A week or two ago I came across an article from Business Standard that summarised a Gartner report suggesting that over 30% of GenAI projects won’t survive beyond proof of concept (PoC) and will be dropped by the end of 2025. Having run a large project portfolio I’m always interested in stats like this so I decided to pick at this a little and see whether this is indeed an issue, or just a headline.
What is it?
The Gartner report is said to predict that about 30% of GenAI projects will be abandoned by the end of 2025, citing reasons such as poor data quality, inadequate risk controls, escalating costs, and unclear business value. Having said that respondents also reported average improvements of:
- 15.8% revenue increase
- 15.2% cost savings
- 22.6% productivity improvement
What does it mean from a business perspective?
Lets compare this with data from the Project Management Institute (PMI) on successful projects to see whether the 30% of abandoned projects is out of sync with broader project data, and we see that successful projects are in the 70% range (to be fair, this is a bit of an inference from the PMI Pulse report), and we also see other project success rates from the PMI in the 50%-90% range (depending on how you look at the data).
Overall, 48% of projects qualified as successful, with 12% an outright failure and 40% with mixed views. PMI used this data to calculate a Global Net Project Success Score of 36 (48% rated successful – 12% rated failure = 36)
There are a few things to recognise here:
- Dropping projects at the end of the PoC doesn’t mean failure – the purpose of a PoC is to prove whether an idea is feasible or not and whether it deserves further investment (its a good risk mitigation and resource allocation approach).
- 30% of projects being abandoned is broadly in-line with industry data (assuming a PoC is recognised as a project or a project phase).
What do I do with it?
Given these insights, here are some actionable steps for businesses considering or already implementing GenAI:
- Start with a clear strategy: Identify specific use cases where GenAI can deliver significant value. Focus on areas with potential for major productivity gains or cost savings.
- Invest in data quality: Before jumping into GenAI, ensure the data you plan to use is clean, well-structured, and accessible. This foundational work will pay dividends in the long run.
- Plan for adoption: Develop a comprehensive training and support plan. The success of your GenAI project depends as much on user adoption as it does on the technology itself.
- Set realistic expectations: While the potential for results is real, its important to set realistic timelines and KPIs.
- Use PoC’s: Use PoC’s to test the approach and whether this use-case is going to work out and don’t be afraid to stop at the end of the PoC – it’s a valid approach.
- Stay agile: The GenAI landscape is evolving rapidly. Be prepared to pivot your strategy as new technologies and best practices emerge. Use PoC’s.
- Start small: Consider starting with smaller, targeted implementations to build momentum and prove value.
Approaching these projects with a mix of optimism and pragmatism will help you be successful and maybe the most important thing from all of this is to use PoC’s to understand whether you have a success or failure on your hands.
Further Reading
PMI Pulse of the Profession 2023
Business Standard – Over 30# GenAI projects won’t survive beyond proof of concept: Gartner
PMI Blog – Reframing Project Success
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