Implementing technically perfect solutions often meets unexpected resistance.
Even with vast amounts of data and advanced AI, organizations struggle with decision-making.Russell and I explore why this is so. Because the problem isn’t always technical; it’s systemic. We discuss case studies where this happens and we see this pervasive structural issue. Dropping an amazing gadget into an ecosystem without addressing “structural changes” like incentives, rewards, and identity within the organization can lead to rejection. It’s not just about changing tech; it’s about changing the “collective habit of the system.” This idea of small impactful experiments and measurable success is key to creating momentum for adoption and scaling the understanding of the value of AI-human collaboration to drive organizational change.
Leaders must address these systemic barriers in order for the organisations to intentionally redesign workflows, processes, relationships and results.
What structural changes does your organization need to make to truly embrace AI-driven insights and transform decision-making?
The main insights you’ll get from this episode are :
- Decision-making is still a big failure point in transformation; even if data is perfect, humans still don’t make the right decisions due to excessive information and emotional aspects that cloud our judgement.
- The (dysfunctional) product innovation process clearly displays the consequences of a poor decision over the course of idea to product – despite huge input, many products don’t succeed.
- The competitive process of gaining customer insight is exacerbated by huge amounts of data and embodies a pervasive incentive system that is punitive if things go wrong or fail.
- AI makes things worse with a glut of data – we need a balance between humans enabled by AI (to provide sanity checks + intangibles) and the use of AI to synthesise and curate huge volumes of data.
- To keep humans in the loop, we must intentionally slow down in the rush to automate as AI + human outperforms either on their own; we can partner with AI to make sense of unstructured data as part of a stage gate model.
- Structural and systemic issues remain obstructive – leaders must disrupt the system to facilitate sustainable transformation, but legacy human systems are not as quick to update/upgrade as legacy tech systems.
- Leaders need compassion for this evolution and should aim to reshape rather than reduce their organisation; a pragmatic approach to understanding the tech and a vision for the organisation’s purpose and mission are vital.
- The potential of AI in future could see it empower us to make better decisions; it is time now for action to experiment and succeed on a small scale as a decisive factor for transformation.
Find out more about Russell & his work here :
