The Unlikely Link: AI, Hypermobility and AgileBA

Bill Gates recently wrote a blog post about AI and how the choices we make now will have a profound impact on what happens next. This led me to think about the subject, somewhat randomly, through the lens of hypermobility and Agile Business Analysis. Unusual, but hear me out!

You might have heard hypermobility being described as someone being ‘double jointed’ but that’s not really the case. Whilst hypermobile people are often more flexible in their range of motion, it’s not caused by having additional joints but, instead, by a difference in their connective tissue. It affects around 30% of the population in some way or another, with 10% of people experiencing skin, joint, soft tissue and nervous system symptoms relating to their condition.  

I am hypermobile and, as I have discovered, so are many of my colleagues at the Consortium. Maybe we are taking agility to a new level! But, jokes aside, one of the things you learn very quickly with this condition (and are frequently reminded of by parents and personal trainers) is that just because your body can move in a certain way, it does not mean that it should. Having such a great range of movement can be useful, sometimes it can be impressive and occasionally a good party trick. But without enough control, that same range can lead to overextension, accidents and problems down the line. This is where I started to see a surprising parallel with AI. 

AI is drastically increasing the range of movements available to organizations. It can identify patterns, summarize huge volumes of information, create content, generate recommendations, and much more. It can also automate activities and support decisions that usually require significant human effort. We all know this and it feels very exciting. I am a big fan of AI and, as a linguist, it feels like a great time to be alive. However, like the popular Spiderman quote says, “With great power, comes great responsibility”. I think this familiar trope is particularly relevant here because greater capability creates a new question: Just because AI can do something, does that mean it should? And perhaps even more importantly: Who decides?  

This is where Agile Business Analysis comes in. 

Range is not the same as control

Hypermobility is not simply about having a greater range of movement. The real challenge is having enough stability and control around that movement. Perhaps organizations now face something similar? The question is no longer whether AI can carry out analytical work; it is doing that. AI can produce requirements, synthesize research, generate options, identify patterns and accelerate activities that would previously have taken considerably more time and effort. 

But being able to do these things is not the same as knowing when, why or how they should be done. 

Just like my colleague Dawn benefitted from working with an occupational therapist to understand how to navigate the unpredictable and complex nature of hypermobility, traditionally, business analysts have helped organizations make sense of complexity by better understanding their problems. Exploring needs, challenging assumptions, and connecting strategy with delivery. As technology makes it easier to move quicker, these capabilities become more important, not less. 

Proprioception for organizations 

There is another aspect of hypermobility that is relevant here: proprioception. 

Proprioception is essentially our awareness of where our body is in space, and in relation to itself. When movement becomes more flexible, awareness and control become particularly important (and much less simple) and organizations need something similar, to support them to move as one. If AI is allowing organizations to automate decisions, redesign processes, and move more quickly, they also need a strong sense of what is happening across the wider system. Without this awareness, an organization can move very efficiently while moving in the wrong direction, or not very efficiently at all as teams move quickly into one another’s way.  

This is one of the reasons why Perspective Analysis really matters. It can help organizations consider different viewpoints and understand the wider implications of their decisions.  

The skill is not simply to generate more analysis, which AI can do with ease; it is being able to interrogate it. As AI makes plausible answers easier to produce, the scarce capability may become the ability to ask which questions are worth asking.  

AI makes answers cheap, but good Agile Business Analysis makes questions valuable. 

Not all resistance is bad

Asking the right questions often means being willing to challenge assumptions and embrace a little friction! 

We often talk about friction in organizations as though it is automatically a problem. Slow processes, disagreement, challenges, and resistance are all things we instinctively want to remove, and sometimes that is absolutely right. But not all friction is bad, and there is such a thing as productive resistance.  

Think about that awkward stakeholder’s conversation or the person that challenges an assumption that everybody else has accepted. Or the workshops where competing perspectives refuse to fit neatly together. These moments, and we’ve all had them, can feel inefficient, but they are often where understanding is created.  

AI can remove friction remarkably effectively, but at what cost? Summarizing disagreements, proposing compromises or generating polished recommendations before everyone has finished their coffee can feel like progress. But what happens if the friction was doing useful intellectual work?  

If we remove every disagreement or moment of uncertainty, we might also be removing some of the thinking that happens because of them. This raises a question I think everyone needs to ask when using AI: Are we outsourcing our thinking? 

Knowing when not to move

This brings me back to hypermobility; there are movements my body is perfectly capable of making that I deliberately choose not to make. Not because I cannot, but because I understand the possible consequences. Repeatedly making the wrong movements can make them easier, but that doesn’t make them right.  

In Gates’ blog, he raises a similar idea around AI: that there may be areas we deliberately choose to keep human even when technology becomes capable of doing them. Perhaps mature organizations will develop their own version of a “human-reserved” space. There will almost certainly be decisions, conversations, or judgments where the question is not whether AI is competent enough to participate, but whether there is something important about human involvement itself.

Overextension is not the same as progress

Because of this, there is a general danger in treating AI adoption itself as evidence of innovation. Many organizations are currently rushing to demonstrate that they are using AI. That can encourage the practice of creating a solution before fully understanding the root cause of the problem: we have the technology, so where can we apply it? Agile Business Analysis asks the question the other way round: what problem are we actually trying to solve? 

This comes back to the hypermobility analogy. Greater range does not automatically mean better movement, and more movement does not automatically mean progress. AI creates more options for organizations, but the existence of those options does not tell us which are worth pursuing. That requires intentionality: problem before solution, intent before implementation and value before adoption. 

AI overextension happens when organizations stretch towards what technology makes possible before asking whether that movement serves a genuine need. 

Capability needs direction

Perhaps that is what living with hypermobility has unexpectedly taught me about AI. Having a greater range of movement can be a brilliant advantage in the right (very specific) circumstances, but range on its own is not strength and movement on its own is not progress. Having the capability to do something does not tell us which direction to travel; what matters is understanding where we are trying to go. This means recognizing the consequences of getting there and having the awareness and control to decide when to move, and when not to. AI is dramatically expanding the range of movement available to organizations, and that makes human judgment more important, not less. The challenge is no longer: Can we do this? The challenge is: Is this movement actually taking us somewhere we want to be? 

Those are Agile Business Analysis questions, and they may be some of the most important questions organizations have to answer in the age of AI.  
 
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