A great deal of performance improvements through AI stem from automating tasks and moving labor from humans to machines. However, recent research highlight that the reality is more subtle and that human resource is a major component and pre-requisite for leveraging performance improvements through automation with AI. Additionally, using AI has significant impact on staff qualifications, job descriptions and organisation.
What if the key to succeed in leveraging automation with AI was human-based? What does it mean for strategy making and competition dynamics?
A new wave of automation with AI
The abundance of non structured data, combined with democratization of storage and computing power reinforced by open source software opened avenues for machine learning algorithms to perform more and more complicated tasks much faster and sometimes with a higher quality than humans. Algorithms are massively used for recruitment (reportedly, 75% of resumés are read by an algorithm in the US), while actors worry that AI is taking centre stage (FT) Text generator GPT-3 and text to image generator DALL-E were recently made accessible enabling everyone to use them.
These progresses are associated with a transfer of labor from human to machines. With more tasks and decisions automated, the need for human labor is lower. In their research, Tchang and Almirall look specifically on the impact of AI as automation on employment and their main conclusions are the following:
- AI is augmenting automation, hollowing-out of middle-skilled jobs. AI allows firms to modularize and control routine work.
- The remaining work tends to be non routine and low-skilled (allowing for further replacement in the future), or high-skilled.
- Dynamic effects occur when AI is combined with other key technologies, creating economies of scale and scope for firms.
- Through augmentation, the resulting employment structures may also have lower quantities of high-skilled work. This depends on advances in AI, and its ability to replace more complex forms of work.
Because they automate tasks, AI algorithms contribute to reducing the human labor required.
Behind the automate, human labor is important in AI
When taking a closer look, automation with AI requires a lot of labor all along the value chain. There is a direct human effort and cost related to the training phase of machine learning algorithms. For an algorithm to perform a diagnosis on an image, millions of images need to be annotated, labelled by experts. Similarly, millions of labelled image of streets are needed to feed a self driving car algorithm. Later in the process, when the algorithm performs its tasks, human are usually still present to check if the program produces the expected results.
Then, there is an indirect cost of automating tasks with AI. Sometimes it’s visible when automation at a step of a process generates new burden at another step. In other cases, the indirect cost is made from the errors made by the algorithm. For example, when massive pricing errors are automatically at scale by an algorithm on an e-commerce website (Amazon reportedly offered a 13k$ camera lens for 99$). Similarly when bad outcomes and decisions are made based on a mislabelled dataset (30% of Google dataset on emotion is reportedly mislabelled).
Automation with AI triggers major changes in the workforce, example in a cost center
Additionally, for automation to deliver results, major job and labor transformation are required. The last finding of the research by Tchang and Almirrall is clear: as the impact of AI is massive on middle-skilled jobs, AI adoption requires significant changes in the business and operating models of organisations.
I had the opportunity to have a direct illustration of this finding during a conference I attended a few weeks ago. The responsible of a call center was explaining how puzzled he was about automation in customer service. The company had successfully leveraged several AI tools so support and automate their processes. Robot mails, chatbots, speech analysis, … The results on productivity were pretty clear at first sight: for the same volume of activity, less humans were needed without deteriorating the quality of the service.
However, the company is facing new challenges, somehow more difficult to solve than the previous productivity one. In fact, when a lot of tasks and answers are automated, the customer service agents are only dealing with the most difficult problems. Which has several side effects:
- First, the job becomes more demanding as they no longer have easy questions to solve, they never have some slack during the day
- Second, as they need to be more expert, more investments in training and experience are required
- Third, as the turn-over on the job is still high, the investments in training and experience are not paid back on a long enough tenure.
- Last, as the job is more demanding the possible candidates for occupying it are not numerous enough.
What does it mean for strategy making and competition dynamics ?
As the direct and indirect costs of AI are significant, not all use case are fit for automation. A recent study by MIT researchers showed that AI produces massive productivity increase for frequent and known problems. Interestingly they conclude that human perform better than tools for less common or new problems.
As the transformation required to become AI-fit is significant, the investments on transformation and change are massive for an attractive enough return to be delivered. It may concern structure, processes, job description, training, …
In terms of competition, as the productivity gains are important for companies succeeding to structure their organisation to leverage AI, a large productivity gap between “AI-fit” businesses and the rest is to be expected.
Using AI for automation requires to develop human and organisational capabilities, which is a much bigger investment than the direct investment in the technology. Automation is first and foremost a human resource matter.
Photo by ThisisEngineering RAEng on Unsplash