Crystal ball

AI: What does it mean to you?

Much of this month’s content is contributed by one of my clients, Fraser McCallum. Fraser has a background in molecular pharmacology but has been working in pharmacovigilance, the science of monitoring the safety of medicines, for more than 20 years. Over the last few years, in discussion with many of his colleagues, he has dreamt of a future paradigm for pharmacovigilance, one powered by artificial intelligence. He is now Business Lead for Voyager, a program to deliver just such a future.

In this article, he looks into his crystal ball to explore the impact of artificial intelligence on the world of work.

AI: What does it mean to you?

Artificial Intelligence (AI) may feel a bit like stepping into science fiction, but it is already shaping the world we live and work in. From social media to shopping and finance to national security, AI is here to stay.

I feel we should embrace it. Data is, after all, the next ‘industrial revolution’ and we can’t sift through all that amount of data manually.

I believe we have an opportunity to shape the way technology can support the transformation that each of us goes through, and ultimately use it to make our lives more fulfilling. That might be through new jobs that currently don’t exist, or perhaps – intriguingly – moving us to leisure-based rather than activity-based fulfilment.

Because technology plays such a part in shaping our journey means we will ensure it best serves humanity. You’ll know this if you have read Max Tegmark’s fascinating journey into being human in the age of AI, or followed the work of The Future of Life Institute, or explored the work of their partner (and more alarmingly titled) Centre for the Study of Existential Risk.

You might be familiar with the (possibly apocryphal) story of the poor workers and their wooden shoes? Known as sabots, or clogs, the workers threw their shoes into the machinery because they were worried it would replace their jobs. This gave rise to the word ‘sabotage’ and the phrase ‘clogging up the works’.

As for me, I don’t want to get left behind.

What does that mean though, aside from not being afraid of the future?

It means being open to throwing what we know about how we work out of the window. In the age of AI, we need to:

  1. Understand the difference between media hype and technical reality.

According to Tegmark, we are still a long way from human-level artificial general intelligence or super-intelligence of the science fiction stories of my youth. However, I have no doubt that we’ll get there eventually. After all, any time someone says “That’s impossible!”, the universe’s response tends to be “Hold my beer!”

  1. Work more closely with the data scientists and programmers who design and train these neural networks.

The programmers may have the technology, but we, the less-technically minded end-users and consumers of these new emerging technologies, have the insights and use cases which bring the value to our business and society. There are very few individuals who have both skillsets, at least until the education systems design AI into the appropriate courses. To illustrate this point, I suggest watching Greg Kohs’ 2017 movie AlphaGo, which follows the development of Google DeepMind’s ultimately triumphant Go system. (It was the first computer program to beat a professional human player of the game Go). Without such close collaborations and open-minded discussions, AI will be a technology looking for a problem to solve, and there are plenty of real problems that need real solutions.

  1. Redefine traditional systems and processes, and build capabilities in monitoring these when the hard work is performed by AI systems.

Linked to this closer collaboration between technology and business, it’s important to view how we actually conduct our various business processes. I come from a heavily regulated industry where checks and balances play an important part in assuring our compliance to those regulations. They themselves come from a time when information was still largely paper-based. Without going into specifics, I believe many of these process steps and timelines are obsolete when they can be automated and conducted in milliseconds. Where we can add the most value is likely to be in monitoring those automated activities and ensuring the machine is performing correctly and learning continuously.

My crystal ball now starts to cloud over and I don’t know where this path will take me individually or us collectively.

I have embarked on a journey internally that has a definite end. I’m tasked with bringing AI to my part of our company but have no idea where that’s going to lead me afterwards. That has perhaps been the biggest realisation for me, that the unknown can be a friend. There have been times when I’ve had a very clear idea of what I want to achieve and times when self-doubt has me questioning my sanity.

Thanks to a combination of coaching and mentoring, both formal and informal, supportive colleagues, leadership and project teams (and the occasional long run to iron out the kinks) I’ve found a way to dream of a future which is practical and achievable without the nightmares of obsolescence or redundancy.

Our project team and I have certainly had to be agile, by all definitions of the word, as we design and build our future. The degrees of volatility and uncertainty we have faced in doing something novel have often been surprising and occasionally breath-taking. I have no doubt that will continue, but our rock-solid belief that we are doing the right thing for our patients has been, and will continue to be, the foundation of our success.

We have an often stated mantra of “challenge everything”, which has been refreshing in a regulated, traditionally conservative, and risk-averse discipline. That belief has equipped us to deal with the complexity. Something I’ve learned however, is that you can become too used to dealing with ambiguity. Often there is a simple and definite solution staring back at you, if you have the people around to help you see it and the bravery to make that leap and embrace the art of the possible.

As many of my colleagues have heard me say, I firmly believe that in helping design our future system, I have the best job in the world. One that truly makes my heart sing. It still fills me with a sense of nervous excitement and trepidation, a feeling I guess will be felt by many as the veils of the future are lifted still further. I don’t believe AI will take my job, but I know it will transform it and I need to develop (as AI has) in order to be ready.

Will you be ready? Or do you feel like throwing your sabots into the AI machine?


Food for thought from Rose


You might recognise this diagram about the evolution of business paradigms from my article about Creating a values-driven organisation. It shows how workers were originally rewarded for their strength (see Fraser’s reference to the workers and their clogs), next, for their skill at operating equipment, and then for the information in their heads. We’re now in an age where human value comes from our creativity, vision and emotional intelligence – qualities that can’t (yet) be replaced by AI.

It’s hard to predict how far AI will go, and how long we will stay in control of it. World leaders are already expressing concern that AI is more intelligent than us, and that it could ultimately control us – not in a good way.

On a scale of 1 (low) to 10 (high), how would you rate yourself and your organisation in relation to these statements?

What way of thinking and being do you need to embrace?

  • I’m nervous about the impact of AI   |   I’m excited about the impact of AI

Consider your mindset and confidence – what beliefs do you hold that enable or block you being open to the idea of re-imagining your value? What skills and personal attributes do you have, that you can apply in navigating the unknown and will release you to be creative? Use these as enablers, and try and not worry about being unable to predict your future – after all, we’re all in the same boat!

Are you comfortable with uncertainty, ambiguity and change?

  • I like to think I have all the answers   |   I’m ready to let go of ‘having all the answers’
  • I’m afraid of saying “I don’t know”   |    I have no fear of saying “I don’t know”

If you are part of a culture that values knowledge and expertise, saying “I don’t know” can be seen as a weakness. Whereas, in the age of AI, it’s actually a strength. How will you role model an “I don’t know” mindset and behaviour, especially if your boss, team and those in authority see it as weak?

How do you respond to people who don’t see things the same way as you?

  • When someone has a different perspective to me, I see it as a hassle/threat   |   When someone has a different perspective to me, I’m curious and see it as an opportunity

You need to listen and build on different viewpoints. Don’t just shut them down.

Organisational values

The image below is a model that provides a way of looking at organisational values. Another way of looking at it, more simply, is “how things are done around here”.

If the organisation feels like a good place for you to be, you’ll be able to add your best thinking. If the culture enables you to embrace AI for business transformation, long-term success will be more likely to flourish.

Level 7 in the image considers benefits to society overall and a long-term contribution (rather than solely this year’s business results, reflected in level 1). Once an organisation meets Level 5, the culture becomes an enabler, as it leaves ego behind.

More can be found on this in my previous article, mentioned above. I’ll also share some more thoughts next month…


Next month

AI is just one of the things that is turning everything we thought we knew on its head. Change is happening faster and faster. No-one really knows what the future will bring. With the future world of work so unpredictable, what can you do?

The only way to be ready is to cultivate an attitude of agility.

Agile working is a strong focus for many of my clients and will become a key competitive differentiator. Therefore I am going to explore this in the next three articles: agile organisations, agile teams; and agile you. What does it mean and what are the key success criteria… Watch this space!

P.S. If you are working in a future-focused area and would like to be a guest author, please let me know.