"The future won’t belong to humans or machines, but to those who master the hybrid skill set combining AI literacy and human literacy."

The future of work is not about humans versus machines. Instead, it belongs to those who master a unique hybrid skill set. This blend combines AI literacy with essential human capabilities. Literacy in today's world lies in the ability to learn, unlearn and relearn - this has never been more true as it is in today's partnership with Agentic AI.

Andréa and I delve into the what these combined forces could look like, and how to build the framework for operational implementation. Digital transformation requires a hybrid skill set that fulfils the three different facets of transformation (cognitive, behavioural and emotional), which in turn align with the aspects of workplace culture (how we think, act and interact).

We discuss how to build a culture of trust in AI, essential for successful collaboration and highlight a critical distinction : humans interpret data semantically, giving it meaning and purpose, while AI processes it syntactically, based on patterns and probabilities. This difference impacts decision-making and ethical considerations. 

Leaders of the future must be honest about and clearly see what tasks should be augmented using AI and how the time saved should be spent, i.e. what does AI do best now and, consequently, what should humans do better?

How are you ensuring that you, your teams & your organisation are developing the skills necessary to complement AI’s analytical power and drive results together.

The main insights you'll get from this episode are :

-      Democratising access to a hybrid skill set means defining how to navigate the ‘fear vs. opportunity’ narrative of human potential in a world of AI, harking back to ‘man vs. machine’ as opposed to embracing a ‘man with machine’ approach.

-      Digital transformation requires a hybrid skill set that fulfils the three different facets of transformation (cognitive, behavioural and emotional), which in turn align with the aspects of workplace culture (how we think, act and interact).

-      The cognitive transformation element, i.e. decision-making, is the most problematic for leaders as humans still believe in the old way of making decisions; leaders are most exposed to this risk due to their past successes.

-      In the words of Rasmus Hougaard, “ego is the worst enemy of leadership” and hampers effective decision-making - AI makes new things possible and humans are taken aback by the exponential rate at which we must learn and unlearn.

-      Prompting, data sense-making and re-perception mean that we need to craft better input for AI but also ask humans better questions - unexpected questions open our minds to novelty and creativity.

-      Our inherited educational model rewards good answers, not good questions, yet this stifles creativity and re-perception; the latter goes against the human (and educational) grain, but AI tools represent a good sparring partner.

-      Rather than a product-centric approach, we are now called upon to make sense of data, but AI and humans interpret data differently: humans interpret it semantically (adding their own perspective); AI interprets it syntactically (as tokens without understanding meaning).

-      The problem inherent to AI is that it does not understand or give meaning to its decisions and has no conscience about the action taken - humans must have responsibility for giving data meaning and not outsource this to AI.

-      AI learns on a binary basis without context; tasks that are too demanding generate bad outcomes due to a lack of adaptability and long-term perspective - AI requires a predictable environment to perform well.

-      Uncertain tasks result in hallucinations, generalisation and transparency problems (how conclusions are reached); hallucinations result from the need to provide an answer, and humans need to be able to recognise this.

-      AI does not fail often and boosts the need for humans to accept failure and fail more through smart failures – the value of learning is higher than the cost, but automation reduces the scope for failure.

-      Emotionally in terms of transformation, what does it mean to feel in the world of AI? AI’s empathy is code, not consciousness; it cannot prevent the uncanny valley effect of providing empathy but not reciprocating feeling.

-      Leaders must embody such reciprocation, blending efficiency with the HITL (human-in-the-loop) empathy touchpoint; the complexity of the human discernment process requires building a culture of trust in AI.

-      The cognitive dimension is about training people to understand how AI works and how to use it well to collaborate successfully: Do people feel reassured or scared? Do they feel that AI complements or substitutes them?

-      Leadership endorsement of the tool increases positive perception and builds trust as the lubricant for collaboration. Messaging and communication are also important, as is agency, both individually and collectively.

-      Outsourcing decisions to AI does not relieve us of responsibility - AI is not responsible legally, morally or technically, and our responsibility increases the more we outsource (risk of the ‘many hands’ problem).

-      Leaders of the future must be honest about and clearly see what tasks should be augmented using AI and how the time saved should be spent, i.e. what does AI do best now and, consequently, what should humans do better?

-      AI is not coming for humans - those humans who use AI well are coming for those who don’t; there must be an understanding of the sense of urgency as we cannot afford to miss the boat.

Find out more about Andrea and his work here

https://andreaiorio.com/

https://www.linkedin.com/in/andreaiorio/

https://betweenyouand.ai/