Category: Organizational Change & Culture

Change Management in AI Adoption: Effective Strategies for Managing Organizational Change While Implementing AI

Posted on June 11, 2025 by - AI Transformation, Organizational Change & Culture

Artificial intelligence (AI) is a living, learning capability that only achieves full impact when paired with human-centered change management. Think of AI and change management as a symbiotic pair: AI supplies the insight and automation that can reinvent how work gets done, while change management provides the human alignment, culture-building, and governance that let those insights take root and scale. Each amplifies the other.

Introducing AI reshapes how people make decisions, collaborate, and create value.

This blog explores how embedding proven change management practices into every stage of AI adoption—discovery, implementation, optimization, and value realization—turns isolated pilots into enduring, enterprise-wide advantage.

Successfully integrating AI into an organization requires personal investment from all affected parties, from leadership to frontline employees. Failure to secure this buy-in leads to wasted resources and resistance, as individuals grapple with fears of job displacement, loss of control, and uncertainty about AI’s purpose and impact.

To navigate this, organizations must adopt a strategic, human-centric approach, leveraging established change management practices. Success depends on:

  • Transparent, ongoing communication that addresses specific stakeholder concerns
  • Executive leadership that champions AI and cultivates adaptability
  • Early-stage engagement that co-designs the AI journey and validates value through pilot programs

Empowering people at every level is central to AI success. Organizations unlock strategic advantage by building a culture that values human-AI collaboration. Focusing exclusively on the mechanics of AI often sidelines its most important dimension: empowering your people.

1. Discovery & Strategy: Laying a Strong Strategic Foundation

Every successful AI adoption starts with a strong strategic foundation. First, surface the highest-impact opportunities across the business, from automating back-office workflows to embedding intelligence into customer-facing products. Use a proven readiness model to benchmark data, talent, and infrastructure against industry standards, revealing both strengths to leverage and gaps to close.

Translate those insights into a pragmatic roadmap that balances quick-win pilots with bold, long-horizon initiatives, each backed by a clear business case and defensible ROI model.

Throughout, bring the right voices to the table—executives, domain experts, compliance, and frontline teams—to secure sponsorship and reduce risk. Pair the technical plan with a targeted change management playbook: structured communications, hands-on enablement, and a culture-building program that turns wary employees into empowered AI champions.

The result is an AI strategy that is not just technically sound but financially disciplined and fully integrated into your organization’s DNA.

2. Implement & Integrate: Turning Vision into Action

With a strategy in place, delivery begins, translating ambition into capability that augments human decision-making and accelerates team performance. We weave AI into the tools teams already trust, whether Atlassian, ServiceNow, or bespoke platforms, so intelligence feels like a natural enhancement, not a disruptive shift.

Start with targeted pilots where the upside is clear and human expertise is indispensable, proving that algorithms combined with people outperform either alone. From day one, instrument workflows with performance and safety dashboards to detect and resolve drift, bias, or bottlenecks before they escalate.

In parallel, roll out role-specific enablement—from bite-size tutorials for frontline staff to deep-dive labs for data scientists—helping every employee master new capabilities and reinvest saved time into higher-value, creative work. By the end of this phase, AI is a trusted co-pilot that amplifies human judgment and frees talent to focus on what only people can do.

3. Tune & Optimize: Refining Performance and Experience

Post-implementation, sustained value depends on rigorous tuning. Establish a governance layer that blends security controls with clear accountability for model performance, ethics, and data privacy. A Center of Excellence—staffed by AI specialists and front-line power users—creates a real-time feedback loop for continuous improvement.

Ongoing scenario-based testing keeps bias, drift, and edge cases in check, ensuring AI systems remain trustworthy across conditions. Just as important, continue human enablement through onboarding sessions, refresher courses, and role-specific playbooks.

Targeted communications celebrate quick wins and share lessons learned, building confidence and curiosity across the organization.

4. Value Realization: Scaling Impact

When AI becomes an enterprise-wide capability, success is measured by how far and how sustainably it multiplies human potential. Wire each use case into a live scorecard of KPIs and value metrics, paired with ongoing pulse checks on adoption, readiness, and employee sentiment.

Advanced analytics surface underutilized areas or friction points, allowing teams to adjust both technology and supporting processes. Early wins are shared, scaled, and celebrated to accelerate momentum. Internal Centers of Excellence turn grassroots expertise into repeatable playbooks and reusable assets.

To ensure inclusive and ethical growth, maintain open forums and clear accountability across operations. This creates a scalable AI ecosystem that compounds value and supports the people driving your enterprise forward.

5. Future-Proofing: Sustaining Long-Term Advantage

AI is always evolving, and future-ready organizations evolve with it. Build for adaptability by championing continuous learning and expanding the AI frontier, from dashboards to prediction, prescription, and eventually autonomous support.

At every stage, AI should amplify human ingenuity. Algorithms handle the analysis so people can focus on strategy, creativity, and relationships. Promote this mindset through cultural touchpoints like guilds, lunch-and-learns, and communities of practice. Grow in-house talent that can lead future waves of innovation.

When technical roadmaps are interwoven with cultural evolution, AI becomes part of your organizational DNA: resilient, adaptable, and ready for what’s next.

Change Management Strategies for AI Success

  • Living Documentation: Keep artifacts current to reflect real-time changes in implementation.
  • Tailored Solutions: Adapt change approaches to your business context and tools.
  • Expert Guidance: Leverage experienced change professionals familiar with AI projects.
  • Proven Practices: Ground your approach in established principles from Lean Change Management or CMI.
  • People First: Involve employees early through workshops, feedback loops, and consistent communication.
  • Visual Clarity: Use change kanbans and impact maps to show how AI impacts different functions.

Earning Advocacy and Engagement

  • Communicate Clearly: Articulate the benefits of AI in plain language and address concerns transparently.
  • Empower Champions: Support influential employees who can advocate for AI change.
  • Invest in Training: Provide role-specific learning to build confidence and fluency.
  • Celebrate Wins: Highlight and amplify early successes to build enthusiasm and momentum.

The Bottom Line
Integrating AI into your organization requires more than just technical implementation. With a clear change strategy and a focus on people, you can orchestrate adoption, accelerate impact, and unlock the full potential of AI across your enterprise.

Technology Alone Won’t Cut It: Building an AI-Ready Culture to Support AI Transformation

Posted on February 11, 2025 by - AI Transformation, Organizational Change & Culture

Organizations invest heavily in AI tools and infrastructure—to the tune of well over $1 trillion globally since 2022—but often fail to generate meaningful results. The tech they’re implementing isn’t the issue. It’s the lack of cultural and operational readiness. AI only becomes valuable when it is embedded into the business, informing decision-making, improving workflows, and delivering measurable outcomes.

Many businesses treat AI adoption as an IT upgrade, assuming that implementing new tools will automatically improve efficiency. This approach frequently leads to underwhelming results. 

Companies that achieve real success take a different approach: they integrate AI into everyday operations, ensuring teams understand its capabilities and trust its recommendations. AI adoption requires organizations to rethink how work gets done, how decisions are made, and how data is used.

Change Management Determines AI’s Impact

AI disrupts workflows, decision-making, and job roles, making structured change management essential. Without clear leadership, employees may view AI as a threat rather than a tool. Resistance, confusion, and lack of trust can stall adoption.

Successful AI-driven organizations make change management a priority. Leaders must communicate AI’s role transparently and ensure employees see its value. 

When AI adoption is positioned as a tool for augmenting strategic decision-making, teams are more likely to engage. Deloitte, for example, has successfully integrated AI-powered document review into its legal and compliance teams by providing clear training and demonstrating tangible efficiency gains.

Companies also need to establish feedback loops. Employees who interact with AI daily should have input on refining models and improving usability. AI adoption should be an evolving process, not a one-time rollout.

Building a Data-Driven Culture to Make AI Work

AI adoption depends on a company’s ability to make informed, data-driven decisions. Moving from instinct-based decision-making to AI-backed strategies requires significant shifts in processes, incentives, and leadership priorities. But this isn’t going to happen if the organization’s culture doesn’t support that goal.

Trust is one of the biggest barriers to AI adoption. Employees often hesitate to rely on AI-generated recommendations because they don’t understand how AI reaches its conclusions. To bridge this gap, organizations must foster data literacy at all levels. Leadership should actively model data-driven decision-making, ensuring that teams see AI as a valuable input rather than an opaque black box.

Fostering trust also means maintaining human oversight, allowing users to validate AI-generated outputs, and continuously refining models based on user feedback. When employees understand and trust AI, they are more likely to integrate it into their decision-making processes.

For example, financial institutions use AI-powered fraud detection to flag suspicious transactions. AI models analyze transaction patterns in real-time, identifying anomalies that human analysts might miss. Instead of replacing fraud investigators, AI enables them to focus on the most urgent cases.

AI Must Be Embedded Into Business Systems

AI’s impact is diminished when it operates in isolation. Siloed data, disconnected workflows, and fragmented systems prevent AI from delivering its full value. The most successful organizations integrate AI into the platforms employees already use, such as CRM systems, finance software, and customer support tools. Intelligently orchestrating these systems across the organization ensures that AI insights are easily accessible and immediately actionable.

For instance, AI-powered customer support tools, like ServiceNow and Jira Service Management, are used by Amazon and Salesforce to analyze customer inquiries in real-time and recommend responses based on previous interactions. This streamlines service delivery while maintaining human oversight, improving both speed and accuracy.

The key to success is phased integration. Instead of deploying AI across the entire organization at once, companies should focus on high-impact use cases first—areas where AI can deliver quick wins. Once teams see tangible benefits, broader adoption follows more naturally.

AI Can Work Even When Data Isn’t Perfect

Data quality is often cited as a barrier to AI adoption, but waiting for a flawless dataset can delay progress indefinitely. Many leading AI initiatives thrive despite incomplete or inconsistent data. The best approach is to deploy AI where it can add value while simultaneously improving data practices.

A prime example is Subtle Medical, which enhances medical imaging even with imperfect datasets. Their AI models improve image resolution and reduce scan times, demonstrating that AI can deliver measurable benefits despite data limitations.

Final Thoughts

AI adoption requires more than acquiring the right technology—it requires building a culture that enables AI to generate business value. Companies that embed AI into existing systems, integrate it with decision-making processes, and actively manage change see the greatest impact. By ensuring AI works alongside human expertise rather than attempting to replace it, organizations can achieve sustained improvements and unlock AI’s full potential.

Organizational Change That Works: A Smarter, Smoother Approach

Posted on February 6, 2025 by - Organizational Change & Culture

We all know businesses must continuously evolve to stay competitive. Yet, traditional approaches to organizational change often fail due to widespread disruption, internal resistance, and competing priorities. 

Research shows that as much as 88% of large-scale transformation initiatives do not achieve their intended results, often because they attempt to drive change too quickly and without the necessary alignment across teams. Organizations need a method that minimizes risk, delivers value quickly, and builds toward long-term success.

Guided Evolution offers a more effective path. Rather than pursuing sweeping overhauls that can destabilize an organization, this approach prioritizes incremental, adaptive improvements that align with the business’s strategic goals. By evolving in a controlled, intentional manner, companies can avoid the pitfalls of transformation fatigue and achieve sustainable success.

What is Guided Evolution?

Guided Evolution is a structured, step-by-step approach to change that reduces friction while accelerating value realization. Unlike traditional transformation efforts that attempt to overhaul entire systems at once, Guided Evolution enables organizations to implement meaningful, scalable improvements with minimal disruption.

This approach works because:

  • Changes are integrated into daily operations rather than introduced as abrupt shifts.
  • Incremental improvements build confidence and momentum across teams.
  • The organization continuously adapts to emerging needs rather than struggling through a single, large-scale transformation.

Achieving true enterprise-wide transformation is not just about modernizing individual workflows or integrating systems—it requires an orchestrated approach that optimizes how people, processes, and technology interact. Organizations that take a fragmented approach often experience inefficiencies, while those that evolve their Systems of Work, Systems of Insights, and Systems of Engagement in harmony are best positioned for long-term success.

Intelligent Orchestration: The Three Systems That Must Evolve Together

Change cannot happen in isolation. A truly effective transformation requires all three foundational systems within an organization to evolve in sync. Without coordination, isolated improvements in one area may create new inefficiencies elsewhere.

Guided Evolution ensures that transformation across these systems is deliberate and cohesive, reducing friction and maximizing impact.

System 1: Systems of Work (How the Organization Operates)

The way an organization operates—its workflows, tools, and processes—determines its efficiency and scalability. Many companies struggle with outdated systems and disjointed workflows that hinder productivity. Fragmented processes create inefficiencies, forcing employees to navigate multiple platforms or rely on manual workarounds that slow operations. 

For example, one study found that “70% of employees spend upwards of 20 hours a week chasing information across different technologies instead of doing their job.” Additionally, as businesses grow, scaling operations without a structured approach to workflow optimization becomes increasingly challenging, potentially costing organizations millions. 

Guided Evolution addresses these issues by introducing targeted automation initiatives that streamline workflows without overwhelming employees. Rather than attempting full-scale automation from the outset, businesses can begin by identifying the most inefficient processes and gradually implementing AI-driven enhancements. 

This phased integration allows teams to adjust at a manageable pace, increasing adoption rates and minimizing disruption. Cross-functional collaboration also improves as silos are gradually eliminated, making the transition toward optimized operations smoother and more sustainable.

System 2: Systems of Insights (How the Organization Makes Decisions)

Organizations thrive when they can make informed, data-driven decisions, yet many struggle with limited visibility, data inconsistencies, and decision-making bottlenecks. A lack of real-time insights prevents leaders from responding proactively to challenges, while siloed data makes it difficult to draw meaningful conclusions. When data remains fragmented across departments, translating insights into measurable actions becomes a cumbersome and often delayed process.

Guided Evolution helps overcome these challenges by first establishing a strong foundation for real-time insights. Implementing connected dashboards creates a unified source of truth, ensuring that decision-makers have access to accurate and timely data. 

From there, organizations can gradually apply predictive analytics to shift from reactive to proactive strategies, using historical patterns to anticipate future trends. 

Over time, AI-driven recommendations refine resource allocation and operational efficiencies, ensuring that insights lead directly to strategic improvements rather than remaining isolated reports with no clear action path.

System 3: Systems of Engagement (How the Organization Connects with People)

An organization’s ability to engage with employees and customers directly influences satisfaction, loyalty, and long-term success. However, many businesses struggle with disjointed engagement strategies that result in inconsistent experiences. 

Customers and employees alike expect seamless, personalized interactions—with one survey reporting that 82% of customers prefer chatbots over waiting for a representative—yet disconnected systems often create frustration. Manual processes further exacerbate the issue, slowing response times and preventing organizations from adapting to changing expectations.

Guided Evolution fosters stronger engagement by first focusing on high-impact, low-risk optimizations in customer service and employee workflows. By identifying areas where quick improvements can deliver immediate benefits, organizations build momentum for deeper transformation. 

AI-driven personalization can then be introduced in phases, allowing engagement strategies to evolve based on data rather than guesswork. Then, real-time feedback loops ensure that interactions remain relevant and continuously improve, reinforcing a dynamic engagement model that adapts to both customer and employee needs.

Why a Guided Approach to Change Matters

Large-scale transformation efforts often fail because they demand too much, too fast, leading to resistance and operational disruption. Guided Evolution provides an alternative—one that ensures sustainable change by making transitions manageable, measurable, and scalable.

Why This Works Better:

  • Reduces resistance by introducing more gradual shifts rather than radical disruptions.
  • Builds momentum through incremental wins that demonstrate value and ROI early in the process.
  • Creates a flexible framework that allows organizations to course-correct and refine their strategies as they evolve.

Example: A Realistic Path to AI-Driven Optimization

Rather than deploying AI-driven automation across the entire business in one sweeping initiative, organizations should start with the areas where automation can eliminate bottlenecks most effectively—such as IT workflows. Once success is demonstrated, AI-driven enhancements can expand into other areas, building trust and adoption across teams.

The Path Forward: Continuous Evolution

The ultimate goal of transformation is to create an enterprise where technology, processes, and people work in seamless coordination, all at the speed of change. However, this cannot be achieved overnight. The only way to get there is through intelligently orchestrated, step-by-step evolution across Systems of Work, Insights, and Engagement.

Organizations that embrace this guided approach to change will be better positioned to adapt, grow, and lead in the market of the future. The time to start is now.

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Biological Metaphors for Organizational Design: Learning from Natural Intelligence Frameworks

Posted on January 30, 2025 by - Organizational Change & Culture

Organizations, much like living organisms, exist in constantly changing environments. To survive and thrive, they must adapt, responding to new pressures, challenges, and opportunities. While traditional management models often emphasize rigid hierarchies and control mechanisms, nature provides a different blueprint—one built on adaptability, emergence, and distributed intelligence.

By studying biological systems, we can gain valuable insights into organizational design. The principles of evolution, self-organization, emergence, and distributed intelligence reveal pathways for creating adaptive, resilient enterprises. Just as ecosystems do not resist complexity but harness it for survival, organizations can rethink structure and strategy to embrace change as a competitive advantage.

The Parallel Between Biological Evolution and Organizational Adaptation

Evolution is not about the survival of the strongest but the survival of the most adaptable. In ecosystems, species find evolutionary niches—unique roles that ensure their survival. Likewise, organizations must continually refine their value propositions to carve out sustainable competitive advantages.

  • Biological Example: Darwin’s finches evolved distinct beak shapes based on available food sources, demonstrating that adaptability, rather than brute force, determines success.
  • Organizational Analogy: In the business world, companies that iterate, experiment, and pivot in response to market shifts are the ones that endure. Just as ecosystems foster diversity to sustain balance, organizations must cultivate innovation and learning to remain relevant.

This aligns with the idea of turning complexity into a competitive advantage rather than seeking to simplify it. Complexity can be an asset when managed correctly, enabling organizations to respond dynamically rather than reactively.

2. Principles of Emergence in Nature and Organizations

In nature, emergence occurs when simple interactions among individual components lead to complex, adaptive behavior. Ant colonies and schools of fish display remarkable coordination without central command.

  • Biological Example: In ant colonies, no single ant dictates the actions of the group. Instead, ants follow simple rules and respond to environmental cues, creating a sophisticated system that efficiently finds food, builds structures, and defends territory.
  • Organizational Application: When companies encourage decentralized decision-making, they enable emergent solutions that would be impossible under rigid, top-down control. Agile and Lean methodologies leverage this principle, allowing teams to self-organize and innovate in response to challenges.

Organizations that design for emergence rather than enforcing control can unlock new levels of agility and responsiveness.

3. Self-Organization: A Blueprint for Scalability and Resilience

Self-organization is a core feature of natural systems, where order arises through local interactions rather than central direction. This principle applies to everything from cellular structures to bird flocks in flight.

  • Biological Example: Flocks of birds exhibit coordinated movement patterns without a leader dictating direction. Each bird adjusts based on its neighbors, ensuring cohesion while maintaining flexibility.
  • Implication for Organizations: Enterprises can encourage autonomy while maintaining shared goals, much like how biological systems self-organize. Adaptive workflows, empowered teams, and flexible governance structures allow organizations to scale efficiently without losing coherence.

Rather than enforcing rigid operational models, organizations should create conditions where structure emerges naturally, balancing autonomy with alignment.

4. Distributed Intelligence: A Model for Collective Learning

Nature provides countless examples of distributed intelligence, where no single entity possesses all knowledge, yet the system as a whole functions adaptively.

  • Biological Example: Neural networks process vast amounts of information through distributed connections rather than a single command center. Similarly, fungal mycelial networks transfer nutrients and signals across vast forest ecosystems, enabling collective survival.
  • Organizational Application: Companies can foster distributed intelligence by democratizing data and empowering decision-making at all levels. Systems of Insight—where knowledge flows across teams rather than bottlenecking at the top—enable organizations to respond faster and more effectively to change.

By leveraging AI-driven analytics as an enterprise “nervous system,” and intelligently orchestrating the technology and processes required to support the strategy, organizations can process and react to internal and external stimuli dynamically.

5. Conceptual Models for Organizational Learning and Transformation

Just as genetic material encodes an organism’s traits, organizations carry an inherent DNA—a set of values, principles, and structures that shape their behavior.

  • Organizational DNA: Organizations that intentionally shape their culture, knowledge-sharing practices, and decision-making frameworks create a foundation for long-term adaptability.
  • Ecosystem Thinking: Organizations should be viewed as interconnected ecosystems where various functions interact symbiotically, not as isolated entities. Encouraging mutual support across departments strengthens resilience and innovation.
  • Guided Evolution: Change does not have to be disruptive. Evolution in nature occurs through gradual, iterative refinements. Organizations that experiment in small, controlled ways can drive meaningful transformation over time without destabilizing operations.

Many experts in organizational theory believe the “organization as organism” metaphor falls apart under conditions of continuous change. We believe this concept of guided evolution makes the difference. With expert guidance leading steady, iterative improvements, organizations can rise to the challenge of continuous change and even turn it into an advantage.

6. Actionable Insights for Leaders

Leaders seeking to build adaptive organizations can take key lessons from biology:

  • Adopt Adaptive Structures: Move from rigid hierarchies to flexible, intelligently orchestrated models that enable resilience.
  • Embed Systems Thinking: Recognize how different functions interact, ensuring alignment across people, processes, and technology.
  • Experiment and Iterate: Treat initiatives like evolutionary experiments—constantly learning, refining, and adapting based on results.

By embracing these principles, organizations can move beyond static models of operation and design structures that evolve naturally in response to the world around them.

Conclusion

Success in today’s world is about navigating change effectively. Stability is stagnation. Just as ecosystems thrive through adaptability, organizations that embrace biological principles—emergence, self-organization, and distributed intelligence—will be best positioned for long-term resilience and growth.

Sources:

Enabling ITSM Change Management Using Jira Service Management

Posted on August 9, 2023 by - Organizational Change & Culture, Service Management

In the fast-paced world of IT and software development, changes are inevitable. From software updates to infrastructure modifications, transitions can often lead to challenges and frustrations within an organization. But what if there was a way to manage these changes effectively, reducing the impact and scope of disruptions? Enter Jira Service Management (JSM), a powerful tool for enabling ITSM change management.

This is the first in a three-part series covering ITSM principles and applying them using JSM:

Change management is crucial in any organization. Without it, companies run the risk of encountering server downtimes, leading to confusion, stress, and frustration among employees and users alike. These downtimes not only affect productivity but can also tarnish a company’s reputation.

This article is based on the webinar, How to Enable Change Management With Jira Service Management. Watch the recording now to learn more about what’s discussed here and to see a thorough demo of JSM reflecting the key learning points. 

Unpacking the basic change management concepts 

The webinar linked above covered some important concepts every IT professional should know:

Change Management and Change Enablement

At the core of any IT operation lies the ability to manage and enable change effectively. But, what do these terms mean in the context of IT services and software development?

Change management, as defined by ITIL, is an Information Technology Service Management (ITSM) practice designed to minimize risks and disruptions. It ensures that critical systems and services remain functional amidst changes. This could mean anything from updating API documentation to deploying code to different environments. Any addition, modification, or removal that directly impacts services, processes, configurations, or documentation falls under this umbrella.

On the other hand, change enablement is a term used in Atlassian documentation. It refers to team standards that permit users to handle change requests effectively. Unlike change management, which is often associated with processing changes from outside, change enablement facilitates changes originating from within the organization.

Implementing change using ITIL 

It’s important not to rush the implementation of change. As counterintuitive as it might sound, taking extra time to set up and stick to a change management program can actually improve the process. It might seem to slow down work initially, but embracing ITIL patterns and automation will improve efficiency and reduce the heavy costs associated with botched tasks. The mantra here is to slow down to go fast.

Automation is a valuable tool for minimizing the burden of heavier tasks like documentation. Traditional tools may have complex, manual components that slow down processes and increase the chance of error. In contrast, tool automation can alleviate this heaviness. For example, automating ticket creation and linking various components can significantly reduce the time and effort required for these tasks.

Explore how AI-powered service management can take automation to a whole new level!

Roles and responsibilities in change management

Two key roles in change management are the Change Advisory Board (CAB) and the Release Manager.

Change Advisory Board (CAB)

The CAB plays a pivotal role in overseeing changes within an organization. Composed of senior individuals knowledgeable about the area undergoing change, the CAB provides a holistic perspective on the implications and potential impacts of proposed changes.

Release Manager

Working closely with the CAB is the Release Manager. This role involves reviewing content submitted by the development team, ensuring all aspects of a change request are in place, from documentation to testing assurances. The Release Manager serves as an agent to the CAB, mitigating risk through standardization and completion of requests.

In addition to their review responsibilities, the Release Manager coordinates the personnel involved in implementing changes, checks schedules for conflicts, tracks the process with the CAB, and ensures communication among all stakeholders.

The importance of timing in change management

However, effective change management isn’t just about having the right roles in place. It’s also about timing and planning. 

Respecting the process means submitting changes well before the release date. Common issues like time crunches for development and deployment can pose challenges to the change management process. To alleviate this, sufficient time should be allocated for change management processes during project planning. For example, incorporating an extra sprint for deployments could help manage changes more effectively.

Categorizing changes in a technology organization

Changes are categoric and can be differentiated based on size, risk, and urgency. Understanding these categories is crucial for efficient change management, particularly in a Continuous Integration/Continuous Deployment (CI/CD) setting.

There are three main types of changes:

  1. Standard Change: A low-risk, pre-authorized change that is well understood, fully documented, and proven. Due to CI/CD pipeline practices, standard changes are becoming more frequent.
  2. Normal Change: This refers to non-emergency deployments that must be scheduled and planned. These changes typically require a review from the Change Advisory Board (CAB)
  3. Emergency Change: These are changes that require immediate fixes due to an urgent issue. They often involve a separate procedure with a shorter timescale for approval and implementation.

Regardless of the type, no matter how small the change, it should not bypass the established process for change management. Each change must be properly documented, reviewed, and authorized to ensure minimal disruption to services and operations.

Moreover, understanding the nature of these categories and the associated efforts helps organizations manage changes efficiently. It provides clarity on the level of risk involved, the amount of effort required, and the urgency of the change.
Organizations may need to adjust internal policies based on the perceived risk level of each change. For instance, well-performing teams that have demonstrated their ability to manage risks effectively might be allowed to make production deployments multiple times per day.

Embracing ITSM change management in Jira Service Management

Effective change management strategies create a stable environment and help avoid panic-driven experiences. And at the heart of this strategy lies Jira Service Management.

JSM is a comprehensive tool that assists organizations in planning, controlling, and understanding the impact of changes on their business. It simplifies the change management process, from the initial change request to implementation.

With the ability to provide richer contextual information around changes, JSM empowers IT operations teams to better manage and mitigate potential disruptions. Furthermore, its customizable workflow—designed based on ITIL recommendations—helps service agents learn and adapt to change management processes. By implementing a change management process in JSM, companies can keep track of all changes, ensuring nothing slips through the cracks.

Jira Service Management’s alignment with ITIL 4 is one of its key strengths. This association allows it to offer a comprehensive solution that aligns with software development tools and agile practices, making it a favorite amongst software professionals.

This alignment with ITIL 4 makes ITSM change management in Jira Service Management less bulky than its predecessors and more adaptive to an agile mindset. This adaptivity is further enhanced by the free ITSM template within JSM. It includes change incident, new feature, problem, and service request issue types along with the corresponding request types, giving users a head start in their change management journey.

Additional customizable templates are available as well. 

The ease of use and familiarity of Jira Service Management reduces barriers to entry, making it approachable for professionals from the software side. It’s a tool designed to facilitate and not complicate, making it a go-to for many organizations seeking to streamline their change management processes.

Conclusion

In conclusion, the adoption of change management and change enablement practices, underpinned by ITIL patterns and automation, can bring about significant improvements in the efficiency and effectiveness of tasks within an organization. With tools like Jira Service Management, which aligns with ITIL 4 and supports agile practices, organizations can navigate changes smoothly, reducing the risk of disruptions and costly errors.

The journey towards effective change management may seem slow initially, but remember, slowing down to go fast can lead to long-term benefits. With the right tools and guidance, you can minimize risks, improve efficiency, and foster a culture that embraces change.

To dive deeper into how JSM can revolutionize your change management process, consider watching the recorded webinar, How to Enable Change Management With Jira Service Management. It offers practical insights and a demo that can help you understand the capabilities of Jira Service Management better.

Finally, Safe 6.0 Puts Continuous Learning Culture Where It Belongs

Posted on May 9, 2023 by - Agile & DevOps, Organizational Change & Culture

There is a real buzz in the SAFe community with the new changes in SAFe 6.0…

  • Strengthening the foundation for business agility
  • Empowering teams
  • Accelerating flow
  • Enhancing business agility across the business
  • Building the future with AI, Big Data, and Cloud, and
  • Delivering better outcomes with measure and grow and OKRs

Something that may have gotten lost with the buzz created by these themes is how the continuous learning culture competency has finally become part of the foundation for strengthening business agility.

For myself, this is a reminder that the continuous learning culture competency is not some afterthought or nice-to-have competency, but rather a foundational competency like Lean-Agile leadership. Why is this important?

SAFe 6.0 is about business agility

SAFe 6.0 is a framework for business agility. Business agility is the organizational capability for achieving economic advantage by sensing and responding faster than our competitors.

To do this, we need to learn faster than our competitors. And that means developing a continuous learning culture. Moving the continuous learning culture competency icon from its former side position in the framework to the foundation bar finally realizes the critical importance of continuous learning for business agility.

But sensing and responding to change is not just about learning and exploiting new business opportunities, as suggested by the Business Agility value stream. It’s also about sensing and responding to new opportunities to improve our way of working. Our ways of working must also embrace change through learning. Through iteration retrospectives, inspect-and-adapt problem-solving workshops, measure-and-grow workshops, and communities of practices, we have opportunities to improve our way of working iteratively. I often tell my clients if your way of working is the same two years from now as it is today, then you have missed the point.

Avoiding “cargo cult Agile”…

There is a misperception in much of our industry that if we can ritualistically execute a set of Agile practices, we will be agile. Thus, we often use the term “cargo cult Agile” to refer to this mindset.

No framework is immune to the cargo cult mindset. For example, SAI provides the Big Picture and a wealth of training assets and supporting resources that enable large organizations to get started on their business agility journey. Unfortunately, these same enabling assets which help start the journey can devolve into a cargo cult of prescriptive practices if the organizations do not develop a continuous learning competency. Worse, when leadership lacks a growth mindset, their go to strategy is often to strictly enforce compliance with the practices that make it easy to begin.

…by using SAFe as directed

SAFe roles, practices, and artifacts come from proven patterns for realizing Agile and Lean principles. There is nothing sacred about the practices themselves. They are practices to help you begin with realizing business agility throughout the enterprise. They are not rules. If you want to adjust, you simply need data to inform your decisions.

Improvement backlog items need to be written just like a feature with a benefit hypothesis. We need to know what useful performance and outcome data we can collect to determine if the improvement brought real benefit. Otherwise, when adapting and evolving practices, we are at risk of changing the practice because we don’t want to make the necessary changes to realize business agility. How many times have we heard teams complain that they can’t get anything done in a short timebox and ask if we can lengthen the timebox?

It comes back to continuous learning

This is why I am excited to see the continuous learning culture competency added to the foundation for business agility. Without developing both the Lean-Agile leadership and continuous learning culture, it is unlikely an organization can derive the benefits of business agility, regardless of how many practices they can precisely execute.

Dive deeper into all the changes in SAFe 6.0.

You Need Tools for Transformation–But That’s Not All

Posted on January 17, 2023 by - Organizational Change & Culture, Platform Adoption & Governance

I have something to admit: I was that coach. 

I told people I was a tool agnostic coach because I didn’t understand the value of leveraging the best workflow management tools. Luckily for me, I learned, and the results are cool. In this quick read, I’ll explain why tooling can be such a powerful force in your transformation, and discuss what to consider when deciding what technologies enable digital transformation for your organization.

The moment I learned something was missing

For decades, organizations in both the Manufacturing and Software industries have been working to transform to better ways of working. These transformations come in multiple forms:

  • Six Sigma / Lean
  • Agile ways of working
  • Business Agility
  • Push to a Product Organization
  • Digital Transformation
  • …and more 

I have spent much of my career successfully supporting organizations through this change—defining new ways of working, supporting new methods of communication and collaboration, and guiding people through change.

In recent years, how we look at these transformations have fundamentally changed.

  • Workers are tired of change. We need new behavior drivers to motivate the organization.
  • Organizations are sick of single solution frameworks and mindsets. They want change focused on outcomes.
  • The world has accepted that change is the new norm to stay ahead in the competitive market. If change is a competitive advantage, then we need to be mindful of what and why we change.
  • Leaders need data to make decisions. Due to the fast pace of change, more real time data is needed to support these decisions.
  • These days, transformations need to be more holistic, comprising Agile, Digital, and even AI.

Early in my career, I spoke to organizations about outcomes, but we never followed through to validate those outcomes. Looking back now, I see this approach resulted in half the benefit it should have. We did not validate those outcomes because we did not prioritize data and tooling as a competitive advantage during a transformation. Today, I’m convinced that implementing workflow management tooling as a part of your transformation is essential for success.

This being established, let’s dive into what this means and what you should consider when implementing a tooling strategy.

Start with why, define the approach, and validate the outcomes

Templates_Medium_black_coralAs a leader in organizational change, I often get calls that start by asking questions like

  • Can you turn us into a product-led organization?
  • Can you help us implement SAFe or Scrum at Scale?
  • Can you support our use of OKRs (Objectives and Key Results)? 

My answer… Yes, but why do you want to do this? Further, what pains are you facing and what outcomes are you hoping to achieve? Surprisingly, many organizations do not know the answers to these questions. And sometimes, when they cannot find the answer, they want to start over completely. But that won’t help.

They don’t know these answers because they do not have the data they need.

So, before we jump into a framework, let’s begin by

  • Creating a high-level process and data value stream to understand how things flow through the organization
  • Educating the organization’s executives and leaders on what is possible by establishing new ways of working
  • Establishing expected outcomes and key results for the organization
  • Setting the path with a roadmap for achieving those key results, not compliance with a framework
  • Setting a cadence to measure the key results so we can build in continuous  improvement

The key takeaway that fundamentally changed my approach to transformation is this: Organizations must establish the results they hope to achieve and enable change to reach those results, rather than deciding how they expect the organization to function and trying to force change into that frame.

Looking back, this seems so logical. We even thought and spoke about this years ago, but our efforts moved in another direction. I now believe that happened because the market did not have the flow-based tools we have today, and I would argue our modern tools still don’t offer the ideal state. 

So, which tool best enables this mindset?

The fact is, there is no perfect workflow management tool or framework. But the right tool can aid you in reaching your objectives. Here are some things I have found to be critical to understanding what tooling solution will best enable your transformation.

  1. Start by understanding the dashboards you need to validate the organizational results you’re after. Having the end in mind will allow the solution to remain focused on only building what the organization needs and filtering out unnecessary data. This will help you focus on the minimum feature set you need from your tooling solution.
  2. Establish systems and data that can serve as your single source of truth. There will be multiple tools and data being exchanged across the system. Ensure only the source of truth can be changed, and only by authorized users. This avoids people overriding the data intentionally or accidentally to tell a different story.
  3. Different users will want different tools based on what they do and how they do it. Do not assume that one tool can support OKRs, create and manage a product roadmap, manage user stories, and support your help desk. All those users have unique personas and will need unique features to do their work successfully. Pushing the output from those different tools to a single tool will support the organization’s source of truth.
  4. One tool will not magically solve your problems. Your organization is a large, complex, living thing. Multiple tools will need to come together to provide the insights needed. Establish an enterprise architecture for how tools will exchange data. Leveraging cloud-based tools and tool integration solutions will be essential to your enterprise solution.
  5. Do not forget about communication and collaboration. The tooling is not just about work management and flow-based systems. The users will need an integrated collaboration feature to support them, especially as they work more in a remote and hybrid mode.

In conclusion, don’t be that coach. Or, for that matter, that organization. Don’t ignore tooling as a vital piece of the transformation puzzle. And, at the same time, don’t focus on trying to find the perfect single tooling solution to solve all your problems. Instead, approach your transformation from the standpoint of objectives and results. And use the unique tool stack that best guides you to those results.