SAFe Archives

AI Project Management vs Traditional Methods

Posted on March 16, 2026

Digital transformation continues to reshape how businesses operate, and AI is one of several powerful forces driving that change. From logistics to staffing to operations to product development, there’s practically nothing that isn’t already enhanced by AI solutions, machine learning, and automation.

Even project management, which seems to be inextricably linked to human leadership skills, is leaning more and more on the power of AI to speed up timelines, project success rates, and better control project outcomes.

But is artificial intelligence automatically better than traditional methodologies in project management? Let’s compare.

How the Landscape of Project Management Is Evolving

At the risk of sounding trite, project management is changing rapidly in the wake of digital transformation, and in response to technologies that businesses have adopted and everyone now expects. Even as early as 2019, Gartner was predicting that 80% of project management tasks would be taken over by AI by 2030.

From rigid, predictive techniques like Gantt charts, project management has become more flexible and iterative, thanks in large part to Agile, as well as other modern tools.

This evolution in project management has enhanced planning, budgeting, stakeholder management, risk mitigation, and communication, but it also means that managers need to get used to artificial intelligence in their business environment. lobal GDP, highlighting that disengaged teams are not just a culture issue—they are a bottom-line liability.

The Difference Between AI and Traditional Project Management At a Glance

Steps	Traditional Project Management	AI Project Management
Planning	Fixed upfront, sequential (e.g., Waterfall)	Dynamic, data-driven, with real-time adjustments
Decision-Making	Manual human judgment, often delayed	Predictive analytics for proactive choices
Resource Allocation	Static assignments based on estimates	Automated optimization via real-time data
Risk Management	Pre-identified with contingency plans	Continuous forecasting and early detection
Adaptabillity	Low; resists changes to avoid scope creep	High embraces iterations and feedback

What We Mean When We Say Traditional Methods

Traditional project management doesn’t necessarily mean anything that happened before the advent of AI. In fact, traditional project management originally meant methods that predate the Agile Manifesto and the subsequent development of iterative approaches.

Pre-Agile Era

Before Agile, there were several project management methods:

The Waterfall method is what people usually mean when they’re referring to traditional project management. Categorized as linear, rigid, and sequential, the Waterfall method is broken down into five stages: Initiation, Planning, Execution, Monitoring, and Closure.
While not its own method, Gantt charts are used by PMs to outline the entire scope of a project and help teams better understand the process. Gantt charts are useful in traditional methods because they facilitate project monitoring.
CPPM or critical chain project management (also known as the critical path method) helps PMs keep track of essential resources while they prioritize dependent tasks for maximum efficiency. CCPM is a good strategy to keep an eye on resources so that each task in the critical path has what it needs to reach completion.
The PERT (program evaluation and review techniques) method is more focused on timeline analysis. Using this method, the PM calculates the minimum amount of time each individual task needs, and then uses that to determine how long a project will take before dividing up resources.

Pros of Traditional Methods

Mapping out project plans ahead of time creates clear expectations and makes it easy to estimate costs, workloads, and resources.
Helps everyone on the project clearly understand their responsibilities.
Gives PMs abillity to foresee and therefore mitigate potential risks.
Helps PMs maintain more control of any changes and makes them solely accountable.
Processes and standards are well documented, which helps inform management of other projects.

Cons of Traditional Methods

Lack of flexibility can lead to increased costs and delays.
Non-collaborative.
Limited customer or user feedback due to the linear nature of the process.

Post-Agile Era

These days, the Agile method is the most popular approach to project management because it is iterative, collaborative, flexible—in other words, agile. There are 12 basic principles of Agile, all of which enforce customer satisfaction, adaptability, and cooperation. Common Agile methods and frameworks include:

Kanban is a visual workflow tool designed to help teams limit multitasking by ensuring they only have a certain number of ‘in progress’ tasks at a time. Everyone sees the scope of work, which helps teams better manage workflows and see the “big picture”.
The Scrum framework is an iterative approach to tackling complex tasks that requires the adoption of certain roles (i.e. Scrum master, developer, and product owner) and certain Events (e.g., sprint planning, etc.) to deliver usable product increments frequently.
The Extreme Programming (XP) method is designed to help teams deliver high-quality software products through ongoing customer feedback and short development cycles. This is achieved through tactics like pair programming, frequent communication, and simple design.

Whether your management style is based more on traditional or Agile methods, AI project management tools primarily enhance Agile methods but also increasingly support hybrid approaches, as we will see.e Framework for establishing cross-functional teams, clarifying roles, and fostering the relentless improvement that drives business agility.

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What We Mean When We Say AI Project Management

You’d be hard-pressed to find an organization that doesn’t already have an AI system somehow baked into daily project delivery. When we say AI for project management, we’re not just talking about automating task assignments, though automation is certainly a part of it. Rather, we’re talking about a major shift in intelligence, prediction, and optimization.

AI PM tools use machine learning to enhance business decision-making and efficiency. These tools excel at:

Analyzing historical data for risk prediction (e.g., delays or overruns).
Auto-generating schedules and reports.
Dynamically allocating resources.
Prioritizing tasks.
Integrating with Gantt charts and automatically adjusting task dependencies.

What Could an AI PM Workflow Look Like?

Imagine the human project manager types a natural-language prompt into the AI tool. It could be something like, “Help me make a 10-week plan to launch a new feature, with design, build, test, and launch phases for a team of 6.”

The AI tool will then…

Generate a timeline and break it down into tasks with their dependencies.
Assign tasks to owners and set milestones.

The human PM can then quickly review the project data and tweak any details necessary. As the project tasks are being carried out, all team updates and activities will automatically trigger the AI to auto-update statuses, flag risks like potential delays with confidence scores, and suggest fixes to help teams stay on track and meet their targets. In scaled environments, AI monitors Agile Release Train (ART) work to detect inter-team dependencies and proactively predict bottlenecks.

In the meantime, the AI tool can draft concise status reports for stakeholders and personalized reminders for individuals to minimize the need for manual communication. If a disruption arises, such as a designer going on sick leave, the project manager can ask the AI tool to help simulate their options.

When the project reaches its end, the AI tool can compile a full summary report, including details like:

On-time deliverables.
Delays and slippages, and what caused them.
Resource patterns (e.g., testing is often underestimated by 25%) and risk outcomes.

Benefits of Using AI in Project Management

When combined with human expertise, automation and ML can introduce the advantage of speed, accuracy, and productivity, and act as intelligent forecasters to help PMs make better, more cost-effective decisions. In a nutshell:

Optimized planning and scheduling.
Data-backed decision-making.
Enhanced risk assessment and mitigation.
Better resource optimization.
Streamlined task automation.
More accurate cost estimation.
Insightful predictive analytics and accurate forecasting.

But Before You Invest in AI…

The potential of AI is far-reaching, but without having solid foundations in place, your team may resist adopting it. Becoming AI‑Native means thinking with AI in all aspects of the organization, rather than just seeing it as a tool for course correction.

But you need to invest in your people first. This means building up their AI literacy and confidence. True adoption happens when project managers and business leaders understand how to frame decisions, interpret data-driven insights, and redesign processes around AI intelligence as a core organizational capability.

AI works best when combined with human judgment, experience, and contextual awareness. As poor data inputs can lead to unreliable forecasts or unrealistic timelines (for example), using AI systematically and consistently rather than experimentally is a high priority.

The Smart Project Management Path

No matter which project management style best suits your organization, AI is the next stage in your organization’s evolution.

With structured training that builds the capability to think architecturally about AI from the ground up, your business can start embedding intelligence into every decision cycle, thereby improving project outcomes.

Ready to scale with SAFe + AI? Get started with our AI-Native training courses.

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Why AI is the Ultimate Partner for Product Owners and Product Managers

Posted on January 29, 2026

Editor’s Note: Unprecedented business challenges are impacting your day-to-day role. You need more than theories—you need a plan and tactics. Welcome to the AI-Empowered blog series: Your guide to the what, why, and how of embracing AI to adapt and amplify your impact.

You’ve seen the headlines. You’ve felt the quiet buzz of AI chatbots in the background of your daily stand-ups. As a Product Owner (PO) or Product Manager (PM), your world is shifting beneath your feet.

Perhaps you’re staring at a backlog that feels more like a feature factory than a value-driven roadmap, wondering if artificial intelligence is about to automate your job away. You might feel the pressure to use AI tools but find yourself stuck in prompt purgatory, writing generic requests and getting hallucinated results that don’t fit your business context. The world of the modern product owner and product manager today looks like a frantic race to acquire new skills while simultaneously managing stakeholders who expect “AI magic” yesterday. The uncertainty isn’t just about the technology; it’s about your role in a world where data moves faster than your current workflows can handle.

The Hidden Costs of AI Inertia

Ignoring the AI evolution isn’t just a missed opportunity; it’s a direct threat to organizational health. When product leaders fail to integrate AI for POs and PMs, the business pays the price in three critical areas:

The productivity gap: Without AI augmentation, product teams spend up to 40 percent of their time on administrative debt—drafting user stories, manually summarizing feedback, and chasing status updates.

Strategic blindness: Companies failing to leverage AI-driven data analysis miss market signals that competitors catch in real time. This leads to strategic drift, where you build features that were relevant six months ago but are obsolete today.

The innovation tax: Research shows a widening chasm between AI-native firms and laggards.

Additionally, organizations that adopted AI for business functions saw a drop in productivity of 1.33 percentage points initially, but failing to redesign workflows around AI leads to a long-term ‘productivity paradox’ where legacy processes stifle new technology. ²

A Glimpse of Tomorrow: The AI-Empowered Product Leader

The future isn’t about AI taking over PO and PM jobs; it’s about the AI Product Owner taking over the market. Think of it as a world where your AI tools act as a tireless chief of staff. In this new reality, you aren’t just a task manager; you are an architect of outcomes. You use GenAI to synthesize thousands of customer tickets into actionable personas in seconds. You use prompt engineering to generate high-quality User Stories that are 90 percent ready-to-code, allowing you to spend your Mondays talking to customers instead of fighting with Jira. These are impactful skills you’ll gain from the AI-Empowered SAFe® Product Owner/Product Manager (POPM) course.

Your Expertise Enhanced: Defining the New Roles

The distinction between traditional roles and their AI-empowered counterparts is simple: leverage.

The AI Product Manager focuses on the what and the why by using AI to identify market gaps, conduct competitive research, and align AI initiatives with the long-term product vision. The AI Product Owner focuses on the how and when, utilizing AI-integrated tools to refine the backlog, automate acceptance criteria, and ensure the team is building the right thing at the right time. The Data Product Manager is a specialized role focused on the data supply chain, ensuring the models that power your product are fed high-quality, ethical, and unbiased data. Here are some specific examples of what AI could look like in your daily workflow as a PM or PO.

The AI product manager’s daily workflow

As an AI Product Manager, you leverage AI’s immense data processing power to anticipate a range of outcomes that inform your strategy:

Dynamic roadmapping. Research is vital, but roadmapping and prioritization are the heartbeat of a PM’s daily life. AI helps you move beyond static spreadsheets to create flexible, living roadmaps. You can use AI to create flexible roadmaps and “think around corners” to simulate what-if scenarios. If a competitor launches a surprise feature or a key dependency fails, AI can quickly re-calculate prioritization scores across your entire portfolio, helping you pivot without the usual panic.

Market sentiment synthesis. Instead of reading hundreds of App Store reviews, you use AI tools to ingest quarterly feedback and generate a “Top five friction points” report in minutes.

Strategic planning. Use AI to run “pre-mortem” simulations. “Act as a skeptical stakeholder. Identify three ways our proposed AI-driven recommendation engine might fail to meet our Q3 North Star Metric.”

Persona development. Use GenAI to create hyper-specific user personas based on actual behavioral data segments. This allows you to tailor features to a late-night power user rather than a generic customer.

The AI product owner’s daily workflow

For the AI Product Owner, the focus is on maximizing the flow of value through the Agile Team:

Accelerated User Stories. Writing user stories is no longer a blank-page exercise. By applying prompt engineering—such as providing the AI with a Feature description and asking for a breakdown into INVEST-compliant stories—you reduce drafting time by 70 percent.

Backlog refinement and estimation. During refinement, the PO can use AI tools to cluster sticky notes and identify dependencies across teams. AI can even suggest story point ranges based on historical velocity data for similar past tasks.

Automated acceptance criteria: Use AI to generate edge case scenarios. For a new login feature, the AI might suggest testing for “expired session during active API call,” a detail often missed in manual drafting.

By mastering these skills, you move from being a process follower to an AI-augmented strategist. You can link your expertise directly to tangible business results, such as reducing cycle time or increasing feature hit rates; benefits that are foundational to the SAFe Product Owner/Product Manager certification.

Practical applications: AI in your agile workflow

You don’t need to be a data scientist to lead an AI-empowered team. Here is how you can start today:

Prompt engineering. Stop asking AI to write a story. Instead, use structured prompts like this one: “As a SAFe AI Product Owner, draft three user stories for a new checkout feature, including acceptance criteria in Gherkin format, focusing on mobile-first users.”

Backlog refinement: Use AI tools and chatbots to cluster similar feature requests and identify themes that your human eyes might miss.

Step by step: Integrating AI into SAFe workflows

Preparation (PI Planning). Use AI to ingest your Strategic Themes and generate draft PI Objectives.

Execution. Use AI to record and summarize Daily Stand-ups, automatically updating the team’s blockers list.

Refinement. Use chatbots to take a high-level Feature and break it down into small, estimable User Stories.

The Conscience of the Machine: Responsible and Ethical AI

Innovation without ethics is a liability. As an AI Product Owner or Product Manager, you are the primary steward of how artificial intelligence interacts with your customers and their data. Implementing responsible AI isn’t a one-time task; it is a mindset that must be woven into every User Story and architectural decision.

The ethical guardrails for product leaders

To lead responsibly, you should implement four guardrails of ethical AI within your agile teams:

Data privacy and compliance. Establish clear data classification (public, internal, restricted). Never feed sensitive customer data or intellectual property into a public GenAI tool without anonymization. Ensure your AI features comply with global standards, such as GDPR or the EU AI Act.

Human-in-the-loop (HITL). AI should assist, not decide. High-stakes decisions—such as those involving financial approvals, medical data, or hiring—must always have a final human review. Use AI for drafting and analysis, but keep the human product conscience at the center of the backlog.

Fairness and bias mitigation. Actively audit your training data and outputs for bias. If your product uses AI to recommend features or predict user behavior, ask: Does this system treat all demographic groups equitably? Regularly conduct consequence scanning workshops to identify potential harms before they reach production.

Transparency and explainability. Be open with your stakeholders about where AI is used. Maintain an AI contribution registry and provide transparency notes for AI-powered features so users understand how decisions were reached.

By championing these principles, you don’t just protect the company from legal risk; you build the one thing AI cannot generate on its own: trust. You can further develop these leadership skills by exploring the SAFe Achieving Responsible AI guidance.

Unlock Your Full Potential

It’s time to rewrite the old product playbook. You have a choice: watch from the sidelines or become the author of your career’s next chapter. The AI-Empowered SAFe Product Owner/Product Manager course is more than a certification; it’s your survival guide for the AI-native era.

Feeling the AI pressure?

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Coming soon: The AI-Empowered SAFe® for Teams

“The New Reality of AI in Product Management.” Productboard Report, October 22, 2025. https://www.productboard.com/blog/ai-in-product-management-report/.

McElheran, Kristina. “The ‘Productivity Paradox’ of AI Adoption in Manufacturing Firms.” MIT Sloan Management Review, July 9, 2025. https://mitsloan.mit.edu/ideas-made-to-matter/productivity-paradox-ai-adoption-manufacturing-firms.

The AI Scrum Master: How Scrum Masters Use AI to Accelerate Team Flow

Posted on January 21, 2026

Scrum master using AI to improve scrum team performance

You start your Monday ready to coach your scrum team toward high performance, but by noon, you’re buried. You are manually chasing updates for the iteration report, squinting at Jira boards to find hidden dependencies, and trying to guess why the team’s velocity took a nosedive last Friday.

You’re also manually subtracting vacation days and part-time availability for a seven-person team just to get an initial velocity. Then there’s the mental load of managing the ART Planning Board—aka dealing with the “red-string” chaos. When you’re manually tracking physical dependencies across 5 to 12 teams, a single missed link can tank a PI. Instead of being the servant leader who removes blockers, you’ve become a high-paid administrative assistant. Your Scrum ceremonies feel more like Scrum chores.

You want to focus on team dynamics and psychological safety, but the sheer volume of data management makes continuous improvement feel like a distant dream.

This is the reality for many Scrum Masters today: You are working in the process rather than on the agile team.

Hidden Costs of Manual Agile Workflows for Scrum Masters

When Scrum Masters like you are bogged down by manual tasks, the organization pays a steep price that goes beyond simple overhead. Without the support of an AI-driven Scrum Master, even the most capable teams struggle to sustain true agility.

Innovation stagnation: Every hour spent on manual data entry is an hour lost to mentoring or innovation. Teams without active coaching often fall back into “water-scrumb-fall” habits. An AI-empowered Scrum Master helps reclaim this time by automating low-value work and surfacing actionable insights.

Predictability collapse: Without real-time data analysis, risks like technical debt or scope creep aren’t caught until the Iteration Review—or worse, the release. That can lead to poor ART predictability measures. If a team consistently operates outside the 80% to 100% range, they might lose the trust of the business owners. An AI Scrum Master provides earlier visibility into trends and risks, enabling timely course correction.

Talent burnout: High-performing engineers lose motivation when blockers take days to resolve because the Scrum Master is busy with other priorities. By reducing manual workload, an AI-empowered Scrum Master can respond faster, remove blockers sooner, and keep teams focused and engaged.

Technical debt: A team’s relentless focus on solution delivery often pushes innovation to the wayside. Without artificial intelligence to handle routine tasks, teams lose their buffer for innovation, leading to technical debt that can grow uncontrollably. An AI Scrum Master helps restore that balance by creating space for continuous improvement and innovation.

By integrating AI for Agile Teams, Scrum Masters can shift from reactive administration to proactive leadership, delivering greater flow, predictability, and sustainable agility across the Agile Release Train (ART).

The AI-Empowered Scrum Master: A Glimpse of an AI-Powered Future

An AI Scrum Master is a practitioner who integrates artificial intelligence—specifically Generative AI (GenAI) and predictive analytics—into their day-to-day work to improve their leadership capabilities. Adopting those behaviors involves being AI native—where AI becomes an intrinsic and trusted component in the way you and your teams think.

But let’s be clear: AI does not replace the human Scrum Master. While the AI handles pattern recognition in backlogs and automates meeting transcriptions, the human Scrum Master provides the empathy, ethics, and complex problem-solving that machines cannot replicate. The role has evolved into a strategic, analytics-based position that combines human judgment with AI-generated insights to navigate the complexities of enterprise-scale development.

The future of the Scrum Master role is not about working harder within a manual process; it is about evolving into a high-impact leader by leveraging an AI-augmented workforce. In this future, the Scrum Master acts as the human navigator for a powerful suite of machine-driven tools, creating a force multiplier effect for the entire team.

Redefining the partnership: human vs. machine

There is an important synergy between human and machine intelligence when discussing AI in an Agile Team practicing Scrum.

Humans provide the why. You bring emotional awareness, moral reasoning, and the ability to understand complex team context and nuance. You navigate the storming phase of team development by building trust—something a machine cannot replicate.

Machines provide the how much and how fast. AI excels at processing vast amounts of technical data, identifying hidden patterns in a backlog, and executing administrative tasks at an incredible scale. This is not a replacement strategy; it is an enhancement strategy. By allowing AI to handle the rote, data-heavy tasks, you’re free to focus on high-value leadership activities like coaching, conflict resolution, and strategic alignment.

Everyday applications in the Scrum Master role

Here’s what that synergy could look like in practice:

Planning Interval (PI) events. Use tools to rapidly calculate initial capacity. Instead of spending hours on spreadsheets, you can ask AI to instantly adjust for part-time team members and scheduled PTO, allowing the team to spend more time on story analysis. AI can even assist in drafting PI objectives that are specific, measurable, and aligned with the business goals.

Backlog management. GenAI can assist in splitting large features into vertical slices of value. AI can suggest acceptance criteria in a given-when-then format, moving your requirements from ambiguity to technical precision.

The strategic benefits of AI-Empowered Scrum Masters

Automating the mundane: Scrum Masters can use tools to automate Iteration Planning summaries and technical debt tracking—saving hours of manual documentation. Tools exist to handle sprint reporting, and can connect to the platforms you already use. AI note-takers can record, transcribe, and extract action items from your Team Syncs. During backlog refinement, GenAI assistants can help you write clear acceptance criteria and identify overlapping user stories.

Providing predictive risk management: AI can analyze historical Agile Team data to identify hidden dependencies or predict if a sprint is likely to fail its commitment by midweek.

Enhancing decision-making: By synthesizing vast amounts of data, AI helps Scrum Masters identify why continuous improvement has plateaued, offering suggestions based on industry benchmarks.

Offering facilitation support: AI can help structure retrospectives by clustering feedback into themes, ensuring every voice is heard without facilitation bias.

The future of the AI-Empowered Scrum Master

The future of the role isn’t about working harder; it’s about working smarter with AI-Empowered SAFe® Scrum Master training. This isn’t just a certification; it’s a transformation. An AI-Empowered Scrum Master uses GenAI and advanced analytics to automate the everyday and illuminate the invisible.

Imagine a world where your iteration reports are auto-generated with narrative context, where AI tools predict delivery risks before they happen, and where you have more time to spend on the human side of agile—coaching, conflict resolution, and leadership.

This is the promise of an AI-Empowered Scrum Master.

Boost Your Agile Expertise with AI-powered, Data-driven Leadership

The most tangible application of the AI-Empowered SAFe Scrum Master course is moving from gut-feel coaching to data-driven facilitation. Here are some examples.

A SAFe Scrum Master’s core responsibility is to improve flow. While traditional tools show you a Cumulative Flow Diagram, AI can take this a step further by automatically identifying bottlenecks in your system. It can analyze flow load and iteration velocity to pinpoint exactly where work is piling up—such as a specific testing environment or dependency on another team. Present that information to the team via a bottleneck report that suggests specific flow accelerators, such as adjusting WIP limits, to get value moving again.

AI can quickly convert vague PI objectives and draft specific and measurable ones that tie success measures to business outcomes. The result? Better alignment and stakeholder communication.

If your team is struggling with over-commitment during Iteration Planning, use predictive analytics to compare the current iteration backlog against historical iteration velocity and individual team member capacity. If the team is planning 40 points but AI identifies that the team is only likely to finish 32 (based on current PTO and technical debt levels), you can quickly intervene. This data-driven approach helps the team set realistic iteration goals and maintains the predictability that Business Owners rely on.

Scrum Masters are also turning to AI for “coach me” advice around conflict navigation and tough conversations. Maybe a Product Owner or Product Manager is pushing an unrealistic scope. You can ask AI to help you prepare a ready-to-use conversation guide that is direct, empathetic, aligned to Lean-Agile principles, and focused on outcomes and agreements.

When you can show leadership a clear correlation between technical debt reduction and increased iteration velocity, you move from a facilitator to a strategic partner. This shift is a core benefit of the SAFe^® Scrum Master Certification, positioning you as a high-value asset in an AI-first economy.

Responsible AI: The Ethical Frontier of Agility

Integrating these powerful tools into our teams means anchoring innovation in responsible AI. For an AI-Empowered Scrum Master, this isn’t just about following rules; it’s about protecting the team’s psychological safety and the enterprise’s data integrity.

Three pillars of responsible AI in agile workflows

The AI-Empowered SAFe® Scrum Master course structures your ethical approach around three critical pillars:

Human-centric AI: Protecting people and social norms. This pillar focuses on fairness and inclusiveness, ensuring that AI tools do not inadvertently introduce bias into performance reviews or team dynamics.

Trustworthy AI: Ensuring solutions are reliable, secure, and accurate. As a Scrum Master, you must be the first to verify that AI-generated velocity reports are based on high-quality data.

Explainable AI: Moving away from black-box logic. If an AI tool suggests a specific team member is a bottleneck, you must ensure the reasoning is transparent and documented before taking coaching action.

Next steps: Adopting AI tools in the Scrum Master workflow

Starting with AI doesn’t require a computer science degree. Begin by:

Identifying the three manual tasks that take the most time each week.

Introducing one AI-powered tool (like a meeting summarizer) to your team and gathering feedback during the next retrospective.

Pursuing a Scrum Master certification that specifically includes AI-native modules to understand how these tools fit into SAFe.

Unlock Your Full Potential with AI for Scrum Masters

Don’t let the administrative grind stifle your impact. Transition from a traditional facilitator to an AI-powered leader who drives true continuous improvement.

Enroll in the AI-Empowered SAFe® Scrum Master Course today, and lead your team into the future of agile and AI.

Stop letting contracts become your bottleneck.

Tired of the administrative grind? Discover how the AI-Empowered SAFe^® Scrum Master course turns manual reporting into strategic leadership and team excellence.

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Coming soon: The AI-Empowered SAFe® Product Owner/Product Manager

¹ “The Cost of Dysfunction: How Your Ineffective Team May Be Undercutting Your Organization’s Success,” Profusion Strategies, accessed January 9, 2026, https://profusionstrategies.com/profusion-blog/the-cost-of-dysfunction.

Building on Quicksand: When Tech Chaos Stalls Innovation

Posted on January 21, 2026

Editor’s Note: You’re facing unprecedented business challenges. You need more than theories—you need a blueprint. Welcome to a Leader’s Blueprint, your weekly guide to proven strategies that get results.

You have brilliant engineering teams and a clear business strategy. Yet, delivering a seamless customer experience feels like an uphill battle. When you look under the hood of your organization, you don’t see a unified engine; you see a collection of spare parts held together by duct tape. Data exists in silos that don’t talk to each other. Every new feature requires navigating a minefield of fragile legacy code. Your teams want to innovate, but they spend half their sprints fixing what broke yesterday or reinventing the wheel because they didn’t know another team had already built it.

The Hidden Costs of Architectural Inconsistency

When technology choices are disconnected from business strategy, you accrue more than just frustration—you accrue a massive liability.

Innovation Stagnation: Instead of creating new value, your most expensive talent is stuck “keeping the lights on,” battling complexity and maintaining redundant systems.
Erosion of Trust: When the back-end is chaotic, the front-end user experience suffers. Inconsistent design and system failures tell your customers that you don’t have your house in order.
Compounding Technical Debt: Every duplicated effort and quick fix is a loan taken out against your future speed and efficiency.

A Glimpse of the Solution: Enabling Agility

The answer isn’t to return to the days of rigid, top-down architectural control. The solution is Enabling Agility with Enterprise Architecture. This SAFe® competency shifts the Enterprise Architect (EA) into a strategic servant leader who actively champions collaboration and drives innovation across the enterprise.

Effective EAs provide strategic technical guardrails. These are minimum constraints that ensure consistency and compliance while giving Agile Teams the freedom to innovate within those bounds. It aligns technology investment with business goals, ensuring that the “architectural runway” is being paved before the teams need to land their heavy features. It turns architecture into a continuous flow of value, rather than a static document.

Your First Step

Host a short, dedicated one hour forum with a focused group of Enterprise, Solution, and System Architects. The purpose is not to review failures, but to celebrate and share what’s working well. Ask this single question:

“To accelerate our entire portfolio’s flow of value, what is one successful architectural pattern, standard, or technical principle from your value stream that we can share and align on as a consistent standard for everyone to reuse next week?”

This question achieves the following:
Focuses on Value: It ties the architectural discussion directly to accelerating the flow of value.
Highlights Success: It asks for a successful pattern, reinforcing a culture of positive sharing and learning, rather than only problem-finding.
Promotes Reuse: It immediately pushes for consistency and interoperability by encouraging component reuse.

Unlock the Full Blueprint

Moving from technical chaos to a streamlined Architectural Runway requires a shift in practices and mindset. The Enabling Agility with Enterprise Architecture competency provides the tools to establish technical guardrails, evolve the EA role, and align technology with value streams.

Stop letting technical debt dictate your roadmap.

Unlock the full SAFe Framework to build an architecture that adapts as fast as your market does. Get complete access by purchasing your SAFe® Insider membership today.

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In this Series:

Catch up on last week’s post: Lean-Agile Procurement
Coming up next: Enabling Agility with Enterprise Architecture from a Technology Leader’s viewpoint

¹ Stripe, “The Developer Coefficient,” September 2018, accessed December 8, 2025, https://stripe.com/files/reports/the-developer-coefficient.pdf

The Contract Bottleneck: When Traditional Procurement Slows You Down

Posted on January 14, 2026

Your Agile Teams are ready to sprint. The product vision is clear, the funding is approved, and the market opportunity is right now. But then, you hit a wall. You need a partner—a vendor to supply a critical component or specialized skill. Suddenly, agility grinds to a halt. You enter the world of traditional procurement: months of writing detailed requirements for an RFP, waiting for sealed bids, and enduring long rounds of contract redlining. By the time the ink is dry, the market has shifted, your requirements have changed, and your Agile Teams have been idling. You aren’t co-innovating; you’re just waiting on paperwork.

The Hidden Costs of Transactional Sourcing

When your procurement process operates in a silo separate from your development value stream, it creates a drag on the entire organization.

Lost Market Windows: While you negotiate terms and conditions, competitors who treat partners as extensions of their team are already launching.
Transactional Friction: Focusing rigidly on “lowest price” and fixed scope creates an adversarial relationship. Vendors protect their margins rather than solving your problem, leading to change-order wars later.
Innovation Stagnation: When you dictate the solution in a rigid RFP, you cap the potential for innovation. You get exactly what you asked for, not necessarily what you need or what the expert vendor could have proposed.

From Vendors to Partners: A Glimpse of the Solution

The solution is to stop treating procurement as a back-office administrative function and start treating it as a strategic capability. This is the Lean-Agile Procurement (LAP) competency. LAP moves away from the “us vs. them” transactional model toward co-innovation. Instead of paper-heavy RFPs, LAP utilizes collaborative events—like the Big Room Workshop. Here, key stakeholders and potential partners come together to clarify goals, co-create solutions, and even draft agile contracts in real-time. It integrates procurement directly into the Agile release train, ensuring that legal and sourcing align with the rhythm of value delivery.

Your First Step

You can start shifting the mindset from transaction to partnership this week. Identify one critical vendor or partner relationship currently in the pipeline or up for renewal. Ask your team:

“Are we collaborating with this partner to define the solution, or are we just negotiating the price of a predefined output?”

If the answer is the latter, you are likely leaving innovation—and speed—on the table.

Unlock the Full Blueprint

Moving from traditional sourcing to Agile partnerships requires a new toolkit. The Lean-Agile Procurement competency provides the frameworks you need, including the Lean Procurement Canvas™, to align partners, create adaptive legal frameworks, and reduce risk.

Stop letting contracts become your bottleneck.

Unlock the full SAFe Framework to build a supply chain that moves as fast as your strategy. Get complete access by purchasing your SAFe® Insider membership today.

Get Started

In this Series:

Catch up on last week’s post: Managing a Balanced Portfolio
Coming up next: Enabling Agility with Enterprise Architecture

¹ Mirko Kleiner, “The Values of Lean-Agile Procurement,” Lean-Agile Procurement Alliance, accessed December 8, 2025, https://www.lean-agile-procurement.com.

Escaping the Urgent: Why Immediate Demands Are Killing Your Future Growth

Posted on January 7, 2026

You start every quarter with a bold intention: this is the quarter we finally make traction on our future-proofing initiatives. You have a list of strategic bets that will open new markets and secure the company’s longevity. But then Monday morning hits. A legacy server goes down. A key client requires an immediate bespoke feature update. The sales team needs support to close the quarter. Slowly but surely, the “tyranny of the urgent” takes over. By the time the quarter ends, your team is exhausted from keeping the lights on, and those critical strategic bets haven’t moved an inch. You are surviving today, but you are mortgaging tomorrow.

The Hidden Costs of an Unbalanced Portfolio

When your portfolio is heavily weighted toward immediate demands at the expense of long-term strategy, you aren’t just delaying innovation; you are actively degrading your competitive advantage.

Innovation Starvation: While you pour resources into maintaining the status quo, your competitors are building the disruption that will make your core business obsolete.
Legacy Anchors: Without a strategy for “Horizon 0” (retiring systems), you continue to fund low-value work and legacy debt, draining the budget needed for growth.
Economic Sub-Optimization: By saying “yes” to every urgent request, you dilute your focus. You end up with a traffic jam of good ideas, but very few great outcomes actually getting delivered to the market.

A Glimpse of the Solution

The answer isn’t just “working harder”—it is implementing the Managing a Balanced Portfolio competency. This component of Lean Portfolio Management (LPM) moves you away from reacting to fire drills and toward intentional Horizon Planning. By visualizing your work through a Portfolio Kanban, you can actively manage the flow of value across different horizons:

Horizon 1: Extending your core business.
Horizon 2: Growing emerging value.
Horizon 3: Placing future bets.
Horizon 0: Retiring what no longer serves you. This framework empowers Portfolio Leaders to make data-driven “Go/No-Go” decisions, ensuring you are allocating capacity to the future, not just the present.

Your First Step

You can do a quick assessment of your portfolio’s health this week. Review the last 10 significant initiatives or Epics where your portfolio has made significant progress in delivering.

If 90% or more of your investment is sitting in Horizon 1 (Core), your portfolio may not be balanced for the future.

You are optimizing for safety today at the risk of irrelevance tomorrow.

Unlock the Full Blueprint

Recognizing the imbalance is the start; fixing it requires a systemic approach. The Managing a Balanced Portfolio competency provides the tools to implement Horizon Planning, visualize flow with Kanbans, and use economic prioritization to make the hard choices easier.

Stop letting the urgent crowd out the important.

Unlock the full SAFe Framework to build a portfolio that delivers today while securing your future. Get access by purchasing your SAFe® Insider membership today.

Get Started

In this Series:

Catch up on last week’s post: Validating Investment Opportunities
Coming up next: Lean-Agile Procurement

¹ Moore, Geoffrey. Zone to Win: Organizing to Compete in an Age of Disruption. Diversion Books, 2015.

Rowing in Different Directions: Don’t Let Your Legacy Portfolios Prevent Future Success

Posted on December 10, 2025

You’ve just concluded the annual strategy offsite. The vision is bold, the goals are ambitious, and the leadership team is energized to conquer new markets. But when you and your peer portfolio leaders return to the office, the energy slowly fizzles out.

Despite the new slide decks, the new strategy never translates into action. Realignment is difficult; most companies have to hire expensive consulting firms just to untangle their organization and identify the value streams and product lines that matter. Because you lack a native model to organize these portfolios yourself, your funding and focus remain perfectly aligned to deliver last year’s strategy. You are trying to row in a new direction, but every portfolio is pulling its oar a different way.

The Hidden Costs of a Strategy-Structure Gap

When your organizational structure is not aligned with your strategic goals, it creates constant friction that silently sabotages your success.

Wasted Investment: Precious capital and talent are spent on low-priority work. Worse, different teams in different portfolios unknowingly duplicate efforts, solving the same problem in isolation and wasting valuable resources.
Strategic Drift: The company’s vision points north, but the inertia of the existing portfolios keeps pulling the execution south. This gap between what you say and what you do widens over time, making strategic goals impossible to reach.
Decision Paralysis: With unclear ownership of value streams, even simple decisions are endlessly escalated. Agility dies as leaders wait for approvals from committees that lack the context to make an informed choice.

From Complexity to Clarity: Identifying Value

The solution is to intentionally design your organization to match your strategy. In SAFe®, this is the Organizing Portfolios competency. This involves structuring your organization around clearly identified products, solutions and value streams—the end-to-end set of steps required to deliver a product or solution to a customer.

Instead of grouping people by function, you create a portfolio with all the people, funding, and authority needed to serve the value streams within it. This clarity of purpose and responsibility is what enables clear strategic execution. Teams are empowered to make fast, smart choices because they are fully aligned and have the context of the larger strategic goal.

Your First Step

You can begin to diagnose your strategy-structure gap this week with a simple exercise. Take your company’s single most important strategic goal for this year and ask your leaders:

“Which teams and which budgets are directly contributing to this goal?”

If they can’t draw that map with clarity in under 30 minutes, your organizational structure is obscuring—not enabling—your strategy.

Unlock the Full Blueprint

Visualizing the problem is the first step, but realigning an enterprise requires a proven approach. The Organizing Portfolios competency provides a complete blueprint for defining value streams, structuring portfolios for flow, and dynamically adapting them as your strategy evolves.

Stop letting your legacy portfolios dictate your strategy.

Unlock the full SAFe Framework, with the competencies and guidance needed to build an organization that can execute your vision. Get complete access by purchasing your SAFe® Insider membership today.

Get Started

In this Series:

Catch up on last week’s post: Continuously Delivering Value
Coming next week: Validating Investment Opportunities

¹ Richard P. Rumelt, “Getting Strategy Wrong—and How to Do It Right Instead,” McKinsey Quarterly, accessed October 28, 2025, https://www.mckinsey.com/capabilities/strategy-and-corporate-finance/our-insights/getting-strategy-wrong-and-how-to-do-it-right-instead

The Innovation Brake: When Your Delivery System Can’t Keep Up with Ambition

Posted on November 12, 2025

The CEO’s got a big, game-changing idea, and the product team has the numbers to back it up. All eyes in the strategy meeting turn to you, the technology leader. The question is simple: “How fast can we build it?”

On the outside, you project calm confidence. But on the inside, you’re mentally navigating a minefield of potential bottlenecks, excessive work in process (WIP), and the friction of too many handoffs. The honest answer isn’t a date; it’s a list of caveats. Your ambition as a leader is to say “yes,” but your current system is screaming “not so fast.”

The Hidden Costs of Technical Drag

When your delivery pipeline has too much friction, the consequences ripple through the entire technology organization, creating significant risks and liabilities.

The WIP Whirlpool & Bottleneck Backlog: Excessive Work in Process (WIP) and unaddressed bottlenecks create a vicious cycle. Teams are constantly context-switching, leading to slower completion times and a growing mountain of unfinished work. This grinds innovation to a halt, making every future change slower, more expensive, and more complex.
Developer Frustration & Attrition: Top engineering talent wants to solve complex problems and ship great code, not spend their days fighting a frustrating system. A slow, cumbersome process leads to burnout and the loss of your best people to competitors with modern tech stacks.
Increased System Risk: Every manual handoff and complex, rushed deployment is a potential failure point. As speed is prioritized over stability, the system becomes more fragile, leading to more bugs, unexpected downtime, and security vulnerabilities. This is exacerbated by legacy policies and procedures that are slowing down everything.

From Friction to Flow: A Glimpse of the Solution

The solution isn’t just about better code; it’s about building a better system for delivering that code. In SAFe®, this is the Accelerating Product Flow competency. For technology leaders, this means creating a streamlined, automated path from a developer’s keyboard to a live production environment.

This involves a relentless focus on accelerating flow. Starting with:

Identifying Bottlenecks: This means looking at your entire delivery pipeline—from build times to security scans to testing environments—and finding the single biggest source of delay. Is it a manual approval gate? A slow testing cycle? Addressing these constraints is the key to unlocking speed.
Minimizing Handoffs: Every time work is handed from requirements ideation through to approval for release, you introduce wait time and the potential for error. The goal is to create cross-functional teams and automated processes that reduce these handoffs, smoothing the path to production.

Your First Step

You can begin to diagnose your biggest point of friction this week. Ask one of your engineering teams a direct question:

“What is the most frustrating, time-consuming manual step between writing a line of code and seeing it live in production?”

The answer will immediately pinpoint what you need to resolve first.

Unlock the Full Blueprint

Identifying a bottleneck is the first step, but creating a high-velocity engineering organization requires a holistic approach. The Accelerating Product Flow competency provides a full blueprint for implementing eight flow accelerators, including optimizing time in the zone and getting faster feedback.

Stop letting technical drag be the brake on your company’s ambition.

Unlock the full Framework, competencies and guidance needed to build a fast, resilient, and innovative tech engine. Get complete access by purchasing your SAFe® Insider membership today.

Get Started

In this Series:

Catch up on last week’s post: Accelerating Product Flow from a Product Leader’s Perspective
Coming next week: Organizing Portfolios

¹ Stripe, “The Developer Coefficient: Software engineering efficiency and its $3 trillion impact on global GDP,” (September 2018), accessed October 28, 2025, https://stripe.com/files/reports/the-developer-coefficient.pdf

Why You Should Build a Data-Driven Product Strategy for Modern Product Management

Posted on October 22, 2025

data-driven product strategy - a SAFe series

You’re in the quarterly strategy meeting. The stakes are high, and a critical decision must be made: which major initiative should be prioritized for funding? The debate is passionate, but it’s driven by compelling arguments and seniority, not data. You have dashboards, but they’re filled with vanity metrics. No one can definitively answer the most important question: “Which of these options will actually move the needle on our business goals?”

When the loudest voice in the room becomes your primary decision-making tool, you’re not strategizing; you’re gambling.

What is a Data-Driven Product Strategy?

A data-driven product strategy is one that relies upon product analytics and qualitative insights to inform decision-making and which direction you should take your products in.

It’s a product management and development approach that aims to improve strategy by ensuring it’s driven by a comprehensive understanding of product usage based on concrete information and evidence, such as usage patterns, customer behavior, and performance metrics, rather than guessing what should be done next using assumptions or intuition.

The Hidden Costs of an Opinion-Driven Culture

Operating without clear, consistent product metrics is like flying a plane without an instrument panel. The risks are immense and go far beyond inefficient meetings:

Strategic Drift

Teams invest significant time and effort into features that feel important but are never tied back to defined outcomes. Over time, this disconnect causes the product to slowly drift away from its original goals and customer needs, as well as market position. Without data to course-correct and identify areas for improvement, even well-intentioned work can pull the product in conflicting directions.

Wasted Investment

When priorities aren’t grounded in measurable impact, precious capital and talent are spread thin across initiatives that don’t really make a difference. Engineering time, design effort, and marketing spend are consumed by features or experiments that fail to improve business performance or user experience and satisfaction. And this is often done without anyone realizing the true cost.

Inability to Learn

Without measuring the results of decisions, product teams lose the ability to learn from their work. Every launch becomes a shot in the dark, with no feedback loop to indicate success or failure. This prevents continuous improvement, making it difficult to refine strategy or build confidence in future decisions.

Slower Decision-Making

In the absence of data, decisions rely heavily on debate to try to reach a consensus. This leads to prolonged discussions and decision paralysis. Instead of moving quickly with clarity, teams spend time defending opinions rather than aligning around evidence and quantitative data.

Erosion of Trust and Alignment

When decisions are driven by opinion, stakeholders often question why certain choices were made. This can erode trust between teams and leadership, which creates friction across functions and makes it harder to align around a shared vision. Product development guided by the right data provides a common language; Without it, alignment becomes fragile and short-lived.

Data-Driven Product Management: From Guesswork to Guidance

The antidote to this uncertainty is building a culture of data-driven decision-making. In SAFe®, this is guided by the Measuring Product Performance competency. This framework provides clarity by viewing your product through four essential lenses: Business Outcomes, User Engagement, Customer Satisfaction, and Technical Performance.

This holistic view is powered by combining two types of metrics:

KPIs (Key Performance Indicators): hese are your instruments, providing a continuous pulse-check on the operational health of your product.
OKRs (Objectives and Key Results): This is your destination, aligning everyone toward ambitious, strategic goals.

Using both, you always know your current health and where you’re headed.

Benefits of Using Product Data to Make Strategic Decisions

Product management data analytics help PMs in several ways:

Enable Better Product Decisions for Product Managers

For a product manager, data provides the foundation for confident prioritization. By relying on key data instead of intuition alone, teams can optimize product decisions, focusing effort on initiatives that deliver the greatest value to users and the business.

Leverage Data Analysis to Move Faster with Confidence

Strong data analysis helps teams to reduce uncertainty and accelerate decision-making. When evidence is readily available, discussions become more focused, alignment happens faster, and teams can make data-driven decisions without unnecessary debate.

Create a Data-Driven Culture with Shared Metrics

Shared metrics help create a data-driven culture where teams align around outcomes instead of opinions. This common language enables better collaboration across functions and ensures everyone is working toward the same strategic goals.

Reduce Risk and Waste

When teams use product data effectively, they can identify underperforming initiatives early. This reduces risk and avoids wasted investment. It also ensures resources are allocated based on evidence rather than guesswork.

Support a Strategy Framework with Transparency and Accountability

An effective strategy depends on having a clear view of how the product is performing. When decisions are grounded in measurable outcomes, it becomes easier to understand the reasoning behind them and assess their impact over time. This shared visibility helps teams stay aligned and reinforces ownership of decisions. What’s more, it allows strategic choices to be evaluated and refined over time.

How to Use Data to Drive Product Growth and Actionable Insights

Start With a Clear, Measurable Question

You can begin this shift with a single question. This week, pick one significant feature on your upcoming roadmap and ask the team:

“If this feature is wildly successful, which single, measurable metric will change, and in what direction?”

If there isn’t a clear answer, the feature’s purpose—and its value—is a mystery.

Define Meaningful Metrics

Metrics should tell you something important about your product, not just fill a report. Think about engagement, retention, revenue impact, or operational efficiency—whatever shows real customer value. The key is choosing measures that are specific and actionable. They should be directly tied to decisions, so you always know which levers to pull next.

Integrate Metrics Into a Strategy Framework

Undertaking the first two steps above brings immediate clarity. But creating a true data-driven engine requires a complete system. The Measuring Product Performance competency provides a full blueprint for defining meaningful metrics across all four lenses and integrating them into powerful OKRs and KPIs. Stop flying blind. Unlock the full framework, competencies, and guidance you need to make every product decision with confidence. Get access by purchasing your SAFe® Insider membership today.

Continuously Measure and Adjust

Data isn’t a one-time check; it’s a constant feedback loop. Track results for every launch, experiment, or update, then analyze what’s working and what’s not. Use these insights to refine priorities, validate assumptions, and make smarter decisions for the next round of features. Your product evolves with each insight.

Embed a Data-Driven Culture

A data-driven strategy only works if the team lives it. Encourage using metrics in discussions and planning. Share results openly to celebrate wins and learn from misses. Over time, using data becomes second nature, helping everyone make better decisions and keeping the product aligned with real customer needs.

Scaled Agile: Helping You Use the SAFe Framework to Make Data-Driven Decisions

Embrace the full power of the SAFe Framework, with the competencies and guidance that will empower you to become truly customer-centric and unlock your organization’s full potential. Get complete access by purchasing your
SAFe Insider membership today.

Get Started >

In this Series:

Catch up on last week’s post: The Sound of Silence: When a Lack of Customer Feedback Derails Your Strategy
Coming next week: Creating Responsive Roadmaps

¹ According to the McKinsey Global Institute, as cited on the Data Ideology website, “Data-Driven Organizations Are 23 Times More Likely to Acquire Customers, Six Times as Likely to Retain Customers, and 19 Times as Likely to Be Profitable as a Result”. Retrieved on October 22, 2025, https://www.dataideology.com/data/data-driven-organizations-are-23-times-more-likely-to-acquire-customers-six-times-as-likely-to-retain-customers-and-19-times-as-likely-to-be-profitable-as-a-result/

Large Solution Refinement: Paving the Super-Highway of Value Delivery

Posted on July 13, 2022

This post is the second in a series about success patterns for large solutions. Read the first post here.

Backlog refinement is integral to the Scrum process because it prevents surprises and maintains flow in iterative development. Regular backlog review ensures the backlog is ready for iteration planning. An Agile team understands how much they still need to refine the backlog items before the next iteration planning and beyond.

When applying SAFe® to large, complex, cyber-physical systems, you must expand backlog refinement to include more viewpoints. The complexity of a large solution is rarely fully comprehended by one or a few individuals, and the size of the large solution exacerbates the impact of risks that can escape into large solution planning.

So how do we find the balance between overpreparation, which limits ownership and innovation by the solution builders, and under-refinement, which can undermine the solution and the flow of value delivery?

To answer these questions, we adapted the following success patterns for large solution backlog refinement.

Use the Dispatcher Clause

The dispatcher principle guides large solution refinement by preventing the premature dispatch of requirements to Agile Release Trains (ARTs), solution areas, or Agile teams. Premature dispatching can cause risks like:

• Misalignment in the development of different solution components
• Missed opportunities for economies of scale across organizational constructs
• Sub-optimization of lower priority solution features

In contrast, making the right trade-off decisions at the right level drives holistic and innovative solutions.

Key stakeholder viewpoints that are often overlooked include marketing, compliance, customer support, and finance. Ensuring these voices are heard during refinement work can prevent issues that might remain undetected until late in the solution roadmap.

For complex solutions, we discovered that a planning conference is more effective than pre-and post-PI Planning events alone. This event mimics a PI Planning event and is intended to align upcoming PI work across ARTs and solution areas. To keep the conference focused and productive, it should only include representatives from the participating ARTs. We will cover specific planning conference details in a later blog post.

The goal of the planning conference is to provide a boundary for the large solution refinement work. Preparation for key decisions that can be made in the planning conference should be part of the refinement work. But making key decisions is part of the planning conference. However, key stakeholder inputs that cannot be reasonably gathered during the planning conference should be included in the refinement work.

For example, in Figure one, a review of the key behavior-driven development (BDD) demo and testing scenarios by a customer advisory board is valuable input in refinement. The customer advisory board will not attend the two-day planning conference, so their advance input provides guardrails on the backlog work that’s considered.

Agree on the Definition of Ready

The definition of the readiness (DoR) criteria for a large solution backlog is often multidimensional. Consider, for example, the architectural dimension of the solution. The architecture defines the high-level solution components and how they interact to provide value. The choice of components is relevant to system architects in the contributing ARTs and stakeholders in at least these areas:

• User experience
• Compliance
• Internal audit and standards
• Corporate reuse
• Finance

Advancing the backlog item—a Capability or an Epic—through the stages of readiness often requires review and refinement from the various stakeholders.

Figure one is an example Definition of Ready Maturity Model. It shows the solution dimensions that must be refined in preparation for the solution backlog. Levels zero to five show how readiness can advance within each dimension. The horizontal contour lines show that progression to the intermediate stages of readiness is often a combination of different levels in each dimension.

Applying SAFe for Agility — *Figure 1. Definition of Ready Maturity Model example*

This delineation is helpful when monitoring the progression of a backlog item to intermediate readiness stages on a Kanban board.

The key to balancing over-preparation and under-refinement is to distinguish between work that an ART or solution area can complete independently without a high risk of rework. For example, final costs could be prohibitively high without a Lean business case to scope the solution. Another common high-risk impact of under-refinement is unacceptable usability caused by the siloed implementation of Features by the ARTs.

The Priority BDD and Test Scenarios in Figure one represent how features are used harmoniously. These scenarios provide guardrails to help ARTs prioritize and demonstrate parts of the overall solution without significant rework of a PI.

Identifying the dimensions, levels, and progression of readiness is a powerful organizational skill for building a large solution.

Regular large solution refinement is necessary to ensure readiness. The complexity of a large solution warrants greater effort and participation than Solution Management can cover. And the number of key decisions grows in direct proportion to the size of a solution.

Our experience shows that roughly 10 percent of those who participate in large solution development should participate in a regular refinement cadence. If the total participation is 450 people, then 45 representatives from across ARTs or solution areas should set aside time for weekly refinement iterations.

Backlog refinement for a large solution requires more capacity than a typical backlog refinement session. The refinement crews determine a cadence of planning, executing, and demonstrating the refinement work. One-week iterations, for example, help drive focus on refinement to ensure readiness.

We also discovered that refinement crews of six to eight people should swarm refinement work within iterations. These groups are usually created based on individual skills and their representation within stakeholder groups. Alignment with crews and dimensions or skillsets is determined during the planning of refinement iterations. The goal is always to move the funnel item to the next refinement maturity level in the next iteration.

Our experience says that each refinement crew requires at least three to four core participants. The other crew members can come from stakeholder organizations outside the Solution Train.

Readiness progress must be reviewed on a regular cadence with solution train progress. Progress can be represented in the Solution Kanban between the Funnel and Backlog stages, as shown in Figure two. In our example, these stages replace the Analyzing state provided as a starting point in SAFe.

The organization must also allow each refinement step to vary over time, as it makes sense for the solution. For example, as the development of the solution progresses toward a releasable version, the architecture should stabilize. Therefore the readiness of the backlog item in the architecture dimension should progress very quickly, if not skip some readiness steps. As solutions approach a major release, the contributors’ capacity can shift from readiness to execution of the current release or readiness for the next release.

Because refinement happens in a regular cadence of iterations, weekly, for example, the refinement crews should be empowered to make these decisions in refinement iteration planning.

Employ Dynamic Agility

So is there a definitive template of dimensions with levels and a step-by-step process for determining the DoR? Not quite. And we don’t think that a prescriptive process is best for most organizations.

Instead, we advocate for using the organizational skill of dynamic agility.

As the size and complexity of a solution grow, so do the number and type of variables: compliance type, hardware types, skills required, size of the development organization, size of the enterprise/business, specialization of customer types, and so on. This complexity is augmented by company culture challenges, workforce turnover, and technology advancements in emerging industries.

Individuals’ motivation and innovation suffer when they get lost in the morass of complexity. When things don’t get done, more employees are added to help fix the problem. This workforce growth only magnifies the complexity again.

In contrast, the organizational skill of dynamic agility stimulates autonomy, mastery, and purpose for individuals within teams, teams-of-teams, and large solutions.

Consider the House of Dynamic Agility represented in Figure three.

How can dynamic agility be applied to large solution refinement? DoR identification and maintenance of its dimensions and levels happen through a regular cadence of the right events. How often should these occur, for how long, and who should attend? What elements will represent and communicate the DoR? What roles are best suited to own and facilitate the management and use of DoR over time? How will collaboration across the organization happen most efficiently for maximum benefit? These questions are best determined in the context of the large solution.

Conclusion

Large solutions require a balance of preparation and execution to achieve an optimal flow of value. Conducting backlog refinement in preparation for a large solution planning conference and PI Planning lets decomposed work items be implemented without risk of rework. Avoiding over-specification in refinement allows ARTs to innovate and accomplish within the guardrails of refinement. Enabling large solutions to leverage dynamic agility builds ownership, collaboration, and efficiency in a large-scale endeavor.

Look for the next post in our series, coming soon.

About Cindy VanEpps, Project & Team, Inc.

Cindy VanEpps - SAFe® Program Consultant Trainer (SPCT)

From crafting space shuttle flight design and mission control software at Johnson Space Center to roles including software developer, technical lead, development manager, consultant, and solution developer, Cindy has an extensive repertoire of skills and experience. As a SAFe® Program Consultant Trainer (SPCT) and Model-based Systems Engineering (MBSE) expert, her thought leadership, teaching, and consulting rely on pragmatism in the application of Agile practices.

About Wolfgang Brandhuber, Project & Team, Inc.

Chief Scrum Master, and Agile Head Coach in various Agile environments

Dr. Wolfgang Brandhuber has been a Scrum Developer, Product Owner, Scrum Master, Chief Scrum Master, and Agile Head Coach in various Agile environments. His passion is large solutions. Since the advent of the large solution level in the Scaled Agile Framework in 2016, he has set up and helped solution trains improve their complex systems. During his 18 years as a professional consultant, he worked over 16 of those in the Agile world and more than nine years with SAFe. Among other certifications, he is a certified SAFe® Program Consultant Trainer (SPCT), a Kanban University Trainer (AKT), and an Agility Health Trainer (AHT).

About Malte Kumlehn, Project & Team, Inc.

Malte helps deliver complex ecosystems, people, Cloud, AI, and data-powered digital transformations toward business agility. He pioneers intelligent operating models for portfolios with large solutions as a SAFe® Fellow, advisory board member, and executive advisor in this field. He guides executives in developing the most challenging competencies that allow them to deliver breakthrough results through Lean-Agile at scale. His experience has been published by Accenture, Gartner, and the Swiss Association for Quality over the last ten years.

Learn more about Project & Team.