When all you have are large language models (LLMs), everything looks like a prompt. Enterprises must avoid falling into the trap of the law of the instrument, a.k.a. Maslow’s Hammer.
Legacy systems are typically challenging for CIOs – they often support critical business processes; they are expensive to replace and increasingly don’t meet business needs. As a result, they have been historically placed into the too hard basket. CIOs with significant legacy issues need to develop a deliberate strategy to address these challenges, rather than the squeaky wheel approach.
In November 2023, IBRS published an article titled ‘The Top Reasons Why ERP Projects Fail – Lessons From the Front’, which explored the top seven reasons why ERP projects fail and how to mitigate them. In the short time since, we have seen the reporting of a number of high profile ERP projects that have been abandoned with costs in the 70–200 million dollar range.
It is timely to look back, so this article will advise on reinforcement and subsequent advice that can be offered today as a result.
Artificial intelligence (AI) bias threatens to undermine the fairness and effectiveness of enterprise AI solutions. We examine key types of AI bias, their real-world impacts, and practical mitigation strategies – focusing on examples from Australia, New Zealand, and India. Learn key approaches to develop more equitable AI systems that serve diverse populations and unlock the full potential of AI for your business.
Uncover the game-changing capabilities of AI agents in revolutionising automation strategies for businesses.
By implementing a home-grown solution, the company was able to slash dosing errors and enhance efficiency
Key insights and strategies to help enterprise IT leaders successfully navigate the transition to the dynamic world of startups.
Uncover the secrets to successful machine learning (ML) projects in enterprises, from avoiding common pitfalls to driving significant business value.
By sidestepping hype and misconceptions, companies can stay grounded while still tapping into the transformative potential of recent breakthroughs in large language models (LLMs). A clear-eyed view will separate organisations that achieve material value from those that squander resources chasing an AI strategy.
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