Why AI Projects Fail and How to Ensure Success
Strategies for success in AI implementation
Continual advancements in artificial intelligence are leading to an increasing number of companies adopting AI, and other related technologies, across a variety of sectors.
Despite this surge in adoption, some experts suggest that the failure rate of AI projects can be as high as 85%.
I have to admit that I was more than a little surprised by this figure: despite knowing the significant difficulties that can be associated with these technologies, I would have suspected that it was much lower.
Having said that, let’s look at 4 key reasons behind these high failure rates, before discussing our 8 strategies for mitigating the underlying risks.
Identifying the Reasons Behind the High Failure Rate
1) Unclear or Misaligned Objectives:
A common pitfall in AI projects is not only the setting of unclear objectives but also the failure to align these objectives with the core strategic needs of the business.
One reason AI solutions become misaligned with the business objective is when they are developed in isolation from the broader business context: which can lead to mismatches between the technical capabilities of the AI and the actual needs of the company.
Even if a team develops a technically sophisticated AI solution, it will likely fail if it does not address a genuine business need. Ensuring that every AI project starts with a clear understanding of the specific business problems it aims to solve is crucial for its success.
2) Lack of Quality Data
Effective AI requires high-quality data: the old adage of “garbage in, garbage out” is apt here.
There are many aspects to what makes quality data and many ways (sometimes quite subtle) in which data can inadvertently become a problem.
This is something that I could dedicate a separate article to, but to summarise a few of the issues:
Incomplete Data: Missing critical information can lead to poor model performance.
Inconsistent Data: Variations in format or conflicting values confuse models.
Biased Data: Non-representative data can reinforce stereotypes and make unfair decisions.
Noisy Data: Random errors or irrelevant information obscure true patterns.
Lack of Diversity: Data that doesn’t cover all scenarios leads to poor generalisation.
Outdated Data: Stale data fails to capture current trends, leading to inaccurate predictions.
Small Datasets: Insufficient data doesn’t allow the models to learn effective learning or to generalise well.
3) Resistance to Change
The adoption of AI can necessitate significant changes in existing business processes, which may encounter resistance from employees who are unprepared for the transition.
We have to remember that there is more to the success of AI projects than just the technical solution, particularly when we are looking to change existing processes: being mindful of the human side of any organisation is critical.
Effective communication and engagement with all stakeholders are key to facilitating a smooth transition and ensuring the success of any AI project.
4) Insufficient Technical Expertise
A lack of necessary technical knowledge can result in AI systems that are poorly designed and do not meet the intended business needs.
AI projects are inherently complex due to their technical demands as well as their need for a deep understanding of the underlying business problem (by the technical team).
This complexity can lead to mistakes, especially if the project tackles a novel use case or if the technical team lacks experience.
Strategies for Mitigating Risks in AI Projects
Mitigating risks in AI projects requires a strategic approach that addresses both technical and non-technical challenges.
Here are 8 of the key strategies we use to help mitigate risks and enhance the success of AI projects:
1) Early Problem Identification and Mitigation
In the initial planning phase, we use our expertise to foresee potential issues and develop strategies to mitigate them before the project begins.
These potential issues can cover a range of areas, from data quality, integration risks, etc., this proactive approach helps prevent costly delays and ensures a smoother project execution.
2) Multi-Stage Risk Mitigation
One very effective strategy for reducing risk is to approach an AI project in multiple stages: this works particularly well for more risky projects, such as those where the team is attempting a new use case where they have little or no prior experience.
Feasibility Studies: Conduct initial studies with minimal investment to assess the project's viability. If a project is likely to fail, we advise halting further investment to conserve resources.
Proof of Concept (PoC): Develop a PoC to demonstrate the project's practicality with minimal resources before committing to full-scale deployment.
3) Business Alignment and Clear Objectives
For any successful AI project, we need to establish clear, measurable objectives and ensure they align with wider business goals. This sounds pretty easy, but very often this is never done either because the technical team assumes they know best or because of the siloed setups of the teams.
Many of the companies that successfully implement AI within their business avoid siloing their technical teams and instead bring them closer to their business Subject Matter Experts (SMEs) (e.g. Data Scientists and SMEs as part of a multi-disciplinary team).
If you’re a business leader with in-house technical expertise you might want to think about how to achieve this within your business.
4) Data Management Strategy
We emphasise the importance of robust data management, ensuring AI models are built on reliable and comprehensive datasets.
Part of this also relates to examining any potential biases that might exist within your data and developing strategies to deal with those.
5) Change Management
Change management is often overlooked as a means to helping AI projects succeed, but it is one of the most important, particularly to ensure longer-term success.
Even if an AI project produces the exact technical output that is needed, unless it is embraced by the team it can risk failure in the long-term.
The exact change management strategy is dependent on the type of AI implementation, e.g. whether it's enhancing job efficiency with supportive tools or transitioning employees to higher-value tasks through automation.
But whatever the circumstance, it typically includes comprehensive training programs, clear communication of benefits, and ongoing support to address any concerns and build acceptance.
6) Setting Realistic Goals
Being realistic about what’s achievable is critically important. While it’s great to be optimistic and want to set a high bar for what can be achieved, aiming beyond the capabilities of even state-of-the-art AI requires a rethink.
Unrealistic goals can often be identified early, especially by an experienced technical team.
Sometimes, goals stem from wanting to match a competitor’s success, but subtle differences, like data availability, can make this infeasible.
Conducting a feasibility study or proof of concept can help set realistic goals and prevent a project from being prematurely dismissed.
7) Long-term Thinking
Considering the potential of an AI solution from a long-term perspective is crucial. Often, evaluating a use case based solely on current capabilities might lead to dismissing it as impossible or not worthwhile.
For instance, a machine learning model with 90% accuracy might not meet the 95% required for full automation. Instead of abandoning it, partial automation can be implemented where the model is confident, with humans handling the rest. Over time, human inputs can improve the model’s accuracy.
This is just one example, but there are countless other scenarios where it’s important to consider not only what is currently achievable with a solution but also how it might improve or evolve in the future.
8) Expertise and Training
Expertise and training are key to minimising risks in AI projects. If the team doesn’t currently have these skills then there are several options available.
Upskilling may be an option, but AI is quite specialised (and different from seemingly similar areas like software engineering).
Hiring new staff with AI expertise can be beneficial, especially if the goal is to continuously develop new AI solutions.
Bringing in external consultants is another option. Depending on the company’s needs, consultants can provide strategic guidance, develop AI solutions to be handed over to the internal team, or manage the development and maintenance entirely.
Beyond technical development, investing in training programs for both technical and non-technical staff can build AI literacy across the organisation. This helps reduce risks and improve the quality and reliability of AI projects in the future.
Conclusion
Although the high failure rates of AI projects are a concern, adopting a strategic and informed approach can significantly enhance the likelihood of success.
Our strategies are designed to address common pitfalls and ensure that AI initiatives not only meet but exceed their intended goals.
If your company is struggling with AI projects, don’t hesitate to contact us, our expert guidance can drastically improve your chances of success, transforming potential failures into successful technological integrations.
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