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.
Maximise your artificial intelligence (AI) investments by strategically aligning platform choice with your organisation’s AI maturity level.
To truly harness the transformative power of artificial intelligence (AI), enterprises must shift their focus from standalone AI applications to comprehensive AI systems that are deeply integrated into their existing workflows and processes.
Embracing large language models as an integration layer can enable enterprise application development by providing a natural language interface between diverse systems, APIs, and human users. This approach allows seamless data flow, intuitive user experiences, and rapid prototyping of complex applications, dramatically reducing development time and costs while opening new avenues for innovation and process optimisation across the organisation.
Unlock the secrets to successful ICT project prioritisation with a proven framework.
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.
Aligning skills and developing a strategic workforce plan can drive successful digital transformation within organisations.
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