Coaching and mentoring - The antidote to rampant technology

David Clutterbuck

17th September 2019

Whatever happened to conversation?

• Discussion

• Debate

• Dialogue

Seven kinds of dialogue

Analytics can be a dangerous distraction

• At a time when people are demanding to be treated as individuals, analytics treats them as statistics

• Linear systems thinking belongs to 20th century HR; we are now in an age of complex, adaptive systems, where coping with uncertainty is more relevant than creating predictability

Will AI make things better of worse?

Coachbot v AIEntityCapabilityFunctionRoleCoa

Entity Capability Function Role

Coachbot Reproduces human

algorithms

Applies human algorithms

more consistently

Tool

Basic AI Learns and adapts human

algorithms

Develops and experiments

with new algorithms

Adaptable tool

Advanced AI Able to pass Turing Test;

moderated creativity

Predicts human interaction

and responses

Collaborator

Super AI Intentional and self-aware;

exhibits curiosity

Elements of emotional

intelligence

Partner

Coachbots

• Provide simple algorithms for relatively simple and frequently met business and personal issues

• Provide instant access to know-how and expertise in simple, linear contexts

• But are of limited value in personalised, complex adaptive situations

AI is very good at...

• Tasks that are repetitive and relatively simple

• Tasks that can be broken down into multiple simple algorithms

• Tasks where context is stable and predictable

• Sticking to the task (humans can easily get distracted)

An AI therapist….

• Registers micro-expressions that are usually too fleeting for a human to notice

• More accurately observes other minute physical signs of stress• Continues the conversation while analysing for patterns • Holds an accurate record of previous sessions• Does not have to deal with interference from “parallel processing”

– where the coaches’ own “stuff” intrudes• People are less likely to feel “judged”

The ultimate robot challenge???

• Japanese robotics company Preferred Networks claims it will have by 2024 a robot capable of tidying kids’ bedrooms, using algorithms designed on the human brain to scan the floor, identify items and move them to where they belong

AI is quite good at...

• Assembling and experimenting with miscellaneous data, from which humans can generate creative solutions

• Observing and quantifying the whole picture (humans are selective in what they notice)

Pattern recognition

• Was a strength of humans

• Now equally a strength of AI, which can recognise a much wider volume of patterns

• Pattern recognition is based on algorithms

AI is not good at...

• Tasks that need a deep understanding of changing context

• Tasks that involve conflict of values

• Extrapolation from one context to another (they do one thing very well, but only one thing)

Dangers of AI

• Algorithms can reflect the biases (e.g. racial, gender-based) of the programmer

• They can over-simplify complex situations (e.g. causing financial crashes because everyone follows the same algorithms)

The diversity challenge

Headline in Sunday Times, April 28 2019:

“Q: How do you end up with AI that’s white and male?

A: Let Google design it”

AI Coaches

• Initially expensive to create but much cheaper to use (so especially useful in high volume situations)

• Available on demand

• Today’s coachbot is tomorrow’s AI

AI v Humans

In complex tasks

• Robots sometimes outperform humans at routine diagnosis; and sometimes vice versa

• Combining human experts and AI in decision-making usually works better than either on their on

Intelligence in humans and AI

• Humans have “general intelligence”, meaning we can apply learned knowledge in many situations and environments.

• An AI has “weak” intelligence: the ability to do one thing really well.• Human brains are not computers. They store and recover data in

totally different ways; they remember things as snapshots and impressions

• Creative breakthroughs come from breaking rules and are unpredictable; computers are designed to follow rules and make things predictable

McKinsey Perspective on AI

• What AI does well is learn from observation and become better at prediction than humans, but…

• As the value of human prediction falls, the value of human judgment goes up because AI doesn’t do judgment—it can only make predictions and then hand them off to a human to use his or her judgment to determine what to do with those predictions.

AI can...

• Recognise emotions, but not feel them• Deduce emotions better than humans can from analysis of written

or spoken words• Project empathy (even though it does not feel it) but not

compassion• Innovate (for example in creating haiku), but only by experimenting

with combinations; AI has not capability of imagination

Who is at risk of becoming obsolete?

High risk

• Skills and basic performance coaching, especially at the GROW model level

• Transactional (Fast Knowledge Transfer) “coaching and mentoring”

• Coaching with predictable patterns or following a set formula

Medium risk

• Basic level coaching for behavioural change

Low risk

• Transformational coaching and developmental mentoring

• Coaching that requires higher levels of judgement and/or drawing on relevant personal experience or wisdom

The importance of wisdom

• Lean wisdom – context (task) specific

• Broad wisdom – reflection on life experience (personal and vicarious)

• Meta-wisdom – brings together multiple, shifting perspectives

The Coach-AI Partnership

• It provides real-time information about what is going on in the conversation, in the client and in the coach

• It allows instant access to other sources of relevant and potentially relevantinformation

• The AI can suggest questions and lines of enquiry (you as coach have to spend less time thinking about what you are going to ask next)

• You can check your intuitions for confirming or disconfirming evidence• It creates opportunities for in-depth review of each coaching session, from the

perspective of alternative approaches or better wording of questions. • Each coaching conversation is a learning process for both the coach and the AI.

Practical ways to work with AI as a coach

• Emotional monitoring

• Scenario analysis – alternative dynamics

• Finding hidden patterns

• Suggesting ways through “stuckness” – e.g. menu of powerful questions

Algorithms in team coaching

• Initial interviews with team members are very time consuming, yet essential. Questionnaires are a poor substitute.

• An AI can learn to hold a structured information-gathering conversation similar to that of a coach

• The coach-AI partnership then reviews what targeted questionnaires could be helpful for the team to understand its dynamics better

Some challenges for coaches

• What about client confidentiality?

• How can we learn to cope with data overload?

• Who is in control of the coaching conversation?

• How smart does a coach need to be to partner well with an AI?

The challenge for HR

• Who ensures that AI applications are ethical?• When should you use coachbots, AI and old-fashioned human

coaches?• You can contract with a coach, but a coach-AI partnership…??• How can you assess coaching performance?• Will these developments make it easier or harder to create and

maintain a coaching culture?

Your thoughts and questions

????

Thanks for listening!

David Clutterbuck

David Clutterbuck PartnershipWoodlands, Tollgate, Maidenhead, Berks, UK, SL6 4LJMobile: +44 (0) 7710 170019Skype: david.clutterbuck1Twitter: Mentor2mentorsE-mail: david@davidclutterbuckpartnership.comWebsite: www.davidclutterbuckpartnership.com