At its heart, the model divides all business activities into two buckets:

• Core: The things that make you you. These are the differentiating capabilities that give your organization a competitive edge. If your customers choose you over the competition for a specific reason, that reason is core.
• Context: Everything else. These are the necessary, often unavoidable, activities that keep the business running but don’t set you apart. Think payroll, IT support, HR administration, compliance, they’re all vital, none differentiating.

Moore’s advice was straightforward yet powerful: focus your internal energy on Core: nurture it, invest in it, protect it. Meanwhile, find efficient ways to handle Context often by outsourcing, automating, or standardizing it.

This mental model became a useful lens for leaders trying to decide what to build in-house, what to outsource, and where to invest limited resources. It helped make the invisible visible: just because a task is urgent doesn’t mean it’s strategic.

But here’s the twist: Moore’s framework assumed a world where humans (and their organizations) were the primary doers. Today, that assumption no longer holds.

The Rise of Agentic Work

Fast forward to today, and we’re in a radically different landscape. We’re not just automating tasks anymore; we’re handing off goals.

Thanks to advances in AI, especially the emergence of autonomous and semi-autonomous agents, we now have software that can plan, decide, and act with a surprising degree of independence. These aren’t just tools waiting for human input. They take initiative, respond to changing conditions, and in some cases, coordinate with each other or with humans to get things done. This capability is accelerating, especially with the rise of standard interaction protocols such as MCP.

Think AI agents that:

• Monitor customer feedback and open support tickets automatically
• Manage marketing campaigns, adjusting content and spend based on live results
• Research, draft, and summarize documents based on loose goals
• Analyze internal data to recommend (or even trigger) process improvements

These agents are crossing a boundary. They’re no longer just augmenting human work. They’re starting to own chunks of it.

Which brings us to the big strategic question:

Where do AI agents belong in the Core vs Context model?

Should they be treated like an outsourcing layer as a smarter, faster way to handle context work? Or can they play a role in the core contributing to the activities that differentiate us?

Or perhaps they’re something else entirely: a new kind of actor. A third pillar.

Updating the Model: Core, Context, and the Rise of Delegation

So how should we think about AI agents in the Core vs Context framework?

Here’s one way I believe we could evolve the model:

We keep the original distinction, but introduce a new layer of agentic involvement, considering the level of autonomy or collaboration model assumed by AI agents within the model’s quadrants.

1. Context Offloading (with Human-in-the-Loop)

Much of the “Context” category consists of essential but non-differentiating work such as generating reports, handling internal documentation, processing support tickets, chasing routine approvals ,etc.

These aren’t the areas where companies win in the market but they do take up time, and often a lot of human energy.

With AI agents, we have a new opportunity: offload the predictable parts of this work, while elevating the people currently doing it to higher-value roles.

Instead of running through a checklist manually, a team member becomes the one who monitors, validates, and refines the agent’s output.
Instead of writing reports from scratch, they ensure the generated version is accurate, contextualized, and aligned with business goals.

In short: humans aren’t being replaced , they’re being repositioned as supervisors and orchestrators of agentic workflows.

This shift opens up time and mental space for more strategic thinking, problem-solving, and cross-functional collaboration.

That said, it’s important not to treat this as a clean handoff. Not all context roles map neatly to agent supervision, and not every team will be ready culturally or structurally to adapt overnight. There’s real organizational inertia to overcome: process silos, misaligned incentives, and natural skepticism about “AI theater” (where automation is more optics than outcome). Offloading work to agents isn’t just a technical decision. it’s a change management challenge. It requires rethinking roles, retraining teams, and re-earning trust in how decisions are made and validated.

2. Core Augmentation (Human-Led, AI-Enabled)

In core work (the stuff that makes your business different) human creativity remains central. But AI agents can still play a powerful supporting role.

Here, agents act as:

• Research assistants who surface insights at speed
• Creative sparring partners who generate fresh angles or variations
• Task accelerators who take care of the groundwork so humans can focus on synthesis and strategy

Importantly, humans remain the authors of originality, the stewards of brand and culture, and the ones who connect dots in unexpected ways. AI isn’t replacing core work, it’s expanding what’s possible within it.

3. A New Strategic Lens

With this reframed model, you get a more adaptive map for the age of agentic work:

• Core → Human-led, agent-augmented
• Context → Agent-executed, human-supervised
• Your job as a leader → Constantly reevaluate what belongs where

Because here’s the catch: the boundary between core and context is fluid.
A task that’s context in one organization might be core in another — especially if AI is involved in a novel way. And what seems like a routine task today might become a source of differentiation tomorrow, if approached creatively.

The model still works. But now it demands ongoing attention, deliberate delegation, and a clear-eyed view of where humans add the most value.

So, What Now? Rethinking Work in an Agentic Era

Adapting the Core vs Context model for the age of AI agents isn’t just a strategic exercise; it’s an invitation to rethink how we design work itself.

Leaders should start asking new questions:

• What tasks are we doing today that could be supervised rather than executed?
• Where are our people bogged down by context and how might agents help them rise above it?
• How do we create space for our teams to lead creatively, not just operate efficiently?

And at the individual level:

• If part of your role feels like repeatable context, how could you evolve into the agent’s coach, validator, or integrator?
• If you’re doing core work, how might an agent push you into new creative territory?

This isn’t about humans vs. AI. It’s about humans with AI, working differently.

AI agents are the new interns, copilots, research aides, and systems thinkers. But we’re still the ones setting the goals, making the calls, and deciding what matters.

The companies that adapt fastest won’t be the ones that automate the most; they’ll be the ones that delegate the smartest, using agents to clear space for better thinking, faster learning, and more meaningful work.

Moore’s model helped us focus.
Now it could also help us evolve and incorporate agentic work shaping the (imminent)future of work.

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Let’s face it: the Agile Manifesto is starting to show its age. Since its inception in 2001, the agile movement has derived so much that it’s hard to tell what it even stands for anymore.

I’ve always believed the manifesto’s foundational ideas were helpful as a solid compass to navigate uncertainty and change. But with today’s wave of disruptive, emerging technologies blossoming everywhere, I’m starting to question the raw validity of some of its principles.

The focus on customer satisfaction, embracing change, and frequent delivery to gather feedback are still relevant today as they were in the early 2000s. But perhaps it’s time to to revisit some of these principles’ implications if we want to avoid missing the real edge opportunities that emerging tech brings.

Reframing the fundamentals

Let’s take a closer look at the first principle of the agile manifesto:

Individuals and interactions over processes and tools

Back in 2001, “individuals” meant the people in the room: human beings with ideas, values, prejudices, intuition and experience. Tools were, well, just the things those humans used to get the job done.

But more than 20 years later, something fundamental has changed:

We’re no longer working only with other humans.

We collaborate with AI agents. Assistants. Copilots. Autocomplete bots that don’t just suggest code, they write it. Systems that prioritize tasks, refactor architectures, even participate in incident response.

Which raises an uncomfortable but essential question for anyone still quoting the Agile Manifesto like scripture:

Are AI agents individuals or tools?

AI agents aren’t sentient (let’s make that assumption for the sake of staying on topic) but they are far from being passive actors. We’ve enabled them to respond, react, reason, optimize and sometimes make decisions on our behalf.

So when we interact with these AI-powered systems, should we treat those interactions like we treat human ones as valuable, worth optimizing, worth questioning?

Or do we continue to ignore them because, technically, we’re still “working with tools”?

Why This Matters for Teams

If we take the manifesto literally, we risk overlooking the most transformative change in how work gets done today.

How can we optimize the way we work if we do not value the interactions we do with AI agents and yet we leverage some decision-making on them? Here’s what happens when we undervalue the relevant interactions we have with non-human actors:

• We miss process gaps: AI agents might be involved in critical parts of the workflow (from triaging bugs to proposing solutions) but if they’re invisible in our conversations and workflow evaluation, we might miss the optimization oportunities. For example, if we have an AI agent that prioritizes product backlog items that is not taking into account user friction.
• We make accountability murky: If an AI-generated recommendation leads to a bug, who’s responsible?. For example, an AI code suggestion copilot adding a piece of code that causes an incident in production
• We fail to adapt our practices: Agile was born to help humans navigate complexity and change. But what if the biggest change is how we collaborate not only with people, but with increasingly capable systems?

The Agile Manifesto, in its original form, provided a great compass to navigate complexity while building software in the early 2000s. By adapting its principles to the realities of AI-powered collaboration, we can unlock the full potential of this technology while ensuring that human values and ethical considerations remain at the heart of our work.

The future of work is not about replacing individuals with AI.

It’s about empowering individuals to collaborate more effectively with AI to achieve extraordinary results.

That means re-embracing the spirit of inspecting, adapting to change, and continually striving to improve the way we work, together.

They may not be human, but they’re already part of the team.

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The AI wave is unstoppable. Like every major technological shift, it brings both a great promise and an overwhelming amount of hype. New AI-powered tools are popping everyday, each claiming to transform how we work, create and interact with each other. As exciting as it may be, it can also be confusing to jump on the hype train and we can lose sight of a more important question: how can we meaningfully collaborate with AI and make the best out of it in each case?.

As I’ve explored this rapidly changing world, both personally and professionally, I’ve found myself repeatedly asking: which tools are truly useful, and for what kinds of problems? Coming from a background where matching tools and people to context is paramount, I began to look past specific brands, products and buzz words and started asking myself: what are the underlying patterns in how we interact with AI?

Without being an expert nor doing extensive research, one can observe recurring modes of interaction on ways we integrate AI into our daily tasks and thinking processes.

To bring some clarity, I’ve identified five emerging patterns of Human-AI collaboration and came up with some interaction categorization. Thinking about AI through these lenses helps me cut through the noise, and assess the strengths and limitations of each tool, understand in which cases they can be applied and those where it doesn’t make sense and understand better how this partnership might evolve.

1. The Copilot Pattern: In-Workflow Assistant

The copilot is a very recognizable pattern and many tools already include it in their name. In this pattern, the AI is not a separate tool to consult but a partner embedded directly within your work environment.

A copilot has access to what you’re actively working on whether it’s a document you’re editing, an email you’re writing or the code you are developing and it can offer proactive context-aware assistance.

One example of this pattern is GitHub Copilot, which assists developers coding within their IDE of choice. When the developer starts writing the code, the tool gathers context and proactively suggests one or several lines of code to complete the task more efficiently.

Another use case that is pretty extended is Microsoft 365 Copilot, integrated into the Office 365 suite. It helps users draft email replies or summarize large documents.

The gist of a copilot model is a seamless integration that works alongside you, augmenting your abilities without having to break your focus.

2. The Expert Consultant Pattern: On-Demand Specialist

This pattern may be the most familiar way for most users to interact with AI. The expert consultant allows users to consult specific information regarding a topic or get guidance on how to do a specific task. Think of it as a way to query an external massive specialized knowledge base or service.

Unlike the copilot pattern, the interaction here is transactional and on-demand. The user has a need, then uses a tool (typically outside their main workflow) to ask the expert consultant for help. To get relevant results, the user must provide the relevant context in their query. Once the expert provides a response, it’s up to the user to integrate the output back into their work.

The most common and widespread example of this pattern is the use of chatbots like ChatGPT, Gemini or DeepSeek to get explanations, generate content or gather information on a specific topic.

Tools for AI image generation like MidJourney or Stable Diffusion would also fall under this pattern.

3. The Knowledge Synthesizer Pattern: Sense-Maker

The knowledge synthesizer pattern might seem similar to the expert consultant, but it tackles a different challenge: information overload and complexity. While the expert consultant is used to find answers for a specific question, the knowledge synthesizer helps users make sense of large, often unstructured and diverse sets of data.

Its core function isn’t just retrieving facts directly from a knowledge base, but analyzing, connecting, distilling and summarizing information to reveal hidden patterns, underlying trends and potentially find contradictions. In summary, the knowledge synthesizer helps users make sense of complex situations, boost research outcomes or get a holistic understanding without the time-consuming impact of doing it manually.

Examples of tools that rely on this pattern are sites scite.ai, which helps researchers identify connections between academic papers, and AlphaSense, used in business intelligence to extract insights from vast amounts of documents and reports from internal intelligence.

4. The Interaction Orchestrator Pattern: Facilitator

This interaction pattern may seem similar to the copilot, as both involve AI tools integrated into the user’s main workflow. However, the interaction orchestrator focuses on subtly, and often proactively, guiding the interactions of multiple participants. It focuses less on what the user is doing and more on how.

We can think of the interaction orchestrator as a conductor, director or skilled facilitator whose role is to ensure interactions are seamless, focused and effective. In this pattern, users rely on the AI to monitor, understand and manage the flow of the interactions to make the most of them. This could involve managing turn-taking in conversations, tracking participation to ensure all participants are active and all voices are heard, keeping the conversation on agenda or routing the conversation through different specialized agents in order to keep communication effective.

A common example of this is the AI-powered chat agents in customer support. Here, the AI tool models the interaction between a user and different actors depending on the user’s query, ranging from redirecting them to a page with the specific help, relaying them to another AI agent specialized in the topic of the query, or, eventually connecting them with a human customer support agent to attend their demands.

Another example could be an AI tool running alongside an online call (Microsoft 365 Copilot, for example) tracking speaking time to encourage balanced participation.

5. The Adaptive Coach Pattern: Personalized Tutor

This pattern is more forward-looking than the others described above. While the previous patterns focus on supporting the tasks or query you have at hand right now, the adaptive coach is designed to help you get better over time.

This pattern is primarily used for learning and skills development. The fundamental benefit lies in personalization and adaptation. The AI monitors the user’s progress, analyzes their input and identifies areas of improvement, dynamically adjusting its approach to how to support the user achieving improvements.

Many AI-powered fitness tools in the market follow this pattern. By tracking data such as food intake, exercise routines, weight and other health indicators, they adjust your exercising routines or meal plans to help you achieve your fitness goals.

Another example is language learning apps that make use of AI to make users proficient in a selected language (eg. Babbel, Duolingo, Talkpal). These apps not only provide the user with lessons and exercises, but also adjust them based on their identified weaknesses or strengths.

Making Sense of the Future of Collaboration

These five patterns aren’t necessarily rigid, mutually exclusive. A single AI tool might implement characteristics from multiple patterns. A Copilot, for instance, might subtly coach you on better coding practices over time, or an Expert Consultant might synthesize information before presenting it.

However, recognizing these distinct modes of interaction helped me kickstart this framework which I’m still extending and evolving. It helped me move beyond the general hype and ask more specific questions: How is this AI actually designed to work with me? Is it helping me do a task faster (Copilot)? Is it providing knowledge on demand (Expert Consultant)? Is it deepening my understanding (Knowledge Synthesizer)? Is it improving how we interact (Interaction Orchestrator)? Or is it helping me grow (Adaptive Coach)?

By understanding these underlying patterns, we can better evaluate the tools emerging, choose the ones that are better fit for our specific purpose and, hopefully, design more effective human-AI workflows.

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We’ve all been there, at least I know I have: The fear of failing. We expect to do things almost perfectly. In many professional environments, the word failure is often treated like a curse word. It’s associated with bad performance reviews, missed goals, and going-out-of-business. But what if I tell you that intentionally accepting failure could be the most effective trigger for enhancing teamwork, fostering innovation, and, ultimately, being successful?

Over the last few years, I’ve been learning and practicing Improv as a hobby, and the more I learned about it, the more I believed I could apply its fundamental principles to all facets of my life -work included- to get better results.

The liberating force of failure is one of the most profound things I’ve learned from the improv stage: it is not perceived as a loss; but as a learning opportunity, a chance to experiment, an open door to new creative paths and a great tool for building strong, cohesive and highly collaborative ensembles, ehem! teams.

Improv Principles on Failure and Experimenting

In traditional business settings, failure is often frowned upon. In improv, on the other hand, failure not only expected but also embraced. It’s often in the origin of the most imaginative, humorous, and captivating scenes.

Improv teaches that there are only opportunities, not mistakes. This simple mindset shift is the foundation of creating a team culture where risk-taking is encouraged, and experimentation is appreciated.

The Power of the Yes, And… principle: Every improv newbie learns the yes, and… as the cornerstone of the discipline. It is the rule of accepting any offer and augmenting it with a new one that builds on top of the original, making the story move forward.

This simple principle allows the group to build upon each other’s ideas, explore its potential, and often, discover new and unexpected possibilities. It’s the foundation of resilience and an open door for new opportunities.

Do-Not-Negate: In the spirit of building upon each other’s proposals, this is the counterpart of the yes, and… negating is seen as the blocker of exploring and develop new narratives, so it is discouraged to negate or dismiss the previously proposed ideas.

When a group is afraid of being judged or punished for making mistakes, they become risk averse, which hinders its flow and creativity. By discouraging the negation of one member’s ideas to impose another’s, psychological safety is reinforced.

Real-World Application: Cultivating a Culture of Experimentation

Consider the example of a software development team struggling with a new product launch. Instead of punishing the team for early bugs or negative user feedback, Martha, the team leader, embraced the principles of improv. She:

• Encouraged Open Communication: she created a safe space where team members felt comfortable sharing their concerns and suggestions.
• Re-framed Failure: She called early glitches “experiments” and invested time on investigating them and finding the learning opportunities from them to feed back into the backlog of future developments.
• Embraced Iteration: She tried to find a way to build features one step at a time and always building on top of what customers required -yes, and…?-, so she incorporated user feedback to make rapid adjustments and improvements as they go.
• Celebrated Growth: She recognized and rewarded the team’s resilience and willingness to learn from their mistakes.

The result? The team not only launched a successful product but also developed a more collaborative, innovative, and resilient culture.

Improv Exercises: Practical Tools for Embracing Failure

Here are some concrete improv exercises you can implement with your team to foster a culture of experimentation and resilience:

The basic “Yes, And…”:

Two people engage in a conversation where each statement must begin with “Yes, and…” before adding new information. This forces participants to accept what was said and build upon it rather than negating or redirecting.

This game encourages active listening, collaboration, and an open mindset for idea generation. It reinforces the habit of validating others’ contributions before adding your own.

In brainstorming sessions or meetings, adopt the Yes, and… approach to ensure ideas are expanded upon rather than immediately criticized. This fosters a culture of innovation and psychological safety.

“The Expert”:

Players take turns on the center of the stage acting as experts on a certain topic, the rest of the players will ask questions on the topic so they can be enlightened by the expert.

This game taps on two areas: one is developing people’s ability to ask for help and active listening and the other is the ability to formulate ideas with confidence and provide feedback. Also, by giving different people the opportunity to act as an expert, it may expose different or unexplored views on a subject.

This game could be helpful on discovery sessions or when trying to build personas that could be used to model the needs of a group of users for a given product.

“Conducted Story”

One person (the “conductor”) points to individuals in a group who must continue telling a story from where the last person left off. The goal is to have the players tell a story that moves seamlessly from one player to another.

This game develops adaptability, active listening skills, and the ability to quickly contribute to a shared narrative.

Use this game when co-creating presentations, project plans, or customer journeys. It ensures fluid team collaboration and helps everyone stay engaged in a shared objective.

“What’s Next?”

One person starts performing a simple action (e.g., writing an email). The next person says, “What’s next?” and the performer must add a new, logical next step (e.g., “I double-check the recipient”). This continues until the task is complete.

This game encourages process thinking, iteration, and structured improvisation.

This could be used for workflow mapping or process improvement sessions to break down complex projects into clear, actionable steps.

“Three Sentences Scene”

Players must build a scene that last no longer than three lines. At the conclusion of each the scene the basic elements should be present: who are the characters, what is their relationship and where they are located.

This game works on focus and conciseness, it helps the players to be specific and provide the required details in a short direct way so the others can complete with the missing information.

This game could be used to structure conversations where we need short, solid outcomes, for instance, when defining high-level goals.

The Path Forward: Cultivating Resilience & Innovation

Ready to lead a more resilient and innovative team? Start by:

• Reframing Your Mindset: Examine how you perceive failure. View mistakes as chances for learning and growth.
• Implementing Improv Exercises: Integrate the exercises above (or adapt them to suit your needs) into your team meetings and training sessions.
• Leading by Example: Set an example of the behavior you want to see in your team. Share your own mistakes and talk about what you learned from them.
• Creating a Safe Space: Encourage a culture of trust and psychological safety where team members feel comfortable freely expressing their ideas and taking risks.

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Eva loved Perita Brew. For over a decade, her café in sunny Málaga was more than just a place to grab a coffee; it was a community hub. Her team knew the regulars by name, remembered their intricate coffee orders (two extra drops of milk in Málaga can turn a sombra into a nube, and the locals are really serious about it!), and offered a friendly chat with every cup. The aroma of freshly brewed coffee mingled with laughter and lively conversations — a vibrant tapestry of human connection.

Then came the “modernization.” The business owners decided to introduce an ordering system driven by AI. The expectation was to boost efficiency: customers would order via app, a robotic espresso machine would make the drinks, and human baristas would become a thing of the past. A smooth, automated service with none of the human messy touch.

The reality was… a bit disappointing. The cozy, welcoming ambiance of the café worsened within days. Consumers complained that the AI couldn’t recall their preferences. It was a cardinal sin in Malaga for one poor soul to receive their “very hot sombra doble” in the wrong glass at temperatures close to volcanic. Additionally, when the inevitable mistakes happened, unhappy customers encountered a digital brick wall with no human available to listen to their concerns or provide a quick fix.

Instead of improving, wait times increased as staff had to invest their time troubleshooting AI mishaps. Eva’s team, once passionate and proactive, felt disempowered, reduced to technicians. The heart of Perita Brew, its human touch, had been torn away.

The Perita Brew Café story, is a familiar one despite the exaggeration. Too many companies, seduced by the hype of new & shiny tech, fall into the same trap: they treat it as a static, one-size-fits-all solution, neglecting the vital role that human collaboration plays.

This “automate everything” mindset results from a misinterpretation of artificial intelligence. Building more intelligent, responsive teams is more important than simply following trends or reducing expenses. AI is a tool, and like any tool, its usefulness depends on how it is applied. It is not a silver bullet.

The Power of Partnership: Human + AI

AI thrives in environments that embrace continuous learning and adaptation. Instead of massive, all-in investments, companies should start with small, real-world experiments. Consider AI a collaborator rather than a substitute. Here are some examples of how that collaboration thrives across many service sectors:

• Healthcare: AI algorithms analyze medical images, assisting radiologists in detecting diseases like cancer earlier and more accurately. But the final diagnosis rests with the experienced human doctor, who can interpret the AI’s findings in the context of the patient’s overall health. This collaboration leads to improved diagnostic accuracy and efficiency, ultimately saving lives. For example, PathAI uses AI to assist pathologists in cancer diagnosis, improving accuracy and reducing the time it takes to get results.
• Customer Service: AI-powered chatbots can handle routine inquiries, freeing up human agents to tackle complex issues and provide personalized support. Imagine a customer service team where AI handles the “easy” questions, allowing human agents to focus on building relationships and resolving challenging problems. Companies like Intercom are using this approach to improve customer satisfaction and agent efficiency.
• Hospitality: AI analyzes guest preferences to personalize future experiences, recommending restaurants or activities. Human concierges then use these insights to craft truly memorable stays. AI provides the data; humans provide the magic. Marriott, for example, uses AI to personalize offers and experiences for its guests.
• Education: AI platforms analyze student performance, identifying areas where they need extra support. Teachers use this information to tailor their instruction, providing personalized attention where it’s needed most. AI empowers teachers to be more effective, not replaces them. Khan Academy is one example of personalized learning powered by AI.
• Transportation: AI predicts when vehicles need maintenance, preventing breakdowns and improving safety. Human mechanics then perform the necessary repairs. AI identifies the problem; humans provide the solution. Companies like UPS use AI for predictive maintenance to optimize their fleet operations.

The AI-Ready Team: A Continuous Evolution

AI isn’t a one-time transformation; it’s a continuous evolution. Companies that view AI as a dynamic, learning system will outperform those who view it as a fixed technological investment. Businesses that create AI-ready teams — those that are quick, learning-focused, and collaborative — will win the race. Here’s how:

• Embrace Experimentation: Start small, test, learn, and iterate. Don’t be afraid to fail fast and adapt quickly.
• Focus on Human Augmentation: Identify tasks that AI can handle, freeing up humans to focus on higher-value activities.
• Invest in Training: Provide your teams with the skills they need to work effectively with AI tools.
• Foster a Culture of Collaboration: Encourage communication and collaboration between humans and AI systems.

AI is not a replacement for human ingenuity, empathy, and creativity. It’s a powerful tool that, when used thoughtfully, can amplify our strengths and create a better future for businesses, employees, and customers alike. The secret is to embrace AI as a collaborator in creating a more human-centered world rather than to be afraid of it.

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In the last three decades, adaptability has emerged as an essential engine of innovation and business growth. Organizations that have been able to pivot, adjust to market changes, and adopt new technologies have thrived in a dynamic and competitive environment.

In parallel, the value of soft skills such as effective communication, empathetic leadership, creativity, and collaboration has been widely recognized. These skills, which foster positive and productive work environments, have become a differentiating factor for companies seeking to attract and retain talent in the 21st century.

But what happens when Artificial Intelligence (AI) becomes a key player in the labor landscape? Are adaptability and soft skills still relevant in a world where interactions with virtual agents are increasingly common?

I believe the answer is a resounding yes. In fact, in an AI-driven work environment, adaptability and soft skills become even more crucial.

Adaptability: The key to navigating uncertainty

AI is rapidly transforming the job market, automating repetitive tasks and generating new roles and professions. In this context, the ability to learn new skills, adapt to changes, and reinvent oneself professionally is fundamental.

Adaptability allows us to collaborate effectively with AI, leveraging its capabilities to improve our productivity and efficiency. At the same time, it helps us identify opportunities where human creativity and critical thinking are indispensable.

Soft skills: The human touch in the age of AI

While AI can perform many tasks, the soft skills that make us uniquely human remain essential. Effective communication, empathetic leadership, creativity, and collaboration are skills that allow us to:

• Build strong relationships with our colleagues and clients, both human and virtual.
• Solve complex problems that require critical thinking and creativity.
• Lead teams effectively, inspiring and motivating others.
• Adapt to different work styles and collaborate effectively in diverse environments.
• Navigate the ethical implications of the application of AI requires the human touch providing an ethical framework that AI itself doesn’t have.
• Provide context for complex situations and dilemmas that require nuanced judgment that goes beyond simple programming or generative solutions.
• Oversight, accountability and responsibility. While AI can do the execution, in the scenario of a mistake, most organizations won’t be able to get away with blaming the AI, hence, we need some level of oversight and accountability to rely on human actors who understand the expected inputs, outputs and specially the outcomes of what the AI should produce.
• Critical thinking to identify issues or gaps in the AI-produced products & services and avoid them from deviating from the market (ever-changing) needs.

The future of work: A necessary collaboration between humans and AI

Instead of viewing AI as a threat, we must recognize its potential to complement our human skills. The collaboration between humans and AI will allow us to reach new goals and create innovative solutions that benefit society as a whole.

To thrive in the future of work, it is essential that we cultivate both our technical skills and our soft skills. Adaptability and soft skills will allow us to navigate uncertainty, collaborate effectively with AI, and bring our human touch to an increasingly digital world.

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In the world of digital transformation, navigating organizational culture is like solving a puzzle. A toxic culture can act as a formidable roadblock, voiding efforts to adopt better practices and principles that lead to improved outcomes, hindering collaboration, trust, and innovation.

In this blog post, we’ll delve into the issue of toxic culture and explore how the techniques of Culture Hacking,, as proposed by Management 3.0, can be the secret weapon in overcoming toxic vibes and turning transformation journeys into success stories.

Understanding Toxic Culture

Toxic culture within an organization is characterized by a set of detrimental behaviors and attitudes that can undermine the acceptance of core values for a successful transformation. Resistance to change, blame games, trust issues, micromanagement and weak psychological safety are among these toxic elements. They can leave teams and individuals demoralized and unmotivated, impacting the transformation desired positive outcomes.

Enter Culture Hacking, presented by Management 3.0. This approach presents a proactive take to get creative and reshape organizational culture in a change-positive direction. Here’s how it can address toxic culture:

• Breaking Resistance

Culture Hacking encourages experimentation and the introduction of small, incremental changes. By involving team members in proposing and testing these changes, resistance to Agile practices can gradually be broken down.

• Shifting Blame to Learning

Toxic cultures thrive on blame; Culture Hacking shifts the focus from blame to learning. The idea is to empower Teams to learn from mistakes, embrace continuous improvement, and share their insights openly.

• Rebuilding Trust

Trust, the key for functional teams and organizations. Culture Hacking lays the foundation for trust with open communication, and fostering psychological safety.

• Empowerment Over Micromanagement

Culture Hacking encourages self-organizing teams and autonomy. By giving teams the freedom to make decisions and take ownership, micromanagement tendencies can be replaced with trust in the expertise of team members.

Practical Steps for Culture Hacking

Ok, so what can we do to kickstart culture hacking within our organizations?

• Start Small

Pick a tiny corner of the culture that needs a makeover and throw in a quirky experiment. For example, introduce a new team ritual that promotes open feedback.

• Collect Feedback

Time for a reality check! Ask the team what they think about the changes. Is it a thumbs up or a meh? Actively seek feedback and keep refining.

• Celebrate Successes

Recognize and celebrate even small victories. This reinforces positive behaviors and encourages further experimentation

• Storytelling

Share the success stories! Turn experiences into captivating tales that inspire others to jump on the Culture Hacking bandwagon. Stories are powerful, so weave some magic.

• Leadership Support

Get the big guns on board! Culture Hacking works best when everyone, from the top brass to the ground crew, is in sync. Time to secure that leadership support and make Culture Hacking a company-wide jam.

Conclusion

Overcoming toxic culture in Agile transformation is a challenge worth embracing. With the tools and techniques of Culture Hacking, organizations can gradually shift away from toxic behaviors and foster a positive culture that supports everyone.

The only way out of toxic culture is to roll up our sleeves, harness the creativity of Culture Hacking, and steer our organizations toward a culture that thrives on agility, collaboration, and innovation. The journey may be challenging, but the rewards of a healthier, better and more humane workplace are well worth the effort.

This summer, Netflix premiered a live-action series adapted from the renowned manga and anime One Piece. The series follows the captivating adventures of Monkey D. Luffy, a young and charismatic aspiring pirate, widely recognized for his iconic straw hat.

Without giving away too many spoilers for those who may not be familiar with the story, it revolves around Luffy’s quest to locate a legendary treasure, the One Piece, which would also grant him the title of Pirate King in a fictional world teeming with, well… pirates.

Luffy is a character who gives high importance to values such as friendship, loyalty and the bonds he forms with his diverse crew, known as the Straw Hat Pirates. As they sail the seas, they face enemies and uncover mysteries along the way all in pursuit of their dreams and ultimately to find the elusive treasure.

It comes as no surprise that Luffy emerges as a leader both likable and reliable, captivating his crew and the series’ viewers alike. However, what specific traits and behaviors underlie his effective (and unconventional) leadership? Here is a glimpse of those qualities:

Effective Goal Communication (clearly & often)

In each episode, Luffy reaffirms his goal with the resounding phrase “I’m going to find the One Piece and will become the Pirate King”. This clear and consistent declaration serves as a beacon, providing direction toward the ultimate destination, even if the precise path remains unclear.

Empowerment and Trust

Luffy empowers his crew members to make decisions and take on leadership roles within the group. Recognizing each member’s unique expertise, he fosters an environment where individual strengths are valued and respected, allowing for optimal decision-making in specialized domains, be it thievery, navigation, or sword combat.

Leading by Example

Despite the diversity of his crew’s backgrounds, Luffy successfully instills core values of trust, respect, and camaraderie by exemplifying these principles through his actions. His commitment to these values highlights the relevance, not only for the team’s cohesion but also for achieving their collective goal.

Commitment to Growth

Luffy recognizes the intrinsic motivation of individuals and its impact on the group’s well-being. He actively invests in helping his crew members pursue their individual dreams, even if this entails embarking on alternate quests or facing additional adversaries or even part ways. His belief in the transformative power of personal growth underscores his dedication to the collective success of the crew.

Empathy and Active Listening

Luffy’s practice of actively listening to the concerns, ideas, and feedback of others allows him to exhibit empathy and compassion, even toward adversaries. This empathy serves as a valuable tool for evaluating diverse situations and challenges, offering insight into effective problem-solving and conflict resolution.

Adaptability

Luffy’s ultimate goal is matched by his adaptable approach. Rather than a fixed path, his journey consists of incremental steps that bring him closer to his ultimate objective while accommodating unforeseen setbacks or novel circumstances. Upon completing each side quest, he assesses his progress and sets new milestones.

Celebrating Successes

Fostering a cohesive and supportive team transcends goal setting and achievement. Celebrating successes, whether marking the end of a significant battle or other accomplishments, strengthens personal bonds among team members and enhances overall team morale.

Luffy’s awareness of the significance of celebrating successes is evident in his propensity to commemorate any achievement with grand feasts and parties for the entire crew.

In summary, leadership comes in various forms, and sometimes, the most unexpected individuals can teach us valuable lessons about what it means to lead effectively. Monkey D. Luffy may not fit the traditional mold of a leader, but his leadership style embodies effective servant leadership.

His selflessness, adaptability, and fearlessness in the face of adversity serve as a compelling example of how leadership can thrive outside conventional boundaries. Luffy’s journey to become the Pirate King may be a fictional adventure, but the leadership lessons he imparts are very much real and we can draw inspiration from his unique style to navigate the challenges of our dynamic world.

As Heraclitus said: “The only constant in life is change, " so often we have to figure out how to go from our current state to a future desired state. This article explains one way to facilitate a workshop to run this kind of gap analysis with a group and come up with a quick high-level plan to get to the desired state.

The story behind it

I was once asked to help one department to come up with a plan to evolve the role of the product owner as a part of a company-wide digital transformation initiative. The input for this conversation was very clear: the organization identified and shared 12 skills/behaviors as key for the Product Owner role, and the expectation was for each department to align and come up with a plan to master these skills and behaviors (or acquire them if missing) so they could own the evolution of their roles.

Considering the time constraints and thinking about how to give voice to everyone on a remote setup, I thought that a structure similar to the Decision Espresso format would be handy, so I designed a workshop to run a collective gap analysis and come up with an action plan to bridge the identified gaps on a single 1.5-hour session.

Here’s how to quickly facilitate a gap analysis conversation using this Express Decision Espresso workshop (pun intended) for a fully remote setup, however, the same workshop could be prepared for an on-premises using whiteboards and post-its.

The Express Decision Espresso for Gap Analysis

• First, set up a remote collaboration board with the expected skills/behaviors (or expected outcomes) [5 mins].

• After walking the participants through the desired future and clarifying any potential doubts, get them to do a collective self-assessment for the group on each of the items from the desired state box. Given we were assessing skills/behaviors, the proposed categories for the assessment were: Missing (for those skills totally absent within the group), Partially Achieved/Partially Present (for the ones who are not completely mastered by the group or not everyone within the group has it) and Totally Achieved/Totally Present [15 mins].

• After debriefing the assessment, get the participants to vote and decide on the top 3 or 4 critical items from the Missing and/or Partially Achieved section [5 mins].

• Rephrase the top voted items using the How Might We…? (HMW) format and give the participants some time to brainstorm. The point is to get them to propose concrete ideas or actions to bridge the gap between the current state and the desired state. (Tip: For this part, I used a 1–2–4-all structure and I suggested the participants to write their proposals starting with “We will…” to promote a call for action) [30 mins].

• After all the proposals have been presented, discuss and agree with the group on their relative priority using the MoSCoW method. Make sure you limit the number of items you can have in each category (and have fewer on the Must than on the Should) and note down if there are any dependencies between them [10 mins]

• Taking the MoSCoW classification, discuss and agree on what a reasonable timeline would be to start actioning the different proposals based on their relative priority [5 mins]

• Finally, build a tentative high-level roadmap emphasizing the goal for each of the proposed actions, review it and get an agreement to action it (plus a confidence vote) [10 mins].

Et voilá! Within an hour and a half, we came up with a high-level action plan to evolve the Product Owner role in line with the organization’s expectations. The only thing left was to start working on the plan and keep iterating over it to adjust it as needed.

TL;DR

This workshop was designed to quickly facilitate the gap analysis and find an action plan to bridge the identified gaps.

• Start presenting the list with the expected future (or outcomes).
• Get participants to self-assess their current reality against the future.
• Vote on the top items that are missing or partially missing from the current reality against the desired state.
• Allocate time to discuss and propose actions to bridge the gap for each of the top critical items identified.
• Prioritize the agreed actions using MoSCoW.
• Establish a high-level reasonable timeline based on the agreed priority.
• Formulate a tentative high-level roadmap putting the focus on the goal.
• Start working on the plan and iterate over it to adjust!

If you find this workshop structure useful I strongly suggest checking Matthew Croker’s article about the Decision Espresso for an alternative and more comprehensive approach to facilitate and enable quick decision-making.

For any questions about how to facilitate this workshop, feel free to contact me or leave a comment here.

Susan woke up that morning at 07:09 in her small apartment in Miami. She rubbed her eyes while glancing the earlier signs of sunlight rising above the bay through her window. After taking a sip of coffee, she grabbed a piece of paper where she wrote: 22 days from today, at this very same hour, hurricane Rose will touch land in Key Biscayne, from there, it’ll travel following a straight line until Naples for 3.75 hours over the everglades.

Let’s imagine for a brief moment that Susan is a meteorologist with 10+ years of experience studying hurricanes in Florida, would you still believe a prediction like the one above with such level of accuracy? Furthermore, would you take important life decisions based on it? Believe it or not, this is what we often do in Software Engineering.

It goes without saying that weather models and hurricane paths are very complex subjects with too many variables at play, so the best we can aim for is to get an approximation with what we know that is good enough. What some people often miss is that Software Development often belongs to the complex domain, so why do we pretend that we can predict timelines, costs and other aspects with total accuracy?

But, unsurprisingly, one of the first things someone will ask when considering hiring your services to develop something or buying your product to satisfy their needs is: “How much will it cost?” and “How long will I have to wait until I can use it?”, so we do need to somehow set expectations.

Mind you, that setting the wrong expectations with faux-overconfident guesses that you will likely miss will harm your current and future business. On the other hand, replying: “I don’t really know, I’ll call you once I’m done and then I’ll give you the price” will probably get you no business at all.

So how can we get this right?

Understand your ‘system’ and assess the risks

We are saying that Software Engineering tends to stand within the complex domain, which means that the ‘knowns’ are often unknown to us, so the best we can do is to make an informed guess in the form of a probabilistic forecast using the data we have available, similar to what meteorologist do to produce a hurricane path forecast. It is important to remember that a forecast acknowledges that there might be other possible outcomes apart from the one your are presenting, so if you do your maths right, together with the forecasted value you should also get the likelihood for it.

The key to forecast is to understand the odds you have to miss the desired outcome and to feel comfortable with it, if not, check your probabilities and try to find a forecasted value that comes with a lower risk.

Before running any numbers it is very important to understand the needs, capabilities and limitations of the system we have to create value. In software engineering, this means understanding and mapping the flow of value from the inception of the idea to the actual delivery of the feature/system/functionality.

Once we know all the different parties that are needed to get the work done and how they interact with each other, we can start gathering facts and data that we can use to validate our forecasts in the future and adjust when needed. It is also key to improve the internal flow of our system and make it more consistent, optimal and ‘predictable’.

Using the history of REAL outcomes we’ve observed in the past, we could run probabilistic simulations (ie a Montecarlo simulation) that would give us an approximation to what future outcomes could look like based on what we’ve observed and validated from previous experience. Don’t get me wrong, we are still talking about guesses and not facts, but rather than doing a random guess, at least we’d be narrowing the range of possibilities by fine-tuning them using empiric data.

TL;DR

We need to stop acting like we can do deterministic estimation in Software Engineering and we should all agree that given it’s often in the complex domain with high uncertainty/unknowns, we should embrace a probabilistic approach through forecasting.

We can do forecasts of possible outcomes by running probabilistic simulations using the REAL outcomes we’ve observed in the past. We need to understand how the work flows through our ‘system’ to generate value, and we should always observe and improve the internals of our system to optimise the flow.

Forecasts should always come with the probability of missing it, and we should pick the forecast we feel more comfortable with the chance we have of missing it.