刻舟求剑
(kè zhōu qiú jiàn)
AI may be new, but many organisations are still using old assumptions, workflows and operating models. 刻舟求剑 offers a surprisingly relevant lesson for transformation leaders today.
I learnt about this Chinese idiom when I was a kid and even memorised it for Chinese composition examinations. However, I cannot remember using this phrasein my daily conversations ever since then. Recently, I came across this phrase again and as I reflected on its meaning, it really got me thinking about the wisdom behind this phrase and how relevant it is in this fast-changing world today.
刻舟求剑 (kè zhōu qiú jiàn) describe s a man whose sword fell into the water while traveling on a boat. He then made a mark on the side of the boat where the sword fell hoping to retrieve the sword later from that spot. We laughed at his ignorance as the boat with the mark had already moved on while the sword remained where it fell in the water.
As I reflected more deeply, this story is about how one may still rigidly follow old assumptions while being oblivious to changing circumstances or use old solutions to solve evolving problems and hope to get the same results.
I am not saying that the lesson of 刻舟求剑 is that old knowledge has no value. Rather, it is that a reference point is only useful while the conditions that made it valid remain unchanged.
This further got me thinking about my own work in transformation and innovation, and the frameworks, methods, models and programmes that I architect. Do I still base these on what worked before to address problems and develop future thinking?
Factors such as the introduction of emerging technologies, including AI and automation, changing workforce demographics, and different levels of technology literacy amongst a multi-generational workforce have challenged how organisational development strategies should be developed. They also have an impact on how HR attracts, manages, develops and retains talent. New customer and partner expectations, as well as changing business landscapes have challenged established corporate
strategies, process and workflow designs, business models and customer engagement methods.
Zooming out to the macro level, these changes would also have an impact on approaches to industry development and transformation, ecosystem architecting, and attracting investments.
As transformation, ecosystem development, and innovation practitioners, if we continue to rest on the past laurels and assume that what had worked in the past will continue to work, and recommend strategies based on old models, we may not achieve the same outcomes. Worse, we may even miss opportunities and fall behind.
This is especially true when it comes to driving AI transformation.
What if our management frameworks are the marks on the boat?
For example, there are frameworks and strategies that were developed when organisations were predominantly human-operated and technology was largely deployed to automate predefined tasks, or when digital transformation generally meant digitalising processes and workforce capabilities
could be addressed through broad-based training programmes.
However, unlike in the past, AI is broad and pervasive. One can deploy it within a limited scope, or re-engineer entire organisational structures, workflows and business models.
While AI transformation is a top-of-mind topic for many boards and CEOs, it is quite common to see that leaders struggling with how to get started and what the right approach and strategy should be so that AI transformation can be effective and achieve the intended ROI. Another dilemma faced by leaders is deciding who in the organisation should be tasked with driving AI transformation? Should it be Strategy, IT, HR or Transformation, or Innovation department? Should it be done with an entirely new setup? Who should provide the oversight and governance?
There is no straightforward answer to this, and it often depends on the type of organisation and the industry it operates in. However, if the organisation is going to treat this as a side project, a typical staff training initiative or simply as an IT system deployment, then I am quite sure that it is unlikely to yield the intended ROI and this could eventually lead to the project being abandoned. It may then be very difficult to restart the transformation again, and the organisation could miss the opportunity to transform itself.
On the other hand, it is also important to recognise that leaders often have other considerations such as the cost of investing in AI, risks and governance, staff sentiments, and resistance to change and disruption, and ROI. These factors may contribute to decision-making challenges. The new AI models and products that are being released regularly may further contribute to a wait-and-see
approach.
In my opinion, organisations may benefit from a more agile approach.
1) AI transformation must be driven and owned atthe highest level, close enough to the CEO to challenge organisational boundaries, rather than becoming an IT, HR or innovation side project.
2) Establish a cross-functional team with the right mandate and empowerment supported by an AI Council or advisory mechanism that brings together technology, people, business, risk and external perspectives.
3) Identify high-value opportunities, challenge existingworkflows and redesign them from first principles before deciding where AI belongs.
4) Build a parallel AI-native operating model or department and test it in the delivery of products and services. Measure it against existing processes, then progressively migrate successful elements and replicate that success across other workflows and departments.
5) AI fluency, reskilling, redeployment, communication, performance measurement and difficult workforce decisions have to occur alongside AI deployment and transformation.
6) Be realistic about the expected outcomes inrelation to organisational readiness (read IDEAS 2.0) and the types of use cases selected for the AI implementation.
The danger today is that organisations may introduce AI but continue to mark the same old “boat”, with no change to workflows, the same existing departments, job definitions, approval processes and
organisational structures, as well as the same management styles, assumptions and ROI models. Then they wonder why they cannot find the “sword”.
AI practitioners can also learn from 刻舟求剑 too.
It can be easy for consultants and AI transformation practitioners to accuse organisations of using outdated models. However, we can become equally guilty of doing the same thing.
Design thinking, Agile, Lean, Innovation Labs, Corporate Accelerators, Digital Transformation Offices, Centres of Excellence, Hackathons, Workshops and similar approaches may not be entirely obsolete.
However, are we still placing the “mark” on the boat or are we anchoring a buoy to the point where the sword actually fell?
The question is whether we challenge the assumptions upon which these models were built and ask whether those assumptions are still true today?
To drive effective transformationand innovation, therefore the first question we should ask is what needs to change. We should challenge our assumptions, rethink our strategies, and be willing to adjust the methods and approaches that may have worked well before.
Let us not be 刻舟求剑.
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