Welcome to “Cutting through with KSIB”, a short monthly newsletter summarising key insights that I and the team at KSIB have reflected on over the last month. We are constantly challenging ourselves to join the dots in a world that is increasingly complex and to share the “so what” with our clients. I hope you find this useful.

I’ve discussed what the best format for this newsletter might be with several executives and have decided to keep it short, with some links to relevant articles and a podcast (approximately 20 mins) should you prefer that format.

Please feel free to email, DM on LinkedIn or text me with feedback or ideas.

Regards

Kristin Stubbins

AI continues to dominate the conversation:

1. AI seems overwhelming and fraught with risk, but the bigger risk is doing nothing

2. The way to get started is to experiment and start to learn the “art of the possible”

3. New technology can be harnessed to solve previously intractable problems

If you’d prefer to listen to understand the topics covered in this month’s newsletter, listen to our podcast. Eleanor Hall in conversation with Kristin Stubbins.

① AI seems overwhelming and fraught with risk, but the bigger risk is doing nothing

I spoke with at least 4 different business leaders this month who mentioned how far ahead the US seems with respect to innovation. A couple of them mentioned advertisements that are prolific on the West Coast of the US for AI start-ups and solutions. It feels like AI is BAU in the US. We run the real risk of falling behind as a country if we don’t embrace the opportunity afforded by AI. Yes, there are risks, but the bigger risk is that we don’t innovate and get left behind. KSIB recently published an article on this topic, and it incorporates a methodology that we have developed, and we keep iterating, to help companies safely navigate this new world: [Taking the first leap][3]. One of the key learnings is that it is important to get the environment right – creativity and risk management need to work hand in glove to make sure enterprise use cases are effectively managed.

② The way to get started is to experiment and start to learn the “art of the possible”

I had an hour meeting with 2 mid cap CEOs this month and the conversation quickly went to “how do I get started?”. We executed a couple of prompts together on GPT-4 and Perplexity and generated high level business planning ideas for both organisations. Both CEOs were surprised and excited by the potential emerging just from one prompt on each LLM. KSIB team members have been working with some large-scale adopters here of enterprise solutions in Australia and learning and reviewing what has been happening globally. We are trying to cut through and bring some of our learnings to you through our communication channels, including this newsletter. Each industry will have different opportunities, and the pace of change means that the “art of the possible” landscape keeps changing.

This is why I think it is important to focus on the fundamentals and to get the innovation system right at an enterprise level. The lessons that have been learned with respect to transformations and IT implementations over the past 20 years remain very relevant in this new world, maybe even more so. Read more about this here: A strategic roadmap to transformation - [4 lessons to be learned][5]. The technology is important but focusing on the team and how they adopt and challenge the outputs from the technology is critical.

I have also attached a detailed article from Uber on how they built a prompt engineering toolkit to help guide the team: Uber's prompt engineering playbook. This article focused on machine learning models which are now being overtaken with broader enterprise solutions, but the foundational principles are very relevant. Unleashing AI agents into a citizen led innovation environment is risky and needs top-down thinking and system building. It is worth the thinking time.

③ New technology can be harnessed to solve previously intractable problems

Many Australian organisations will not have the capacity to invest in large in-house data science or development teams. They will be looking to simpler ways to innovate and that is possible. AI agents are removing the need for big software development teams, and this will continue to evolve. Smaller in-house innovation teams can drive big outcomes. AI start-up companies are also developing specific applications to solve previously intractable problems.

A sensible way forward for us (the Australian business community) is to learn from early adopters but also to consider what big problems need to be solved. This might include business model problems and opportunities as well as specific, lower impact use cases. The innovation system needs to always balance creativity and risk management. This means a healthy working relationship, and tension, needs to develop between business owners, data scientists and technologists, and risk managers (noting that managing risk is the responsibility of all).