How Not to Crash and Burn an AI Project

Artificial Intelligence (AI) has the potential to transform industries, but the journey from idea to implementation can be challenging. Despite the hype around AI, many projects will struggle to be successful in the near term. However, AI projects are not easy to execute and often fail to deliver the expected value. According to the Standish Group's Annual CHAOS 2020 report, 66% of technology projects (based on the analysis of 50,000 projects globally) end in partial or total failure.?

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AI projects are different from traditional software projects in many ways. They require a different approach to project management that takes into account the unique requirements of AI projects.?They face many challenges, which are walked through in another article , however, there are ways to improve the success rate of AI projects.?Here, we review some high level ways to avoid project failure.

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Be Clear Before You Start

One of the most important factors that contribute to AI project success is the clear definition of business goals and objectives. Organizations should have a deep understanding of the problem they are trying to solve and how AI can help solve it.?It should also specify the expected outcomes, metrics, and success criteria for the project.?Having a clear problem definition and business objectives can help teams to stay focused and avoid scope creep.

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Have the Right People and Education Everyone, I Mean EVERYONE

AI projects require a diverse set of skills. It is essential to have a team with the right mix of technical and domain expertise. Teams should include data scientists, machine learning engineers, software developers, domain experts, and business analysts - and don't forget your QA.?So often projects fail simply because they didn't test enough. Project leadership should ensure that the team has the necessary skills and experience to deliver the project. Moreover, project managers should also educate the stakeholders on the basics of AI, its benefits and limitations, and how it can impact their business processes and decisions.?Educated stakeholders from various departments can provide valuable insights into the project requirements and goals. This is top to bottom - management to business owners.?Educated stakeholders can also help in better collaboration and communication, leading to better decision-making.

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One of the key factors that can determine the success of any project is having the right people leading it. Project leaders are responsible for defining the vision, scope, objectives, and deliverables of the project, as well as managing the resources, risks, and stakeholders involved. If the project leaders are not qualified, experienced, or skilled enough to perform these tasks effectively, the project may suffer from various problems and challenges that could jeopardize its outcome.??Especially for AI projects, effective leadership requires a combination of technical expertise, management skills, and leadership abilities. It is important to have someone in charge who can navigate the complexities of the project and keep everyone aligned towards the common goal. In short, having the right project leadership in place can mean the difference between success and failure.

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Collaboration, Communication and Culture

AI projects require close collaboration and communication between the technical and non-technical stakeholders, such as data scientists, engineers, business analysts, domain experts, users, and customers. It is important to have clear communication channels to avoid misunderstandings and ensure alignment. Regular meetings with all stakeholders involved in the project can help to foster better collaboration and keep everyone informed about the project status.?

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AI projects often involve experimentation and exploration of new technologies and methods. Organizations should encourage a culture of continuous learning and experimentation to foster innovation and creativity. They should also be willing to pivot or change direction if the project is not delivering the expected value.

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Not just within a project itself, AI projects can have significant cultural implications for organizations, especially if they automate or replace human roles. Organizational leadership should involve HR departments and change management specialists to address potential resistance, fears, and anxieties among employees. They should also consider developing new training programs to help employees acquire the skills needed to work with AI systems.

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Manage Projects with Processes that Address Unique AI Requirements

AI projects have unique requirements compared to traditional software development projects. AI projects require robust processes that address specific challenges, such as data preparation, algorithm selection, and model validation. Organizations should implement processes that ensure quality data, rigorous testing, and effective deployment. If their current processes do not meet these standards, they must change to support the unique requirements to support AI.

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Quality Data and Data Governance

Data is the fuel for AI projects, but it is often messy, incomplete, inconsistent, or biased. Project leadership should ensure that the data is properly collected, cleaned, labeled, annotated, and stored in a secure and compliant way. It is essential to ensure that data is of high quality, relevant, and representative for success, otherwise bias or data drift can be introduced into the system.?Data governance policies and procedures should also be established to ensure accuracy, privacy, security, and ethical use.

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Continual Focus on Areas that are Uniquely High Risk in AI

AI projects can present unique social and ethical implications, such as bias, discrimination, privacy breaches, security threats, or legal liabilities.?Organizations should focus not just on taking a one time approach, but set policy on these areas.??It is crucial to establish ethical guidelines and ensure that algorithms are transparent, explainable, and accountable.?Projects should ensure that the AI systems are designed and developed with these considerations in mind. They should also monitor and evaluate the performance and impact of the AI systems on an ongoing basis and address any issues or concerns that may arise.?Policies and regulations continually change; therefore AI systems must reflect changes in the world around them.

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Build Scalable and Maintainable Systems

AI projects are not one-time events but rather continuous processes that require constant monitoring, testing, updating, and improvement.?Teams should aim to build scalable and maintainable systems that can adapt to changing requirements. Building such systems requires careful planning and design, including choosing the right architectures, frameworks, and tools.?Concepts such as modular design, automation, and continuous integration and deployment are not new, but these best practices become key for mitigating risk in AI systems.?Project leadership should ensure that the AI systems are scalable and maintainable to handle changing data sources, user needs, business requirements, or environmental factors.

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AI projects face significant challenges, but there are ways to prevent failure.?By focusing on the human, technical, and ethical aspects of AI, organizations can develop more sustainable and responsible AI solutions that deliver value to their stakeholders. Following best practices and processes, organizations can increase the chances of success for their AI projects. By applying a different mindset and approach to project management for AI projects, these challenges can be overcome and deliver value.

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