Artificial Intelligence is sweeping across industries, and project management is no exception. AI-powered tools are revolutionizing traditional project delivery practices, shifting them to more data-driven and future-focused approaches. At the same time, these innovations are empowering teams, enabling individuals to achieve greater success and efficiency in their roles.
After the gradual rise, and later full adoption, of agile delivery methodologies in the past 2 decades, we are once again witnessing excitement for change in project management and delivery space with the adoption of AI capabilities.  
People across levels and roles in a typical project delivery team are embracing the benefits of AI – from software developers using AI assistants for code development to Managers organizing and tracking tasks in a more “intelligent” fashion – proving AI is truly for everyone.
A typical project delivery lifecycle consists of 6 key stages:

Let’s dive into each of these to understand how AI is transforming these stages by enhancing decision-making, improving efficiency, and reducing risk.

Envision:  

Over the past few years, especially due to COVID-19, we’ve become accustomed to using various collaboration tools like Miro, Mural, and others to brainstorm and whiteboard with large groups of virtual participants.

But what comes next is leveraging Conversational AIs to be active participants in those brainstorming sessions, contributing insights from the conversation and providing different points of view.

From a product perspective, prompt engineering can be used to create different AI "personas" to elicit a series of responses representing different types of target audiences for your product or solution.

Analyze:  

Business Analysis may very well be the area in the delivery lifecycle that will be most impacted by the introduction of AI. While there are AI tools available today to listen and summarize discussions in requirements gathering sessions, it may over time allow product owners or analysts to simply define their requirements by speaking to an AI system which will take the raw inputs and convert them into structured requirements or user stories. Therefore, unburdening analysts from playing the role of scribe and allowing them to augment their role and focus on research and analysis needed to tie all the requirements together and look at the big picture.

Design:  

A massive way AI contributes to the architects and solution designers is by optimally generating system architecture suggestions based on requirements, constraints, and historical data. Sophisticated machine learning algorithms can analyze past design patterns and recommend better approaches or predict potential flaws in the architecture or system flow before implementation.

For architects and solution designers, tools such as PlantUML, ChatUML, Diagramming AI can be used by itself or as a combination with GPT, Claude or Mistral to generate the complex diagrams.

For UI designers, the popular design tool Figma now offers Figma AI (beta) which allows its users to quickly create design mock-ups, rewrite text name layers and even turn static mocks into interactive prototypes. Figma is not the only option here, AI is making space for new entrants such as Uizard and their Autodesigner solution. This combined with image generation, is sure to boost both designing user interface, and creating the content for it.

Build & Test:

The development stage is likely the area where AI has already made the most significant impact. AI-driven tools like GitHub Copilot and Kite assist developers by generating code snippets, offering suggestions, or completing code, increasing speed and accuracy.

Add the AI-powered test automation, and you have a full suite of tools to create test strategy, generate scripts, run tests, adjust cases as the codebase evolves and even identify/detect bugs.

One capability that shouldn't be overlooked is the ability for these AI systems to convert code from one mainstream programing language to another. For many large organisations who are trying to modernize their legacy systems e.g. Cobol applications running on mainframe, or C solutions on Unix systems, this could prove to be the solution that proves to be efficient and cost effective.

Deploy & Maintain:

AI has empowered teams globally to optimize Continuous Integration/Continuous Deployment (CI/CD) pipelines by automatically identifying the best time to deploy and reducing downtime.

With regular, accurate and relevant input of release-related data, it is now possible to assess the stability of the system after each release predicting the likelihood of issues in production, and suggesting rollbacks or hotfixes as needed.

Monitor & Manage:

Project managers are heavily leveraging smart monitoring and administration techniques to stay on top of the game. For example, Conversational AI tools can now assist with a plethora of management tasks, including:  

• Smart/dynamic resource allocation
• Drafting stakeholder communications
• Turning project briefs into project plans
• Intelligent task prioritization and breakdowns
• Brainstorming risks & options for budget management
• Writing project documentation
• Researching project background
• Progress tracking

It is evident that AI is unlocking new ways of working for most organizations across the globe. It certainly has the potential to boost productivity across the delivery lifecycle, cutting down not only costs but also manual effort and allowing delivery teams to focus more on delivering customer-centric solutions.

However, this powerful tool does not come without its challenges and pitfalls:

• Understanding that the AI tool is only as good as the data you feed into it to train the algorithms, it is critical to ensure that high-quality, complete and unbiased data is made available for the AI systems to make accurate predictions or generate desired outcomes.
• While AI can save time and reduce errors, the upfront costs of implementing AI tools, especially at a large scale for companies with a lot of technical debt, can be particularly high.
• AI tools adhere to company policies. Many organizations have concerns about sharing their intellectual property with AI providers without guarantees regarding its protection or usage. Additionally, there is a real risk of unintentionally infringing on others' intellectual property.
• Lastly, AI is ‘Artificial’ Intelligence – therefore, embrace the “trust, but verify” approach. Acknowledge that AI will still need human expertise and supervision to understand its limitations and interpret its results accurately.

As we look to the future, AI's role in the project delivery lifecycle will continue to evolve, becoming an indispensable ally in driving efficiency, reducing risks, and enhancing decision-making.

From automating repetitive tasks to providing predictive insights that help mitigate risks before they escalate, AI is transforming the way projects are planned, executed, and delivered.

By embracing these intelligent tools, organizations can unlock new levels of agility, scalability, and innovation, ultimately delivering projects on time, within budget, and with greater success.

As AI technology continues to advance, its potential to revolutionize project management and delivery becomes even more profound, offering a competitive edge to those who harness it effectively.