent of earnings before interest and taxes (EBIT) that’s attributable to AI. Many organizations also reported higher levels of cost cuts from AI adoption in the pandemic’s first year, while revenue spikes held steady.
It is evident that as we gain maturity and clarity on AI, we will see more and more expert tasks getting into the core adoption bucket of AI. This would, of course, need support on facets like skill-sets, human-in-the-loop expertise, and the adequacy of tools for AI.
Any enterprise that invests well in AI talent, AI models, and AI-ready data – is bound to unlock many more miracles of AI in the near future. So this superhero is growing brilliantly in various shades of tasks – as we move ahead.
AI is getting better and better at expert tasks. From a caveman to a Batman- and now it will need a good Alfred all the more. Let’s start getting those Bat-mobiles out.