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AI Projects Management

An AI project must follow the same general rules and best practices that are fundamental for the success of any project.

Project management success depends on defining clear goals, effectiveness, efficiency, adaptability, effective collaboration, roles, responsibilities and accountability, monitoring, using the right combination of tools, and the ability to be prepared for and mitigate risks. Understanding and efficiently using the project resources are a decisive factor.

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Project Success Is Not Automatic

A study published in Aug 2024 by American think tank RAND showed that 80% of AI projects fail, more than double the rate for non-AI projects.

The study lists many difficulties with generative AI, ranging from high investment requirements in data and AI infrastructure to a lack of needed human talent., In addition, a recent Deloitte investigation finds that only 18 to 36% of organisations achieve their expected benefits from AI (Mittal et al., 2024), and only 53% of AI projects proceed from prototype to production (Masci, 2022). The “pilot paralysis” phenomenon, where companies undertake AI pilot projects but struggle to scale up, is epidemic (Gregory, 2021).

Best Practices

Here is a list of important TO DO things you must consider in an AI project

Start by investigating the specific user problem

and then determine which/if an AI solution/tool/architecture can solve it.

Carm Taglienti”: “AI project failure is 99% about expectations,” he says. “It’s not about the failure of the technology but the expectation of what people believe the technology can do.”

The company’s readiness for AI adoption must also be evaluated, including data readiness, cross-functional collaboration, and a culture that embraces data-driven decision-making. You must invest in training and upskilling existing employees in AI, and also in talent acquisition.

which is now cited more frequently than either cybersecurity or regulatory compliance. However, only 3% of firms are mitigating this AI risk.

No matter the solution, if your employees do not fully embrace it, it will fail to reach its potential/its goals.

AI systems rely on high-quality, relevant, and properly labelled data for both training and operation (Overby, 2020). Considering data as a universal asset is wrong. Its utility is highly dependent on the context and specific problem it is intended to solve. Act to prepare your data.

Have a contingency plan. See here a list of typical GenAI risks to consider in your project.

that apply to your industry/company. learn from other organisations’ experience.

Start small and simple and learn how to use generative AI successfully step by step.

AI Challenges

So what should you do to minimise the chances of failure in an AI project?

AI projects, particularly GenAI projects, come with their own specific challenges. Understanding these challenges and learning from other people’s experiences is crucial for the project’s success.

Understanding and efficiently using the project resources are a decisive factor.

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Project Management

Generative AI development is almost always an R&D project. It requires you to try, verify and iterate many times, while working in a flexible framework.

You need a framework designed to accommodate flexibility, fast deliveries, quick iterations and effective collaboration – Agile is the best fit for these requirements.

Meet Our Team

Lastly, you should carefully choose your AI technology partner. At Apsisware we take high pride in developing long-term relationships with our customers.

We have a rich and proven history of delivering successful projects – your success is our success!
Come meet our team and discuss the AI opportunities for your business in a free workshop.

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