Why Is Your Agile Program Still Slow? Many Agile Project Managers struggle to answer this question. Each of your teams works in Sprints. They have Product Owners, Scrum Masters, backlogs, Sprint Reviews, and Retrospectives. Each team meets their Definition of Done each Sprint. Velocity looks reasonably stable.

Yet, when you look at the larger program, something doesn’t add up.

Features still take months to reach customers. Releases are often delayed. Teams frequently wait for each other. Integration becomes a last-minute struggle. A feature that one team completed two Sprints ago is still not in production. I call this a classic “Watermelon Symptom.” Like a watermelon, things look bright and green on the outside. But when you cut open the watermelon (or the project!).  It’s all bright, scary red!!

The individual teams seem Agile. So why is the overall program still slow?

This is a challenge I frequently see in large Agile Programs. We often focus our efforts heavily on making individual teams Agile, while the bigger constraint sits between the teams.

Agile Teams Don’t Automatically Create an effective Agile Program

Imagine an e-commerce company developing a new one-click checkout capability. The Checkout Team finishes the customer interface in a Sprint. But it needs a new API from the Payment Team. The Payment Team, in turn, is waiting for security approval. Once that is complete, everyone needs an integration environment that is currently being used by another release.

Every team may be working efficiently. Each team may be following the Agile Mindset and Scrum Framework. But the customer is still waiting. This is an important mindset shift for Agile Project Managers: local efficiency is not enough to  create end-to-end flow. The real question is not simply, “How quickly is each team completing its work?” It is: “How quickly can we deliver value across the entire system?”

How Can You Improve End-to-End Flow Across Agile Teams?

One common cause of slow program delivery is the way teams are structured. Siloed teams with specialized work type (think a testing team or an API team ) is one of the key causes of delivery Issues in Agile Programs. As per The 17th State of Agile Report 37% of teams dissatisfied with Agile say that siloed teams causing delays in deliverables is  a key reason.

Consider a feature that requires work on the UI, APIs as well as the database. Often the orgs structure their teams around specialized work. This means they will have an independent UI team, database team, testing team and deployment team.

Then in this case usally the work moves like :

UI → API → Database → Testing → Deployment

Each team will be highly skilled and individually productive. But every handoff adds overhead and delay….

The API team may not have capacity when the UI team finishes.  The database team will have different priorities when the API team completes their work. Testing may be committed to another release. Deployment may happen only during specific windows.

This is why large Agile programs need to think carefully about team design, not just Scrum adoption at the individual team level.

Usually, practical, cross-functional Feature Teams that can deliver end-to-end customer value reduce handoff overhead and dependencies. Component, platform and specialist teams will often still be necessary—but smart organizations create those deliberately and thoughtfully rather than simply reflecting existing organizational silos

Why Shared Services Often Become Hidden Bottlenecks

Large programs often need specialist capabilities such as architecture, security, UX, compliance, data science, or infrastructure. These are costly skills. Also, we often do not need them all the time.

Creating shared teams for these skills usually appears efficient in theory.

But imagine ten Agile teams depending on the same security team.

Each delivery team works in two-week Sprints. But the security team works via a centralized queue. Suddenly, supposedly Agile teams are waiting three weeks for a review.

The individual teams are not necessarily the problem. The constraint usually appears at the system level.

Agile Project Managers therefore need to look beyond whether teams are completing their Sprint Backlogs. They need to ask where work queues up, where teams repeatedly wait, and which shared capabilities often become bottlenecks.

How your technology practices often increase Dependencies in your Agile Program?

Organizational structure is just one factor that exacerbates dependencies.  Technology constraints are another major issue.

A team often ends up waiting for another system, API, environment or dataset before they can complete their work.

For example, the Checkout Team is waiting  to test a new digital-wallet option because the Payment API is not ready. An AI team can’t validate its recommendation engine because historical customer data hasn’t been migrated yet.

Simply putting these dependencies on a program board makes them visible. But it does not make them disappear.

Sometimes the better solution is a change in technical approach: API-first development, mock services, automated environment provisioning, reusable components, or a more modular architecture are some of the technological approaches that help withto reduce dependencies.

As an Agile Project Manager, you need to empower your large Agile Projects/ Programs with right technology enablers

How the Metrics you track impact your large program delivery

Another reason slow programs can appear healthy is that we may be looking at the wrong metrics.

Suppose every team’s velocity is stable. Sprint burndowns look good and most teams regularly achieve their Sprint Goals.

Management sees green dashboards.

Yet a customer feature still takes twelve weeks to travel across the organization. – A classic watermelon!

Team-level metrics tell us useful things about individual teams, but they may not expose the delays or bottlenecks between teams.

At the program level, we need different questions (which means different metrics!):

• How long does it take for an idea to reach production?
• Where are the bottlenecks?
• Are queues growing between teams?
• How predictable are our releases?
• Which dependencies repeatedly delay delivery?

Measures such as lead time, Cumulative Flow Diagrams, and release predictability provide a view of program health.

The objective is not to create more metrics. It is to measure what helps us understand end-to-end delivery.

How can you reduce dependencies in your Agile Program?

When programs struggle with dependencies, the natural response is often to improve dependency management.

We create dependency logs, program boards, coordination meetings and design escalation mechanisms.

These are useful. Dependencies must be visible and actively managed.

But there is a more powerful question: Can we remove  this dependency altogether?

Could we create a more cross-functional team?

Could a commonly required capability become a reusable service?

Could we split a large feature into smaller, more independent increments?

Could teams use mock APIs instead of waiting for another team?

Could architecture become more modular?

Could an approval or environment provisioning process be automated?

Good Agile program management doesn’t only coordinate dependencies better. It continuously looks for opportunities to reduce the number of dependencies the organization creates in the first place.

Snehamayee’s View

What Does This Mean for the Agile Project Manager?

This is where the role of the Project Manager changes significantly in an Agile environment.

The Agile PM should not become a person who simply collects status from multiple Scrum teams and combines it into a program report.

Their perspective needs to be broader.

They need to see patterns that individual teams may not see: recurring dependencies, organizational bottlenecks, shared-service constraints, integration risks, vendor delays and program-level flow.

They facilitate conversations across teams, make dependencies visible, escalate systemic impediments and help teams and leaders find ways to simplify the delivery system.

In other words, the Agile PM increasingly moves from managing individual activities to enabling the overall system to deliver.

Why Should You Stop Asking Whether Your Teams Are Agile?

Having ten Agile teams does not automatically give you an Agile program.

You can have excellent Scrum Masters, empowered Product Owners and teams that consistently complete their Sprint Goals—and still have a slow delivery system.

Customers really don’t care how quickly each individual team completes its work.

They want to optimize the time it takes for the whole organization to turn an idea into a usable outcome.

So usually the most useful question for leaders and Agile Project Managers isn’t: “Are our teams Agile?” It instead is:

“How easily does value flow across our teams and reach the customer?”

That is where program agility really begins.

The post Why Is Your Agile Program Still Slow? appeared first on World Of Agile.

In this article, I will address What changes when you AI empower a Product.

When an Agile Professional thinks about AI, it is often as a “productivity enhancer”. We look at AI as something that can reduce our effort. AI indeed does that job. But more importantly, AI is rapidly changing  the products we use every day. For example, search engines summarize answers instead of simply displaying links. Business applications recommend actions instead of just presenting dashboards. Streaming platforms recommend what show you can watch next. Development tools generate code.  And now in addition, AI agents are beginning to execute tasks on our behalf.

It is tempting to look at this as simply another technology shift—similar to moving applications to the cloud or making them mobile-enabled.

But I believe the change is more fundamental.

When we add AI to a product, we don’t just change the technology behind the product. We often need to change entire product behavior

Traditional software was largely designed to follow predefined instructions. AI-empowered products can interpret, generate, recommend, reason, and, increasingly with Agentic AI coming in picture, these products can act.

As Agile Professionals or even Product Specialists, we need to understand how AI-empowered products transition from traditional data crunching and display to understanding impact. Read on the article to understand this shift better

From Executing Commands to Understanding Intent

Traditional software expects users to understand how the application works. The user performs action, and the Product or Software responds

Imagine booking a holiday using a conventional travel application. (Think MakeMyTrip or something similar) You, as the user, have to select a destination, enter dates, apply filters for price and hotel category, compare options, and gradually build your itinerary.

The application is very good at executing the instructions you provide. But it does not customize the itinerary for you.

Now imagine telling an AI-empowered travel application:

“We have five days in Sri Lanka. We enjoy wildlife and history, don’t want long drives and would like two days near a good beach. Suggest an itinerary.”

The user has expressed an intent, not provided a sequence of commands.

The AI needs to interpret that intent, understand the constraints, consider alternatives and generate an appropriate response.

This represents an important shift:

Traditional products ask: “What does the user want to do? / what steps will help the user”

AI-empowered products can increasingly ask: “What is the user trying to achieve?”

With this shift from user actions to user intention, we now need to rethink entire customer journeys rather than simply adding AI to existing screens.

From Deterministic to Probabilistic Behavior

This is another fundamental difference.

Traditional software is predominantly deterministic.

If a banking system is programmed to calculate interest using a particular formula, we expect the same inputs to produce the same output every time. Even if we make a mistake in the formula, the same wrong answer will be displayed every time

We can broadly describe this as:

Input → Rules → Expected Output

Generative AI does not always behave this way.

Ask an AI system to summarize a customer complaint twice, and you may receive two slightly different summaries. Both could be perfectly acceptable.

Ask it to recommend the next best action for a salesperson, and there may be several reasonable answers.

And occasionally, AI hallucinates. This means that the AI may produce an answer that sounds convincing but is simply wrong.

AI product behavior therefore looks more like:

Input + Context + Model → Probable Output

This has a huge shift in product behavior.

For many AI capabilities, we cannot simply ask:

“Did the system produce the correct answer?”

We also need to ask:

“How good was the answer?” (Was the itinerary suitable ?/Was the new show recommended by the streaming service relatable? / Did the AI-based customer agent really help the complainant)

This shift will eventually impact the entire product life cycle. It will change

• How we write requirements. – Now we will have to write requirements that capture how do we know if user intent was met?
• How we test the product.   –
• How we measure quality
• How we decide what is an acceptable range of correctness?
• Last but not least, how do we decide our feature is ready for release?

AI empowered Intent driven products will not be merely right or wrong – we need to find a way to capture if the user intent was fulfilled

From Fixed Expectations to Context-Aware Experiences

For decades, we have designed digital products with fixed expectations. You navigate a certain menu, click a certain button – each time same action happens. This is a fixed expected behavior. We discussed above that Intent makes a key shift from expected behaviors for AI – But Context is an equally important factor to consider

Imagine a project manager opening a portfolio dashboard. With a traditional, fixed-expectations product, she expects to scan each of his 20 dashboards daily. Maybe she even has an alert built in to highlight dashboards with “red” items. But in the end, that alert is based on a fixed set of rules

But when working with an AI-empowered, intent-driven dashboard, instead of examining twenty charts, the manager might ask:

“What has changed since last week, and which three things need my attention?”

For the product to answer well, however, it needs context.

What projects does this person manage? What happened last week? Which risks were previously discussed? What does this organization consider critical? What information is this person permitted to access?

We can now see that context is becoming an increasingly important part of product design.

The quality of an AI experience may depend not only on the model being used but also on what the product Context – What the Product knows, remembers and retrieves at the right moment.

From Providing Features to Providing Intelligent Capabilities

Consider a traditional sales application.

It may provide customer records, reports, dashboards, filters and alerts. These features help a salesperson understand what is happening. And using the information, the salesperson then makes a decision about which customers are likely to turn to a competitor product this month

Instead, an AI-empowered version of the sales application could possibly answer:

“Which of my customers are likely to turn to a competitor product this month and why? What can I do to reduce the impact?

The product is no longer simply retrieving or representing information.

It analyzes information, identifies patterns /trends and/or anomalies , and then recommends possible action.

We can see similar changes across industries.

A traditional HR product stores employee information. An AI capability may help managers identify recurring themes in employee feedback.

A project management product tracks tasks and dependencies. An AI capability may identify emerging delivery risks and suggest possible interventions.

A customer-support product displays previous interactions. An AI capability can summarize the customer’s history and recommend a response.

The conversation when we think about AI products moves beyond: “What features should our product provide?”

To Goes towards “What intelligent capabilities should our product have?”

From Assistance to Increasing Autonomy

AI products are also changing in another significant way: they can increasingly act.

Imagine an expense-management product. If we want to limit AI use to analytics, AI could identify unusual expenses. If we want to go ahead and provide recommendation abilities, AI could explain why an expense appears unusual. It could then recommend an action.

But an AI agent takes this even further. It could potentially investigate supporting documents, contact the employee for missing information, update the case and route it for approval.

The products are now going from providing intelligence to taking coordinated action toward an objective.

The difference between an AI that says “Here is what I recommend” and an AI that says “I have done it” is often the differentiator.

As products move along this spectrum, autonomy itself becomes a product-design choice. We need to decide what action an AI agent should be allowed to take and where we need to insist on human intervention. For example, it may be OK to have AI approve a standard insurance claim, but you may want AI to flag any unusual claims to human or maybe you want all rejections to be vetted by a human.  Going further you may want AI to suggest a possible diagnosis to a physician but never to a lay person.

The decisions on where AI acts and when it recommends… Or even what it can recommend what to whom are now the Product Design decisions

Products Can Now Fail in New Ways

Perhaps the most important shift in behavior appears when AI fails. Traditional software certainly fails. Applications crash, integrations break and calculations contain defects.

But AI introduces different kinds of failure. An AI system may misunderstand a perfectly valid request. It may generate information that doesn’t exist. (Hallucinations!).It may use outdated or inappropriate context. It may make a poor recommendation while sounding extremely confident. An autonomous agent may correctly execute an action that it should never have been allowed to take.

This creates an unusual situation: The software may be functioning exactly as designed, while the product is still producing an unacceptable outcome. Data – availability /correctness and retrieval will now impact a lot of outcomes . With data, we now have to worry about the data security / ethics

Concepts such as trust, uncertainty, boundaries, human oversight and acceptable failure increasingly important product considerations.

Snehamayee’s Perspective – Empowering your product with AI is more than adding an AI patch

The most common way people try to adopt AI is to see how it can make current work more productive. How can AI automate my MoM? Can we use AI write user stories / acceptance criteria or even code for me? How can AI write test cases and analyze test coverage?

A few people go ahead and try to incorporate AI in their products- what they do is to take an existing product and ask: “Where can we add AI?”

People may add a chatbot to their Product. Sometimes they add data summarization or trend analytics. They may even add recommendations. These certainly add value to the product.

But the more interesting question is:“If this product had access to intelligence from the beginning, would we design the product differently?”

Would users still navigate through ten screens? /Would they still analyze the dashboard themselves? /Would they still manually coordinate five different activities?

Or could the product understand their objective, assemble the necessary context, recommend an approach and—with appropriate safeguards—take some of those actions for them?

That is where AI stops being merely another feature and starts changing the product.

And this leads to an important question for those of us working in Agile product development.

If our products are becoming probabilistic, context-aware, adaptive and increasingly autonomous, can we continue building them exactly as we built deterministic software?

• Do our requirements need to change?
• What does “Done” mean?
• How should we test something when there isn’t always one correct answer?
• What new risks should teams consider?

And what does all of this mean for Product Owners, Scrum Masters and Agile teams?

Those are the questions I will explore next. while I do that please refer to this document for the design guideline when we build AI empowered Products

Because when the nature of the product changes, the way we build the product must evolve too.

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