AI: What Does ‘Better’ Really Mean?

In an era increasingly leaning on AI for solutions, it’s essential to first consider: What does ‘better’ actually mean to us? This straightforward yet profound query is the cornerstone for truly impactful and meaningful progress. And so rarely asked of each other, let alone of AI.

Is ‘Better’ Universal or Contextual?

Often, ‘better’ is perceived as a universal concept. However, it’s predominantly contextual. An improvement in one situation might not equate to the same value in another. This aspect is particularly significant in organisational psychotherapy, where improvement dynamics are closely tied to human experiences and perceptions shaped by shared assumptions and beliefs.

How Do We Measure ‘Better’?

The criteria we use to define ‘better’ fundamentally influence the outcomes we aim for. Are we measuring efficiency, profitability, employee wellbeing, or innovation? In AI, these criteria direct algorithms and shape the generated solutions. The risk here is choosing narrow metrics that miss out on wider impacts, resulting in solutions that advance one aspect while potentially compromising others.

Does ‘Better’ Reflect Our Shared Assumptions and Beliefs?

At the heart of ‘better’ lie our shared assumptions and beliefs. It’s not just about the capabilities of AI and what it can do, but also what it use it to do within the framework of these beliefs. With the extensive possibilities AI offers, aligning these with our shared assumptions and beliefs – be it ethical considerations, social responsibility, or environmental concerns – is vital to ensure ‘better’ doesn’t lead us off course.

Can AI Entrench or Overthrow Our Assumptions and Beliefs?

This section explores the dual role of AI in relation to our shared assumptions and beliefs: its potential to either entrench existing ones or act as a catalyst for overthrowing and re-evaluating them.

Does AI Reinforce Existing Beliefs?

AI, by its nature, operates based on the data and directives fed into it. This can lead to the reinforcement of existing shared assumptions and beliefs, especially if the input data reflects historical biases or status quo thinking. In organisational settings, this might mean perpetuating outdated practices or overlooking innovative approaches simply because the AI is programmed to follow what’s been done before. The risk here is creating a self-fulfilling prophecy, where AI, instead of being a tool for improvement, becomes an agent of stagnation.

How Can AI Challenge Our Current Beliefs?

Conversely, AI possesses the transformative potential to challenge and overthrow existing beliefs. This is possible when AI is used not just as a solution-finder but as a question-asker. By analysing vast and diverse data sets, AI can uncover patterns and insights that human analysis might miss. In organisational psychotherapy, for instance, this could mean identifying unconscious biases, inefficiencies, or underexplored avenues for growth and development. AI can act as a mirror, reflecting not just what we know, but also what we don’t know or haven’t considered.

Guiding AI Towards Positive Disruption

The key to leveraging AI for positive and productive disruption lies in intentional prompting and continuous refinement. This involves feeding AI with prompts that challenge the norm, encouraging it to question rather than simply execute. Also, incorporating regular feedback loops where AI-generated insights are critically assessed and integrated can help steer AI away from merely entrenching beliefs to actively challenging and reshaping them.

Harnessing AI for Meaningful Change

As we integrate AI into our quest for ‘better’, it’s important to be mindful of its influence on our shared assumptions and beliefs. By consciously directing AI not only to provide answers but also to pose critical questions, we can utilise it as a powerful tool for challenging the status quo and fostering meaningful change. This balanced approach ensures that AI becomes a partner in our journey towards a ‘better’ that is truly reflective, dynamic, and aligned with our evolving shared beliefs and assumptions.

Are We Ready for the Changes ‘Better’ Demands?

Adopting ‘better’ often means embracing change. But are we prepared for this shift? Organisations may seek improvement but resist the necessary changes to achieve it. Recognising and preparing for these changes, particularly in how they impact the human elements of organisations, is key for successful integration.

Conclusion: A Reflective Journey Towards ‘Better’

As we harness AI for advancement, let’s remember to deeply explore what ‘better’ means to us. This introspective approach ensures that our pursuit of improvement encompasses not only the final objective but also the journey and the shared assumptions and beliefs we subconciously maintain. By defining ‘better’ in a manner that resonates with our unique context, shared beliefs, and readiness for change, we create a path for advancements that are not only effective but also resonant and sustainable.