Managing AI projects presents a unique set of challenges and opportunities. Success in this field requires a blend of cohesive strategy, resource management, and technological expertise. Our Annual Trends Report has revealed that almost one in five UK organisations tried but failed to manage an AI project this year. To address this, we’ve compiled 12 proven tips to help organisations navigate and successfully manage AI projects.
Here are some tips to manage AI projects seamlessly.
1. Set clear expectations
To successfully manage AI projects, start with setting clear expectations. Data says that 46% of UK organisations that tried and failed to manage AI projects admitted they didn’t fully understand the business problem they aimed to address with artificial intelligence. To avoid this pitfall, understand what problems you want to solve and what you want to achieve with AI. Be aware of AI’s capabilities and limitations too.
However, the job doesn’t end here! Once you establish clear expectations, communicate the same to all the stakeholders involved in the AI project. By doing so, you can minimise misunderstandings and enhance AI benefits for your specific project.
2. Refine your data repository
Data is the fuel that powers AI engine. Yet, many organisations still lack a mature data framework that includes the right practices, processes, and governance to ensure high-quality data is fed into AI systems to produce quality output. Around 35% of UK organisations have admitted that they did not sufficiently review and clean up their data before beginning AI projects. Given this, organisations must have effective data management process to refine their data repositories. This helps them understand data requirements, including data sources, how to access that data and the state of that data.
Curious about how prepared organisations are for AI projects? Download our Annual Trends Report Now!
3. Establish business goals, not AI goals
When managing an AI project, focus on overarching business objectives rather than setting isolated AI goals. Organisations often fall into the trap of implementing AI just for the sake of innovation without understanding what they want to achieve. This can lead to waste of resources, misaligned deliverable, and project failure.
To avoid this, first identify the business objectives and challenges you want to address, and then define measurable outcomes. For example, do you want to enhance operational efficiency, improve customer satisfaction, optimise supply chain management by implementing AI solutions? That way, you can align AI benefits with business objectives and ensure that AI solutions directly contribute to overall strategic priorities.
4. Asses your existing infrastructure to support AI technology
Managing AI projects demands robust infrastructure. According to the Annual Trends Report survey, nearly 50% of AI projects fail due to gaps in technology and expertise, highlighting the critical need for the right tools and systems. Therefore, before initiating and managing AI projects, evaluate whether your existing infrastructure can seamlessly support the management of AI technologies. This includes assessing computing power, storage capabilities, network capacity, and integration compatibility with existing systems.
Cloud technology is a game-changer in this case. By leveraging cloud solutions, organisations can bridge these gaps, enabling access to scalable resources, flexible deployment options, and advanced analytics capabilities.
5. Build a cross-functional team
AI projects require expertise from multiple domains and functions such as data science, IT, business development, and project management. To effectively manage AI projects, it’s crucial to have a cross-functional team in place with each member bringing unique skills and perspectives to the table. This helps to ensure a more holistic approach, better problem-solving capabilities, and effective communication throughout the project lifecycle.
In addition to technical roles, organisations should also include representatives from various departments that will be impacted by the AI solution. This allows for a comprehensive understanding of needs and requirements from all stakeholders and promotes buy-in and collaboration across teams.
6. Choose the right AI model
AI is an intelligent technology that encompasses classical artificial intelligence, Generative AI, machine learning, deep learning, neural networks and more. These are neither identical nor interchangeable. AI project leaders must identify which AI is best suited to solve their business problems. For instance, if the project requires textual analysis of a large volume of unstructured data, natural language processing (NLP) and deep learning could be more effective than classical AI. Understanding the unique features and capabilities of each model is crucial to selecting the most appropriate one for your project.
Want to learn about top AI models? Check out our blog “Top 9 AI and machine learning trends to watch in 2024”
7. Develop a cohesive, cross-organisational strategy
Organisations appear to be undertaking AI projects without fully appreciating what is required for success and this may be because, in many cases, organisations have no overall AI strategy. According to our Annual Trends Report, 22% of organisations revealed that individual teams or departments are using AI tools independently, without a cohesive, company-wide strategy. This fragmented approach leads to siloed implementation, limiting the full potential of AI.
Therefore, to manage AI projects successfully, organisations must adopt a cohesive, cross-organisational strategy. This involves establishing strict policy and guardrails, as well as, stringent preparation and management of data, and having appropriate skills and expertise on board. Then only, AI’s transformative potential be fully realised.
8. Ensure ethical and legal considerations
AI technologies present a range of ethical and legal implications that need to be carefully considered when managing AI projects. This includes data privacy, bias in algorithms, transparency in decision-making processes, and potential consequences on society. Organisations must have a clear understanding of any regulatory requirements related to their project and ensure they are compliant with laws such as GDPR. Additionally, organisations should also establish internal guidelines for ethical AI use and regularly review their practices to ensure alignment.
9. Define success metrics for AI projects
Defining success for an AI project is often more challenging than it is for other types of projects due to AI's nature. For example, a probabilistic AI model will never be 100% accurate, so the organisation needs to determine how accurate the model should be with each specific use case before it can be declared a win.
Also, measuring the success of AI projects can’t depend on traditional metrics as those may not accurately capture the impact of AI. Therefore, it's critical to define success metrics for each project based on its specific business goals and objectives. These metrics could include improvements in efficiency, accuracy, customer satisfaction, or revenue growth. By having clear and measurable success criteria in place, organisations can track progress and make changes as needed to ensure project success.
10. Human collaboration is pivotal
Although AI is seen as a game-changing tool to transform the world of work, it can’t be the only plug-and-play solution. Human involvement is crucial for the success of AI projects. From data preparation and engineering to interpreting results and making decisions, human collaboration is essential at every stage of an AI project to manage it effectively. Therefore, it’s important for organisations to ensure that they have the right people in place with the skills and expertise to work alongside AI systems.
11. AI governance is a critical pillar
AI governance is a framework of policies, procedures, and controls that guide the development, deployment, and use of AI systems. As AI becomes more prevalent in organisations, it’s important to have a robust AI governance framework. It ensures ethical, responsible, and transparent use of AI technology by promoting data privacy, security, and accountability.
Want to know in-depth about the principles of AI governance? Read “A guide to mastering AI governance for business success”.
At OneAdvanced, we are committed to developing AI solutions that adhere to stringent AI governance frameworks. Even at the early stages of our journey in embracing AI technology, our approach is informed by best practices in ethics, transparency, and accountability. We prioritise not only the innovation AI can bring but also the trust and confidence our customers place in us to deploy AI responsibly.
12. Learn from failures
Failure is an expected part of managing AI projects. It’s important to remember that not every project will succeed, but it's the learnings from these failures that will contribute to future successes. Organisations should take time to reflect on what went wrong and why and use those insights to improve their approach in the future. Additionally, organisations can also learn from other industry leaders who have successfully managed AI projects by attending conferences, networking with peers, and staying up-to-date with the latest trends and best practices.
How are AI projects different from traditional projects?
AI projects differ from traditional projects primarily due to their unique characteristics and requirements. To effectively manage AI projects, the first and foremost thing is to understand these differences. Here are key characteristics that set AI projects apart from traditional IT projects:
Complexity
AI projects are complex and require specialised expertise from both technical and domain-specific perspective. Unlike traditional IT projects, AI projects involve working with large volumes of data, implementing advanced algorithms and techniques, and leveraging cutting-edge technologies. This makes AI projects more complex than any other traditional projects.
Evolutionary nature
As AI is an evolving technology that learns and improves over time with new data, AI projects are highly evolutionary in nature compared to conventional software projects. For instance, in the healthcare sector, AI-based diagnostic tools continually update their algorithms as new research and medical guidelines emerge. This ensures that the AI system remains accurate and up-to-date in diagnosing diseases and recommending appropriate treatments.
Data-driven approach
Another characteristic that sets AI projects apart is their data-driven approach. Unlike other software projects, where decisions may be influenced by experience or intuition, AI projects rely on rigorous data analysis to drive insights and enhance decision-making. For example, in the finance sector, AI algorithms analyse vast amounts of historical financial data to predict market trends and make investment recommendations.
Iterative
Managing AI projects requires a more adaptive project management approach, which includes regular iteration and continuous feedback to refine models based on new data and outcomes. While conventional software projects depend on agile methodologies, they typically incorporate feedback during product development.
Skill set and team composition
A diverse set of expertise, which includes machine learning experts, data engineers, and data scientists, along with software developers and testers are required to manage AI projects. Whereas, managing traditional software projects revolve around developers, testers, and UI/UX designers. AI projects demand from managers a deeper understanding of statistical models and data analysis, and effective use of project management tools to manage diverse teams. However, this is not the same with usual IT projects.
Challenges of AI project management
Like any other initiatives, AI projects also come with unique challenges that organisations must address to successfully manage AI projects. Here are those:
Lack of skills and expertise
The success of AI project management largely depends on the availability of technical skills and expertise for managing this technology. However, this doesn’t seem to be the case. In 2024, almost one in five have tried and failed to implement an AI project with 48% of these citing a lack of skills and experience to manage the project as the reason.
This shows that there is a significant demand-supply gap in terms of skilled professionals who can effectively manage AI projects. As a result, organisations must focus on identifying skills gaps and upskilling the existing workforce to reap the benefits of AI adoption.
Limited understanding of AI projects
Our Annual Trends Report highlights a key challenge contributing to the failure of AI projects: a limited understanding among business leaders about what it takes to successfully manage them. In fact, 46% of organisations that attempted and failed to implement AI admitted they didn’t fully grasp the business problems they were trying to solve with the technology. AI project managers must clearly understand the business objectives and requirements before embarking on an AI project to ensure it's a success.
Data negligence
AI project management heavily depend on data. However, 35% of companies claimed that they didn’t sufficiently review and clean up data before starting AI projects with 29% said they underestimated the amount of data expertise and resources needed for the project. With proper data management processes in place, organisations can address the challenges of poor data quality, biased outcomes from their AI systems, and manage AI projects successfully.
Cost
More than half, 51% of companies say that cost is their biggest barrier when trying to implement new technology. Implementing AI projects can be expensive due to the specialised skills required and costs associated with acquiring and managing large volumes of data. Additionally, there are costs associated with maintaining and upgrading AI systems over time. Organisations must carefully evaluate the costs involved and have a clear understanding of cost-benefit analysis before embarking on an AI project.
Interoperability
Another significant challenge in managing AI projects is the interoperability of different AI tools and technologies. In many cases, organisations use multiple AI systems from different vendors that lack compatibility, making it difficult to integrate or share data between them. This can lead to siloed data sets and fragmented insights, hindering the overall effectiveness of an organisation's AI capabilities.
How OneAdvanced can help to elevate your business?
At OneAdvanced, we understand the challenges and complexities of modern businesses. Our customer-centric approach, coupled with our expertise in cutting-edge technology solutions like artificial intelligence and cloud makes us the perfect partner to help you navigate the ever-changing business landscape. From streamlining operations to driving growth and innovation, we are committed to help you achieve your goals.
To stay ahead of the competition and gain valuable insights, we invite you to download our Annual Trends Report. This comprehensive report provides in-depth analysis and actionable recommendations to help you make informed decisions and unlock new opportunities for success.
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