Why AI augmentation and up-skilling are the secrets to boosting staff productivity

It may all have been said before (and repeatedly). But it does no harm to re-iterate that simply deploying AI without putting employees at the centre and re-thinking how work is done will deliver few productivity gains.

As Dave Treat, Global Chief Technology Officer at learning solutions provider Pearson, points out:

Those that just pursue a pure automation approach are hitting a wall. They’re getting some results, but they’re not reaching the technology’s full potential…There’s been too much of a focus on developing and deploying new tools and thinking about how it affects humans afterwards. But that’s not the way to do things with this technology.

Instead, using AI to augment how people work (rather than make them redundant) and embedding learning into the change process from the outset is a much more effective tack to take, he believes.

In fact, according to the company’s recent report entitled ‘Mind the Learning Gap: The Missing Link in AI’s Productivity Promise’, such a shift in mindset has huge economic benefits. Its modelling indicates that taking an AI augmentation approach and ensuring knowledge workers have the skills required to work effectively with the technology could add between $4.8 and $6.6 trillion to the US economy by 2034. This is the equivalent of around 15% of current GDP.

As Dr Mark Esposito, Professor of Economics and Social Policy at the Berkman Klein Center for Internet and Society at Harvard University, who is cited in the report says:

Automation offers quick, measurable economic returns. [It] can reduce labor costs, recover some of the technology costs, and ultimately, it can optimize an activity that you were already doing.

The benefits of AI augmentation

AI augmentation, on the other hand, is:

An economic multiplier. It’s about longer-term, more transformational returns. Augmentation can fundamentally change the ability of humans to ask and apply questions. It brings a whole new level of value and productivity.

Put another way, says Andrew Ng, Founder of DeepLearning.AI and the Google Brain project as well as Co-Founder of online course provider Coursera:

Ten percent cost savings is nice, but that’s not what excites businesses the most. It takes reengineering the workflows to get significant growth.

This means, the report attests, that:

The great promise of productivity from AI investment lies in making people better at what they do with the aid of machine technology. This is the real path to sustainable productivity gains: to build on, accelerate, and enhance human knowledge, expertise, and experience.

One way that employees are likely to use AI to improve their productivity in future includes gathering and sharing expertise across both digital and human teams. Another is orchestrating complex tasks undertaken by digital agents while applying their own human judgement as required.

The need for transformational change

But to make this vision a reality requires transformational change. The report explains:

Defining an AI augmentation strategy involves multiple steps and phases of work as AI is introduced into the system. The focus ultimately is on understanding the current workforce: the processes they work within, the roles they hold, the tasks they perform, and the new skills they need to apply in light of generative and agentic AI. The evolution of tasks, and the roles that tasks add up to, sit at the heart of the transformation.

The problem at the moment though is that:

Most companies sit back and hope that employees will change the way they work, use the tools optimally, and improve productivity. That’s not an AI augmentation strategy, and it’s unlikely to yield results. Instead, the crucial starting point must be to take a closer look at business outcomes that can be most effectively achieved through AI augmentation, and the follow-on changes in the tasks that knowledge workers are performing to achieve them.

What is important to understand here though is that most roles in future will neither be fully human nor fully automated: they will instead be hybrid. As the report points out:

This is the conceptual pivot organizations are only beginning to make. AI does not simply reshape tasks. It reshapes our understanding of work, responsibility and human agency. Companies that fail to articulate this new understanding will design jobs that are efficient but hollow, productive but de-humanizing.

Continuous, embedded, skills-based learning

Another key part of the puzzle to make people better at what they do is to provide them with more appropriate upskilling and training, Treat contends:

We need to change the nature of learning itself. The old model of logging onto a system and doing a 30-minute course doesn’t work. According to research, you forget 50% of information within an hour, up to 75% within a day, and 90% within a week. So, it’s just not effective if you take someone out of the flow of work. But what technology enables us to do is to drive learning experiences into the flow of work in a way that’s personalized to each employee’s needs and career objectives.

This is particularly important in a world where the average lifespan of skills are “shortening dramatically”, requiring a move to continuous, skills-based learning. The report explains:

Shifting job requirements are what we know to expect more of. So, it’s regrettable that most organizations structure their workforce and learning plans around static roles. Skills, on the other hand, act as reliable building blocks that can be moved across shifting job requirements. A focus on skills targets what people need to do, rather than the title they hold, allowing more adaptability and a clearer connection between people and business objectives…Skills-focused learning also allows greater agility for aligning with customer needs.

Taking this kind of approach to learning will require a shift in leadership mindset and organizational culture rather than a “massive capex investment” though, Treat says:

In the past, companies held onto their data too closely and told employees to take a particular course or to manually update ERP systems without making it clear why it would benefit them or how the data would be used. But if the proposition is about sharing skills data, which should move with employees from job to job, it changes the value structure…The idea is to empower people with their own data to help inform their careers and demonstrate how best to match new skills development with their ambitions. If they can gather the skills they want and apply them in their own interest, organizations just need to tap into that and enable it.

My take

It’s worth saying again and again until senior executives listen, take it seriously, and act on it: Employees matter. Change management matters. Up-skilling matters. And if they aren’t at the heart of your AI implementation, businesses will never achieve the productivity benefits most of them crave.