Productivity, AI, and knowledge work

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The digital workplace offers many ways to improve employee productivity and make the working days more effective. But, can we actually achieve this, or is it all just hype?

The underlying question is, how does new technology make us more productive? Generally, becoming more productive requires us to change our behavior and form new working habits – and this behavioral change becomes more difficult if you want to make the change for a large number of people, the more generations you want to influence and more styles of working culture you face. This is where technology intersects with people and that is why I love working in this field. It is where you can make the most difference to people with technology.

The productivity challenge

Despite the promise, we haven’t, certainly in the UK, observed significant productivity improvements as technology has evolved. In fact, in recent years productivity improvements have been almost flat, as this Office for National Statistics data shows.

Other data shows that jobs have become less routine and more cognitive in function, i.e. we have more knowledge-based work. Technology has also advanced beyond recognition to support knowledge work but we are yet to see a productivity improvement. Houston, we may have a problem.

The important point here, for digital workplace leaders, is that efficiency does not equal productivity. Bringing in new technology that supports “x minutes” per day improvement for employees doesn’t necessarily mean that those minutes will be added productivity for the business. Only by properly responding to the productivity challenge do we change behaviors. The question is, what is the area of AI that the industry should focus on? Before we discuss that focus area, the business case for AI is so broad that you will run multiple initiatives in parallel – how can AI improve services to consumers, how can you combine AI with automation to allow chatbots to request services that are magically fulfilled, and many more. All those priorities are valid but don’t forget knowledge workers are drowning in too much information.

Email is the biggest challenge for the productivity of knowledge workers – could AI be the answer?

Without realizing it, many businesses will start to see the benefits of AI in their workplaces. In their report “Everyday AI, Harnessing Artificial Intelligence to Empower the Knowledge Worker,” Forbes and Microsoft questioned industry leaders whose responses suggested:

  • 51% felt AI would eliminate repetitive tasks
  • 33% felt AI would streamline decision making
  • 31% felt AI would provide new insights.

In my mind, one of the biggest impacts that the knowledge workers will see is “anticipating context” and “streamlining collaboration/teaming.” The main element holding knowledge workers back today is the information flow and maintaining output with that flow, with email having the most impact on people. Why do I say this is most impactful? Because in 2012, McKinsey research showed knowledge workers spent 28% of their day consumed in the email. By 2018, a similar survey by Adobe showed this was now 3.1 hours per day or 39% of an 8 hour day – and those under 35 spent more time on email than those over 35. This problem is significant and it isn’t being fixed by the generational shift. Email was also the most used method of communication in the workplace and this leaves two questions: how to shift channels of collaboration and, within the email, can AI help?

Thankfully, the good folk at Microsoft Research are looking at this challenge. In my opinion, their focus is correct, information flow and prioritizing emails, collaboration streams, and social feeds will have more impact on knowledge workers than any other element AI can assist workers with. Implementing systems that have AI with the ability to prioritize information is key. All the major cloud collaboration platforms are investing in AI to prioritize information for knowledge workers allowing them to spend more time focusing and less time filtering. Microsoft, for example, has AI tools either deployed or in development, such as focused in-box, Delve, which provides context, allowing you to see the most relevant files and will continue to help prioritize the flow of information. Microsoft has also been working hard to help people use AI to find optimal time for meetings. The other question is how enterprises can support the shift of work from email into activity- and project-focused collaboration. The potential will be to leverage workstream collaboration tools, such as Microsoft Teams. This is one area that many enterprises are focusing on to reduce the amount of time knowledge workers spend on email.

Turning AI into more productive people

The challenge with any technology in the enterprise is adoption and behavior. We’ve all seen claims that technology will save x hours per day or per week but sometimes it feels like we wouldn’t have any work to do if all these claims are to come true! Using AI to assist in prioritizing information and the shift to a workstream-based collaboration tools falls into this category – simply adding the capability doesn’t result in instant business value. In reality, AI and workstream collaboration will bring potential efficiencies and only by changing the way people work and behave will you be able to translate those potentials into business value. Digital adoption is key to enabling enterprises to transform traditional behaviors, break habits, and unlock those potential efficiencies. Our experience shows that making digital adoption engaging, aligning campaigns to the enterprise culture, understanding the profile of the workforce, and bringing friendly competition with gamification into adoption brings the most success. Without active leadership and support to drive change, no amount of those potential efficiencies would translate into personal productivity or business value.

For more information on the Capgemini adoption services for the digital workplace, you can read more about our Connected Employee offer.

Connected Employee: Great ideas should be shared

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