In many organizations, what started as a simple scrum process became overly complex due to agile frameworks that sought to “scale” agile.

Several years ago, I embarked on a journey to convince Digital Trailblazers to define their agile ways of working. Now, driven by AI and with urgency, agile leaders need to revisit their agile practices and digital operating models.

For clarification, when I say the “AI Era,” I am referring to a broad set of global, economic, cultural, and technological structural changes occurring in parallel with AI’s reshaping of business. The bottom line is that business expectations have changed drastically since 3-5 years ago (the pandemic era) and even more since 5-10 years ago (the CX, data, and cloud era).

So if your agile hasn’t evolved, and more broadly, if your organization’s DevOps and delivery model doesn’t reflect today’s realities, chances are you have opportunities for improvement.

Why revisit Agile in your organization?

Review my seven urgent reasons to revisit your agile practices for the AI era. Even if you only check two of them, it’s time to move past last gen’s model.

Critical problems

1. Frustrated stakeholders – Even before AI skyrocketed stakeholder expectations, many organizations were already suffering from a broken product management model. When product owners are funnels for everyone’s wishes, then the stress propagates to agile teams, and product owners underdeliver on targeted business outcomes.

Critical problem: Product managers and owners aren’t empowered to do their jobs.

2. Deployment, but little adoption – DevOps is delivering features and even hitting its deadlines, but end-user adoption of the new capabilities is lagging. A related issue is that when adoption occurs, insufficient feedback does not make its way back to agile teams. Putting these two issues together leads to underperformance and delayed business outcomes.

Critical problem: Change management is treated as a separate process, outside the responsibilities of agile DevOps teams.

Important problems

3. DevOps lacks business benefits – It’s been over 15 years since IT organizations adopted DevOps practices and tried to address the cultural gaps between dev and ops objectives. I count over 40 DevOps best practices, including ten that few organizations prioritize. But many organizations are experiencing DevOps fatigue. Business stakeholders are frustrated by ongoing efforts to address technical debt, implement CI/CD, establish infrastructure-as-code, or improve observability that never achieve a high degree of automation or operational resiliency.  

Important problem: DevOps fatigue escalates when agile teams fail to set expectations,  prioritize practices only through a technology lens, and don’t communicate the business benefits of their efforts.

4. Agile chaos – Should agile teams working in a low-code platform follow the same agile rituals as those developing in pro-code? Should teams experimenting with AI and developing POCs be exempt from following agile standards? Should ERP upgrades and other large-scale initiatives fall back to waterfall methodologies because these projects have more complexities, risks, and interteam dependencies?

Important problem: In many organizations, rigid standards for scaling agile break down due to program-specific factors, leaving the organization with an every-team-for-themselves approach to agile.

5. Too many roles, too many meetings – Scrum teams start with product owners, team leads, and scrum masters when beneficial. Some agile frameworks add on release train engineers, business owners, and epic owners to scale agile, while other organizations add program managers and project managers. The added roles often lead to more meetings, which reduce collaboration and perpetuate slow decision-making.

Important problem: Technology efficiencies from low-code, DevOps, and AI, coupled with better agile management tools (Jira, Azure DevOps, ServiceNow), should yield a more efficient agile operating model.

Emerging problems

6. Inconsistent results working with partners – Different initiative types, team structures, and agile practices yield mixed results. When leaders struggle to deliver results from their initiative, they blame the partner. Outsourcing mentality prevails. Metrics can’t be used for decision-making because of inconsistent practices.

Emergiing problem: The operating model doesn’t include specifics on co-creating with partners, and self-organizing standards aren’t shared with them.

7. AI productivity gains aren’t business-impacting – According to one report, DevOps teams using code generators see 40%+ of code written by AI. AI is helping write requirements, improve QA, and maintain documentation. But leaders struggle to translate productivity improvements into their business impacts.

Emerging problem: AI experiments and tooling need leadership direction around goals and a clear approach to capturing metrics.

Digital Trailblazers are evolving their Agile Wow

One more reason to evolve your organization’s Agile Way of Working is that what we’re developing is also changing. Apps and APIs are now low-code integrations, data pipelines, small language models,  and AI agents. Multidisciplinary teams often require business, compliance, data scientists, and AI governance team members.

Agile teams are not just delivering user stories; they’re agile planning and driving change management.

But this time around, revisiting agile is a greater challenge. Many organizations have to drive the change without agile coaches.

Questions? Challenges? Work with me to seize the real advantages.