Agile has come a long way since the Agile Manifesto in 2001. Originally a call to rethink software development, it is now what underpins how successful organisations adapt to change, lead with purpose and deliver value.
But now, there’s a new disruptor: Artificial Intelligence.
Just as agile methods have evolved over time, with new ideas and preferences shaping our ways of working, AI is emerging as the next enabler of meaningful, business-wide change. It’s not about automation or algorithms. It’s about how we use AI to enhance agility across the whole organisation to empower better decisions and deliver value faster, all without losing sight of the human connection.
Evolution Timeline
The evolution of agility as a concept emerged from software development and has been scaled / transposed into a broader business context.
We can also review the application of AI to agility along that same timeline - taking small steps will mean your organisation doesn't get left behind by this next stage of industry evolution.
Mindset over Method
Agile has shown us through results that flexibility is good, as is delegating appropriate responsibility. Agile has enabled the role of leadership to mature over time from autocracy towards facilitator and escalation point to support blocker removal. Phrases like "servant leadership" have emerged from the methodologies, reflecting that transition.
Recent certifications, such as PMI ACP and the Business Agility Professional Status, focus more on the spirit of agile (small "a") rather than dictating a method. The real skill is in bringing real-world experience, alongside knowledge of your organisation, and creating bespoke ways of work that are right for you.
Governance
The biggest thing missed when organisations have a top-down dictate to "go agile" or "be more agile" is the business side flexibility of outcome. Agile isn't just something that the tech team does in isolation.
One of the biggest challenges organisations face is how to balance the required responsiveness / flexibility of agility with governance, particularly in regulated industries. For instance, in Financial Services, large banks are increasingly digital-first and seek faster time to market for product changes. However, they must still navigate rigorous quality gate processes shaped by FCA, BASEL, and AML regulations. Similarly, in the pharmaceutical sector, businesses push for agility to respond swiftly to public health emergencies or pivot R&D towards promising compounds, while maintaining strict compliance with good manufacturing practices, clinical trial protocols, and drug approval regulations.
Hypothesis - This is where AI will enable a giant leap in meaningful adoption of more agile practices.
Agile Leaders
Empathy. Coaching. Navigating complexity. These are leadership traits that AI simply cannot replicate.
A big part of any leadership, servant leadership or otherwise, is the human connection. Having empathy with the challenges the team faces. Particularly if we think about human challenges that can be impediments to progress (work-life balance!), or when conflict occurs. To that end, AI will never (or at least not for the foreseeable future) replace human leadership. That is, if teams are already maximising efficiency (minimising waste) and focusing on value added. If not, then AI may create some market-driven motivation to make that step change.
How to Apply AI in Agile
Here’s how agile organisations can put AI to work today:
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Data analytics to support sprint retrospectives and sprint planning - AI can analyse trends across sprints, identify recurring blockers, and highlight patterns that would typically take hours to spot manually. This allows teams to make data-informed decisions and continuously improve without the drag of manual analysis.
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Automate meeting notes and free up mental space - Don't waste time recording minutes of meetings. Instead, get AI to do the first draft. Teams can then refine key points and action items collaboratively, speeding up alignment and reducing administrative load.
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Get AI to produce the lowest value items - Whether it’s basic coding, summarising user stories, or organising backlog items, AI can step in to handle repetitive or low-impact work. This frees up your people to focus on what really drives value.
By cutting down on time spent on manual, repetitive tasks, AI creates more space for the elements that make agile truly effective: real-time collaboration, human insight, and strategic alignment.
AI Tools to try
AI can support with:
- more administrative tasks;
- making data-based statements;
- predictive analytics based on data patterns.
AI dev tools and testing tools - not necessarily specific to agile methods, but still of benefit.
Writing user stories may be a good use case.
Champion challenger model - use two AI’s - one to author and one to quality check. Of course, that doubles the licence fee, but it may be a way of bridging the nervousness of stakeholders from the transition to AI.
Encourage teams to give it a try. There are loads of marketing out there for new AI products, some of which may not deliver the value you're expecting. Test & learn! Most of the tools have cheap/free licensing models that allow you to dip your toe in to see if it will add value to your environment.
These products are coming to market as fast as articles like this are being written – therefore, encourage you to keep your eyes and ears open for the latest tools.
A good tool to try right now is Standuply which can automate agile processes to elevate standup meetings, retrospectives, backlog refinement and planning poker directly in Teams or Slack.
Take the free Alirity AI Readiness Assessment (AAIR), to assess your organisation’s agility and AI readiness, helping you maximise opportunities, remain competitive and mitigate risks. The Alirity AI Readiness Assessment helps organisations navigate AI adoption with confidence. By evaluating six key areas — data, governance, people, customers, technology, and strategy — it provides a comprehensive analysis of your organisation’s readiness for AI. Through 46 questions, it highlights organisational strengths, gaps, and strategic priorities to support responsible, sustainable and confident AI deployment and decision-making that aligns with the organisation’s broader objectives.
But also consider tools already available in your business that are underutilised - MS Copilot can do a decent job of some of the suggested tasks outlined earlier in this article.
Visibility and Responsiveness
You might look at business agility as the coming together of visibility and responsiveness. Visibility of new or emerging industry trends, customer needs, or bottlenecks in your business. Responsiveness represents how quickly your business can mobilise and deliver change as a result.
When we talk about being responsive, we have to ask, what will this change mean for project X, person X, or goal X?
AI is already helping with this by using predictive analytics. It looks at large amounts of current and past data to help make better decisions about what might happen next. But using AI across the business brings some challenges.
Take the example of changing project priorities to launch something new. To understand the impact of that change, AI needs data such as timelines, cost estimates, and team availability. This data often lives in portfolio management tools, which are managed by the PMO (Project Management Office). These teams are responsible for making sure projects are properly planned and tracked.
However, these processes can feel heavy and slow. They can sometimes work against business agility, especially when they involve too many steps and too much management time.
That’s why the real opportunity for AI lies in the early stages, gathering data like estimates and timelines more easily and accurately. Done well, this helps leaders respond faster and with more confidence.
Start with the most boring part of your day
Maybe the best way to approach how your business can adopt AI is to take a very human-centric approach - ask your team what the most boring, dullest tasks are, and how can we use AI to automate those and move individuals’ contributions up in productivity/value and spend less time on busywork.
Final thoughts
One should expect an increase in motivation and engagement from the removal of the worst parts of a job, and as a result, and somewhat ironically, Artificial Intelligence could have a huge benefit to very human measures like engagement and job satisfaction!