Category Archives: Design

My Advice to Young Design Engineers

My Advice to Young Design Engineers

GUEST POST from Mike Shipulski

If your solution isn’t sold to a customer, you didn’t do your job. Find a friend in Marketing.

If your solution can’t be made by Manufacturing, you didn’t do your job. Find a friend in Manufacturing.

Reuse all you can, then be bold about trying one or two new things.

Broaden your horizons.

Before solving a problem, make sure you’re solving the right one.

Don’t add complexity. Instead, make it easy for your customers.

Learn the difference between renewable and non-renewable resources and learn how to design with the renewable ones.

Learn how to do a Life Cycle Assessment.

Learn to see functional coupling and design it out.

Be afraid but embrace uncertainty.

Learn how to communicate your ideas in simple ways. Jargon is a sign of weakness.

Before you can make sure you’re solving the right problem, you’ve got to know what problem you’re trying to solve.

Learn quickly by defining the tightest learning objective.

Don’t seek credit, seek solutions. Thrive, don’t strive.

Be afraid, and run toward the toughest problems.

Help people. That’s your job.

Image credit: 1 of 950+ FREE quote slides available at <a href=”http://misterinnovation.com” target=”_blank”>http://misterinnovation.com</a>

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

Why Context Engineering is the Next Frontier in AI

Why Context Engineering is the Next Frontier in AI

by Braden Kelley and Art Inteligencia

Observing the rapid evolution of artificial intelligence, one thing has become abundantly clear: while raw processing power and sophisticated algorithms are crucial, the true key to unlocking AI’s transformative potential lies in its ability to understand and leverage context. We’ve seen remarkable advancements in generative AI and machine learning, but these technologies often stumble when faced with the nuances of real-world situations. This is why I believe context engineering – the discipline of explicitly designing and managing the contextual information available to AI systems – is not just an optimization, but the next fundamental frontier in AI innovation.

Think about human intelligence. Our ability to understand language, make decisions, and solve problems is deeply rooted in our understanding of context. A single word can have multiple meanings depending on the sentence it’s used in. A request can be interpreted differently based on the relationship between the people involved or the situation at hand. For AI to truly augment human capabilities and integrate seamlessly into our lives, it needs a similar level of contextual awareness. Current AI models often operate on relatively narrow inputs, lacking the broader understanding of user intent, environmental factors, and historical interactions that humans take for granted. Context engineering aims to bridge this gap, moving AI from being a powerful but often brittle tool to a truly intelligent and adaptable partner.

In the realm of artificial intelligence, context engineering is the strategic and human-centered practice of providing an AI system with the relevant background information it needs to understand a query or situation accurately. It goes beyond simple prompt design by actively building and managing the comprehensive context that surrounds an interaction. This includes integrating historical data, user profiles, real-time environmental factors, and external knowledge sources, allowing the AI to move from a narrow, transactional understanding to a more holistic, human-like awareness. By engineering this context, we enable AI to produce more accurate, personalized, and genuinely useful responses, bridging the gap between a machine’s logic and the nuanced complexity of human communication and problem-solving.

The field of context engineering encompasses a range of techniques and strategies focused on providing AI systems with relevant and actionable context. This includes:

  • Prompt Engineering: Crafting detailed and context-rich prompts that guide AI models towards desired outputs.
  • Memory Management: Implementing mechanisms for AI to remember past interactions and use that history to inform current responses.
  • External Knowledge Integration: Connecting AI systems to external databases, APIs, and real-time data streams to provide up-to-date and relevant information.
  • User Profiling and Personalization: Leveraging data about individual users to tailor AI responses to their specific needs and preferences.
  • Situational Awareness: Incorporating real-world contextual cues, such as location, time of day, and user activity, to make AI more responsive to the current situation.

A Human-Centered Blueprint for Implementation

Implementing context engineering is not a one-time technical fix; it is a continuous, human-centered practice that must be embedded into your innovation lifecycle. To move beyond a static, one-size-fits-all model and create truly intelligent, context-aware AI, consider this blueprint for action:

  • Step 1: Start with the Human Context. Before you even think about data streams or algorithms, you must first deeply understand the human being you are serving. Conduct ethnographic research, user interviews, and journey mapping to identify what context is truly relevant to your users. What are their goals? What unspoken needs do they have? What external factors influence their decisions? The most valuable context often isn’t in a database—it’s in the real-world experiences and emotional states of your users.
  • Step 2: Map the Contextual Landscape. Once you understand the human context, you can begin to identify and integrate the necessary data. This involves creating a “contextual map” that connects the human need to the available data sources. For a customer service AI, this map would link a customer’s inquiry to their purchase history, recent support tickets, and even their browsing behavior on your website. For a medical AI, the map would link a patient’s symptoms to their genetic data, environmental exposure, and family medical history. This mapping process ensures that the AI’s inputs are directly tied to what matters most to the user.
  • Step 3: Build a Dynamic Feedback Loop. The context of a situation is constantly changing. A great context-aware AI is not a static system but a learning one. Implement a continuous feedback loop where human users can correct the AI’s understanding, provide additional information, and refine its responses. This “human-in-the-loop” approach is vital for ethical and accurate AI. It allows the system to learn from its mistakes and adapt to new, unforeseen contexts, ensuring its relevance and reliability over time.
  • Step 4: Prioritize Privacy and Ethical Guardrails. The more context you provide to an AI, the more critical it becomes to manage that information responsibly. From the outset, you must design for privacy, collecting only the data you absolutely need and ensuring it is stored and used in a secure and transparent manner. Establish clear ethical guardrails for how the AI uses and interprets contextual information, particularly for sensitive data. This is not just a regulatory requirement; it is a fundamental aspect of building trust with your users and ensuring that your AI serves humanity, rather than exploiting it.

By following these best practices, you can move beyond simple, reactive AI to a proactive, human-centered intelligence that understands the world not just as a collection of data points, but as a rich tapestry of interconnected context. This is the work that will define the next generation of AI and, in doing so, will fundamentally change how technology serves humanity.

Case Study 1: Improving Customer Service with Context-Aware AI Assistants

The Challenge: Generic and Frustrating Customer Service Chatbots

Many companies have implemented AI-powered chatbots to handle customer inquiries. However, these chatbots often struggle with complex or nuanced issues, leading to frustrating experiences for customers who have to repeat information or are given irrelevant answers. The lack of contextual awareness is a major limitation.

Context Engineering in Action:

A telecommunications company sought to improve its customer service chatbot by implementing robust context engineering. They integrated the chatbot with their CRM system, allowing it to access the customer’s purchase history, past interactions, and current account status. They also implemented memory management so the chatbot could retain information shared earlier in the conversation. Furthermore, they used prompt engineering to guide the chatbot to ask clarifying questions and to tailor its responses based on the specific product or service the customer was inquiring about. For example, if a customer asked about a billing issue, the chatbot could access their latest bill and provide specific details, rather than generic troubleshooting steps. It could also remember if the customer had contacted support recently for a related issue and take that into account.

The Impact:

The context-aware chatbot significantly improved customer satisfaction scores and reduced the number of inquiries that had to be escalated to human agents. Customers felt more understood and received more relevant and efficient support. The company also saw a decrease in customer churn. This case study highlights how context engineering can transform a basic AI tool into a valuable and helpful resource by enabling it to understand the customer’s individual situation and history.

Key Insight: By providing AI customer service assistants with access to relevant customer data and interaction history, companies can significantly enhance the quality and efficiency of support, leading to increased customer satisfaction and loyalty.

Case Study 2: Enhancing Medical Diagnosis with Contextual Patient Information

The Challenge: Over-reliance on Isolated Symptoms in AI Diagnostic Tools

AI is increasingly being used to assist medical professionals in diagnosing diseases. However, early AI diagnostic tools often focused primarily on analyzing individual symptoms in isolation, potentially missing crucial contextual information such as the patient’s medical history, lifestyle, environmental factors, and even subtle cues from their recent health records.

Context Engineering in Action:

A research hospital in the Pacific Northwest developed an AI-powered diagnostic tool for a specific type of rare disease. Recognizing the importance of context, they engineered the AI to integrate a wide range of patient data beyond just the presenting symptoms. This included the patient’s complete medical history (past illnesses, medications, allergies), family medical history, lifestyle information (diet, exercise, smoking habits), recent lab results, and even notes from previous doctor’s visits. The AI was also connected to relevant medical literature to understand the broader context of the disease and potential co-morbidities. By providing the AI with this rich contextual information, the researchers aimed to improve the accuracy and speed of diagnosis, especially in complex cases where isolated symptoms might be misleading.

The Impact:

The context-aware AI diagnostic tool demonstrated a significantly higher accuracy rate in identifying the rare disease compared to traditional methods and earlier AI models that lacked comprehensive contextual input. It was also able to flag potential risks and complications that might have been overlooked otherwise. This case study underscores the critical role of context engineering in high-stakes applications like medical diagnosis, where a holistic understanding of the patient’s situation can lead to more timely and effective treatments.

Key Insight: Context engineering, by enabling a holistic view of a patient’s health and history, is crucial for improving the accuracy and reliability of AI in critical fields like medical diagnosis.

The Future of AI is Contextual

The future of AI is not about building bigger models; it’s about building smarter ones. And a smarter AI is one that can understand and leverage the richness of context, just as humans do. From a human-centered perspective, context engineering is the practice that makes AI more useful, more reliable, and more deeply integrated into our lives in a way that truly helps us. By moving beyond simple prompts and isolated data points, we can create AI systems that are not just powerful tools, but truly intelligent and invaluable partners. The work of bridging the gap between isolated data and meaningful context is where the next great wave of AI innovation will emerge, and it is a task that will demand our full attention.

Image credit: Pexels

Content Authenticity Statement: The topic area and the key elements to focus on were decisions made by Braden Kelley, with help from Google Gemini to shape the article and create the illustrative case studies.

Subscribe to Human-Centered Change & Innovation WeeklySign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.

Building a Business with Feelings and Emotions

Building a Business with Feelings and Emotions

GUEST POST from Mike Shipulski

If you use your sane-and-rational lenses and the situation doesn’t make sense, that’s because the situation is not governed by sanity and rationality. Yet, even though there’s a mismatch between the system’s behavior and sane-and-rational, we still try to understand the system through the cloudy lenses of sanity and rationality.

Computer programs are sane and rational; Algorithms are sane and rational; Machines are sane and rational. Fixed inputs yield predicted outputs; If this, then that; Repeat the experiment and the results are repeated. In the cold domain of machines, computer programs and algorithms you may not like the output, but you’re not surprised by it.

But businesses are not run by computer programs, algorithms and machines. Businesses are run by people. And that’s why things aren’t always sane and rational in business.

Where computer programs blindly follow logic that’s coded into them, people follow their emotions. Where algorithms don’t decide what to do based on their emotional state, people do. And where machines aren’t afraid to try something new, people are.

When something doesn’t make sense to you, it’s because your assumptions about the underlying principles are wrong. If you see things that violate logic, it’s because logic isn’t the guiding principle. And if logic isn’t the guiding principle, the only other things that could be driving the irrationality are feelings and emotions. But if you think the solution is to make the irrational system behave rationally, be prepared to be perplexed and frustrated.

The underpinnings of management and leadership are thoughts, feelings and emotions. And, thoughts are governed by feelings and emotions. In that way, the currency of management and leadership is feelings and emotions.

If your first inclination is to figure out a situation using logic, don’t. Logic is for computers, and even that’s changing with deep learning. Business is about people. When in doubt, assess the feelings and emotions of the people involved. And once you understand their thoughts and feelings, you’ll know what to do.

Business isn’t about algorithms. Business is about people. And people respond based on their emotional state. If you want to be a good manager, focus on people’s feelings and emotions. And if you want to be a good leader, do the same.

Image credit: Pixabay

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

The Most Powerful Question

The Most Powerful Question

GUEST POST from Mike Shipulski

Artificial intelligence, 3D printing, robotics, autonomous cars – what do they have in common? In a word – learning.

Creativity, innovation and continuous improvement – what do they have in common? In a word – learning.

And what about lifelong personal development? Yup – learning.

Learning results when a system behaves differently than your mental model. And there four ways make a system behave differently. First, give new inputs to an existing system. Second, exercise an existing system in a new way (for example, slow it down or speed it up.) Third, modify elements of the existing system. And fourth, create a new system. Simply put, if you want a system to behave differently, you’ve got to change something. But if you want to learn, the system must respond differently than you predict.

If a new system performs exactly like you expect, it isn’t a new system. You’re not trying hard enough.

When your prediction is different than how the system actually behaves, that is called error. Your mental model was wrong and now, based on the new test results, it’s less wrong. From a learning perspective, that’s progress. But when companies want predictable results delivered on a predictable timeline, error is the last thing they want. Think about how crazy that is. A company wants predictable progress but rejects the very thing that generates the learning. Without error there can be no learning.

If you don’t predict the results before you run the test, there can be no learning.

It’s exciting to create a new system and put it through its paces. But it’s not real progress – it’s just activity. The valuable part, the progress part, comes only when you have the discipline to write down what you think will happen before you run the test. It’s not glamorous, but without prediction there can be no error.

If there is no trial, there can be no error. And without error, there can be no learning.

Let’s face it, companies don’t make it easy for people to try new things. People don’t try new things because they are afraid to be judged negatively if it “doesn’t work.” But what does it mean when something doesn’t work? It means the response of the new system is different than predicted. And you know what that’s called, right? It’s called learning.

When people are afraid to try new things, they are afraid to learn.

We have a language problem that we must all work to change. When you hear, “That didn’t work.”, say “Wow, that’s great learning.” When teams are told projects must be “on time, on spec and on budget”, ask the question, “Doesn’t that mean we don’t want them to learn?”

But, the whole dynamic can change with this one simple question – “What did you learn?” At every meeting, ask “What did you learn?” At every design review, ask “What did you learn?” At every lunch, ask “What did you learn?” Any time you interact with someone you care about, find a way to ask, “What did you learn?”

And by asking this simple question, the learning will take care of itself.

Image credit: Pixabay

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

Innovation is Unknowable Not Uncertain

Innovation is Unknowable Not Uncertain

GUEST POST from Mike Shipulski

Where’s the Marketing Brief? In product development, the Marketing team creates a document that defines who will buy the new product (the customer), what needs are satisfied by the new product and how the customer will use the new product. And Marketing team also uses their crystal ball to estimate the number of units the customers will buy, when they’ll buy it and how much they’ll pay. In theory, the Marketing Brief is finalized before the engineers start their work.

With innovation, there can be no Marketing Brief because there are no customers, no product and no technology to underpin it. And the needs the innovation will satisfy are unknowable because customers have not asked for the them, nor can the customer understand the innovation if you showed it to them. And how the customers will use the? That’s unknowable because, again, there are no customers and no customer needs. And how many will you sell and the sales price? Again, unknowable.

Where’s the Specification? In product development, the Marketing Brief is translated into a Specification that defines what the product must do and how much it will cost. To define what the product must do, the Specification defines a set of test protocols and their measurable results. And the minimum performance is defined as a percentage improvement over the test results of the existing product.

With innovation, there can be no Specification because there are no customers, no product, no technology and no business model. In that way, there can be no known test protocols and the minimum performance criteria are unknowable.

Where’s the Schedule? In product development, the tasks are defined, their sequence is defined and their completion dates are defined. Because the work has been done before, the schedule is a lot like the last one. Everyone knows the drill because they’ve done it before.

With innovation, there can be no schedule. The first task can be defined, but the second cannot because the second depends on the outcome of the first. If the first experiment is successful, the second step builds on the first. But if the first experiment is unsuccessful, the second must start from scratch. And if the customer likes the first prototype, the next step is clear. But if they don’t, it’s back to the drawing board. And the experiments feed the customer learning and the customer learning shapes the experiments.

Innovation is different than product development. And success in product development may work against you in innovation. If you’re doing innovation and you find yourself trying to lock things down, you may be misapplying your product development expertise. If you’re doing innovation and you find yourself trying to write a specification, you may be misapplying your product development expertise. And if you are doing innovation and find yourself trying to nail down a completion date, you are definitely misapplying your product development expertise.

With innovation, people say the work is uncertain, but to me that’s not the right word. To me, the work is unknowable. The customer is unknowable because the work hasn’t been done before. The specification is unknowable because there is nothing for comparison. And the schedule in unknowable because, again, the work hasn’t been done before.

To set expectations appropriately, say the innovation work is unknowable. You’ll likely get into a heated discuss with those who want demand a Marketing Brief, Specification and Schedule, but you’ll make the point that with innovation, the rules of product development don’t apply.

Image credit: Unsplash

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

Innovation or Not – SpinLaunch

Innovation or Not - SpinLaunch

GUEST POST from Art Inteligencia

In the fast-paced world of space exploration, innovation is a driving force that propels new companies and ideas into the spotlight. One such company is SpinLaunch, which is making waves with its novel approach to launching payloads into space. But what sets SpinLaunch apart, and how do we assess whether its approach is truly an innovation or not?

The Concept Behind SpinLaunch

SpinLaunch is taking a radically different approach to space launch by using a kinetic energy-based system rather than traditional rocketry. Their technique involves a high-speed rotating arm that builds up momentum and catapults a payload to the edge of space, drastically reducing the need for fuel and cutting down on costs. This approach is not only cost-effective but also environmentally friendly, addressing two significant pain points in the space industry.

Key Criteria for Innovation Assessment

  • Novelty: Is the concept fresh and previously unexplored?
  • Feasibility: Can the technology be realistically executed?
  • Impact: What benefits does the innovation provide to the industry and society?
  • Scalability: Can the idea grow and adapt to broader applications?

Case Study: Assessing SpinLaunch

Novelty

SpinLaunch undoubtedly introduces a novel approach to space launches. Traditional methods rely heavily on chemical propulsion. In contrast, SpinLaunch’s kinetic system stands out by leveraging physics in a way that hasn’t been commercially applied to space launches before.

Feasibility

The technical feasibility of SpinLaunch’s idea has been demonstrated through successful suborbital launches, proving that their kinetic system can indeed hurl payloads into space. However, the transition from suborbital to orbital flights will be the true test of feasibility. Critical engineering challenges remain, particularly related to the G-forces sustained by payloads during launch.

Impact

SpinLaunch has the potential to revolutionize the space industry by making launches significantly cheaper and more frequent. The environmental benefits of reducing fuel consumption cannot be understated either. If successfully scaled, the impact would reach beyond cost — it could democratize access to space.

Scalability

Currently, SpinLaunch is focused on small to medium-sized payloads. For scalability, the company must expand its capabilities to accommodate larger satellites and potentially human passengers. Adapting the technology for broader applications will be essential.

Conclusion: Is SpinLaunch an Innovation?

SpinLaunch exhibits the hallmarks of a true innovation. By addressing cost, environmental impact, and frequency of launches, it provides substantial benefits to the space industry. However, the road to demonstrating full potential is fraught with engineering and market challenges. Yet, the novelty and promise of their approach cannot be ignored.

Here is a 40 minute documentary that dives deep into the engineering, problem solving and innovation approach:

Opportunities for Expansion

To strengthen the case for SpinLaunch as an innovation, future assessments could involve the impact on related industries such as satellite manufacturing. More real-world data from further launches will offer insights into long-term feasibility and environmental impact. Engaging with regulators and potential partners early will be crucial to addressing scalability challenges.

Revision & Expansion

The ongoing journey of SpinLaunch should be closely monitored. As the company progresses, it should aim to address:

  • Risk Management: How can the company mitigate potential risks associated with high G-force impacts on sensitive equipment?
  • Regulatory Hurdles: Navigating international laws and space treaties will be essential as SpinLaunch aims for global reach.
  • Commercial Partnerships: Collaborations with established aerospace companies could fast-track development and market entry.

The future of SpinLaunch lies in its ability to resolve these emerging challenges while maintaining its innovative edge, positioning the company as a potential leader in transforming space access.

So, what do you think? Innovation or not?

Image credit: SpinLaunch

Subscribe to Human-Centered Change & Innovation WeeklySign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.

Effective Change Tactic Design Starts with Viable Targets

Effective Change Tactic Design Starts with Viable Targets

GUEST POST from Greg Satell

When we’re passionate about something, we want to take action. We want to launch an initiative, start a business, hit the streets, get stuff done. Yet our bias for action can be a trap that undermines—or even completely derail—our efforts. No matter what our intentions, actions without a sound strategy are doomed to fail.

Corporate change initiatives often start with a big kick-off campaign. These rarely convince anybody of anything, but can trigger opposition and kill the effort before it ever really gets started. People who feel strongly about social change often start by organizing a march. Yet marches are a very flawed tactic, vulnerable to sabotage and rarely achieve anything substantial.

Effective transformation strategy always involves mobilizing people to influence institutions. That’s where you start. Once you’ve determined what your strategy needs to be targeted at, you can begin to design potent tactics. There are time-tested tools that have proven out over decades that can help you do this. If you’re serious about change, you should learn them.

Mobilizing Constituencies

Much like a General maps the terrain upon which a military battle will be fought, the first step in designing effective tactics for a transformation initiative is to map the terrain upon which the battle for change will be fought. The tool that will help you do this is called the Spectrum of Allies, which provides a framework for classifying support and opposition.

Mobilizing Constituencies

In concept, mapping the Spectrum of Allies is a simple exercise. You merely classify who is most likely to be your most active allies, who will be supportive but more passive, who will be neutral, passively opposed and actively opposed. However, there are some nuances that take a little bit of effort to master.

First, it’s important to remember that these are targets for mobilization. In other words, they are groups of people that you want to get on board to actively work to influence institutions. Second, these are not individuals, but more like marketing personas. For example, in an educational initiative, parents, teachers and students are all groups you’ll want to mobilize.

There are a number of ways you can go about recruiting supporters. Many initiatives start simply by feeling people out in private conversations. An announcement in social media can sometimes be helpful as well. One strategy that we’ve seen be enormously effective in organizational initiatives is to hold workshops and see who stays behind after the session.

Every change effort is unique. We have found that even in similar initiatives in similar organizations that there were vast differences in the Spectrum of Allies. Here’s an example from a digital transformation initiative:

Spectrum of Allies

Once you’ve mapped the Spectrum of Allies and understand who are your targets for mobilization, you’re ready to move on to identifying your targets for influence.

Identifying Institutional Targets

While mobilizing people to your cause is important and necessary, it is far from sufficient. Just because an idea is popular, doesn’t mean that it will be implemented. In fact, it’s not uncommon for popular ideas to languish for years or even decades. To bring real change about you need to influence institutions that actually have power to enact change.

Think about an all powerful dictator, like Vladimir Putin or Kim Jong-un. They don’t need to pay much attention to popular opinion because they control all of the institutional power. If they were to lose control of those institutions, however, we could expect a huge change in the status quo! The tool we use to identify institutional targets is called the Pillars of Support.

Pillars of Support

In the pillar charts above, we can see three very different examples. Notice how the first, taken from our digital transformation initiative, could really apply to any type of organizational change. It is very context specific. The other two, focused on education and political change, are more generic, but would still vary slightly from case to case.

But look at each one for a minute. Think about how much change you could bring about in education if you could influence all of those institutions? Or how you could change a society if you could impact each one of those political pillars. Even in the organizational example, which is very specific, would be somewhat effective in many cases.

Evaluating Institutional Support

Once you’ve identified the institutional pillars that are relevant for your change effort, you will need to analyze each of them in terms of approachability and influence. Below is an example related to the same digital transformation initiative described above, where approachability and influence are rated on a five-point scale.

Evaluating Institutional Support

Once you begin to analyze the institutional pillars it becomes very clear that there are vast differences. HR leadership, for example, is very enthusiastic about digital transformation, while technology and product leadership are much more skeptical. Industry associations and media are enthusiastic, but not very influential. Customers and partners are fairly neutral.

These differences become even more clear once we chart them on a matrix.

Change Targets

Now that we have a good understanding of our targets, we are much better equipped to design tactics that are specifically designed for the people we need to mobilize and the institutions we need to influence.

Designing Tactics

When designing tactics, context is always key. That’s why we analyze the Spectrum of Allies and the Pillars of Support, so that we can understand the people involved and the forces at play. The annotated version of the Pillar Analysis Matrix below shows how we can use that understanding to guide our actions.

Pillar Analysis Matrix

In the upper right, “Leaders” quadrant, there are institutions that are both influential and approachable. We’ll want to design tactics that leverage their influence. The “Collaborators” in the lower-right quadrant don’t have as much influence as the “Leaders,” but may have resources we can leverage

On the left side of the matrix the institutions are less approachable. We’ll want to leverage shared values to help bring the influential “Blockers” into a more neutral position. We won’t really need to focus too much on the less influential “Holdouts,” but it may be worthwhile to address their fears in the hope that they will be less disruptive.

To see how this all works out, let’s return to our digital transformation example:

Digital Transformation Example

The first action is a hackathon, which is designed to mobilize the Yammer Group members to influence HR and product leadership. Because HR leadership is already supportive, we may want to work with that team exclusively until we can show some success and then leverage those results to win support (or at least neutrality) from product leadership.

The industry associations in our example aren’t super influential, but they are supportive, so it shouldn’t be too difficult to arrange a speaking slot for an executive sponsor which, if successful, could influence all stakeholders. Some best practice exchanges with customers and partners could help move the needle as well.

In practice, this analysis should be updated on a regular cadence (e.g. monthly or quarterly) and combined with OKR’s or KPI’s to track progress. Successful initiatives will often shift the terrain and open up new possibilities even as they render certain tactics less effective. We need to continually adapt to changing contexts.

One thing that I hope is clear by now is how much more effective it is to start with targets rather than tactics. We also find that the process goes much more smoothly when everyone involved has a common language and understanding of the terrain upon which the battle for change will be fought.

— Article courtesy of the Digital Tonto blog
— Image credits: Pixabay

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

Seeing Things as They Cannot Be

Seeing Things as They Cannot Be

GUEST POST from Mike Shipulski

When there’s a big problem, the first step is to define what’s causing it. To do that, based on an understanding of the physics, a sequence of events is proposed and then tested to see if it replicates the problem. In that way, the team must understand the system as it is before the problem can be solved.

Seeing Things as They Are

The same logic applies when it’s time to improve an existing product or service. The first thing to do is to see the system as it is. But seeing things as they are is difficult. We have a tendency to see things as we want them or to see them in ways that make us look good (or smart). Or, we see them in a way that justifies the improvements we already know we want to make.

To battle our biases and see things as they are, we use tools such as block diagrams to define the system as it is. The most important element of the block diagram is clarity. The first revision will be incorrect, but it must be clear and explicit. It must describe things in a way that creates a singular understanding of the system. The best block diagrams can be interpreted only one way. More strongly, if there’s ambiguity or lack of clarity, the thing has not yet risen to the level of a block diagram.

The block diagram evolves as the team converges on a single understanding of things as they are. And with a diagram of things as they are, a solution is readily defined and validated. If when tested the proposed solution makes the problem go away, it’s inferred that the team sees things as they are and the solution takes advantage of that understanding to make the problem go away.

Seeing Things as They May Be

Even whey the solution fixes the problem, the team really doesn’t know if they see things as they are. Really, all they know is they see things as they may be. Sure, the solution makes the problem go away, but it’s impossible to really know if the solution captures the physics of failure. When the system is large and has a lot of moving parts, the team cannot see things as they are, rather, they can only see the system as it may be. This is especially true if the system involves people, as people behave differently based on how they feel and what happened to them yesterday.

There’s inherent uncertainty when working with larger systems and systems that involve people. It’s not insurmountable, but you’ve got to acknowledge that your understanding of the system is less than perfect. If your company is used to solving small problems within small systems, there will be little tolerance for the inherent uncertainty and associated unpredictability (in time) of a solution. To help your company make the transition, replace the language of “seeing things as they are” with “seeing things as they may be.” The same diagnostic process applies, but since the understanding of the system is incomplete or wrong, the proposed solutions cannot not be pre-judged as “this will work” and “that won’t work.” You’ve got to be open to all potential solutions that don’t contradict the system as it may be. And you’ve got to be tolerant of the inherent unpredictability of the effort as a whole.

Seeing Things as They Could Be

To create something that doesn’t yet exist, something does things like never before, something altogether new, you’ve got to stand on top of your understanding of the system and jump off. Whether you see things as they are or as they may be, the new system will be different. It’s not about diagnosing the existing system; it’s about imagining the system as it could be. And there’s a paradox here. The better you understand the existing system, the more difficulty you’ll have imagining the new one. And, the more success the company has had with the system as it is, the more resistance you’ll feel when you try to make the system something it could be.

Seeing things as they could be takes courage – courage to obsolete your best work and courage to divest from success. The first one must be overcome first. Your body creates stress around the notion of making yourself look bad. If you can create something altogether better, why didn’t you do it last time? There’s a hit to the ego around making your best work look like it’s not all that good. But once you get over all that, you’ve earned the right to go to battle with your organization who is afraid to move away from the recipe responsible for all the profits generated over the last decade.

But don’t look at those fears as bad. Rather, look at them as indicators you’re working on something that could make a real difference. Your ego recognizes you’re working on something better and it sends fear into your veins. The organization recognizes you’re working on something that threatens the status quo and it does what it can to make you stop. You’re onto something. Keep going.

Seeing Things as They Can’t Be

This is rarified air. In this domain you must violate first principles. In this domain you’ve got to run experiments that everyone thinks are unreasonable, if not ill-informed. You must do the opposite. If your product is fast, your prototype must be the slowest. If the existing one is the heaviest, you must make the lightest. If your reputation is based on the highest functioning products, the new offering must do far less. If your offering requires trained operators, the new one must prevent operator involvement.

If your most seasoned Principal Engineer thinks it’s a good idea, you’re doing it wrong. You’ve got to propose an idea that makes the most experienced people throw something at you. You’ve got to suggest something so crazy they start foaming at the mouth. Your concepts must rip out their fillings. Where “seeing things as they could be” creates some organizational stress, “seeing things as they can’t be” creates earthquakes. If you’re not prepared to be fired, this is not the domain for you.

All four of these domains are valuable and have merit. And we need them all. If there’s one message it’s be clear which domain you’re working in. And if there’s a second message it’s explain to company leadership which domain you’re working in and set expectations on the level of uncertainty and unpredictability of that domain.

Image credits: Pexels

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

99% of Companies Failed to Do This Last Year

99% of Companies Failed to Do This Last Year

GUEST POST from Art Inteligencia

In today’s rapidly changing business landscape, one essential activity that 99% of companies failed to prioritize last year is conducting regular independent customer and employee experience audits. These audits are critical for understanding the current state and potential improvements needed to enhance engagement, loyalty, and satisfaction among customers and employees.

For most companies, customer and employee experiences are the backbone of their success. A business can’t thrive without satisfied customers buying their products or services, and employees are the driving force behind delivering these experiences. Despite this understanding, many businesses neglect the proactive steps necessary to evaluate and enrich these experiences systematically utilizing unbiased external third parties to walk the experiences and document friction points and opportunities.

Is your company part of the 99% that failed to conduct both an independent customer experience audit and an independent employee experience audit last year?

If you are part of the 1%, please be sure and leave some thoughts about the experience (no pun intended) in the comments!

Why Independent Experience Audits Matter

Independent experience audits are comprehensive reviews of interactions customers and employees have with a company performed by an unbiased external resource. They help identify pain points and opportunities for improvement. These audits should be performed regularly as they can reveal insights into:

  • The alignment between company offerings and customer needs.
  • The effectiveness of internal processes in promoting a positive work environment.
  • The coherence of brand values with actual customer and employee experiences.
  • Emerging trends and preferences that might impact future strategies.

“73% of customers are willing to pay more for a great customer experience.” – Temkin Group

Despite the apparent value proposition of these independent audits, why are so many companies still overlooking them? The constraints are often a mix of perceived complexity, lack of in-house expertise, or prioritization of immediate financial metrics over strategic insights. However, history has shown that organizations that adapt ahead of changes in expectations are better positioned to succeed over those that react out of necessity.

Case Study 1: An Overlooked Opportunity – Company X

Company X, a well-established retail brand, faced declining sales figures and employee turnover. Their product line remained strong, and pay scales were competitive. However, deeper insights revealed that customer experiences were inconsistent, and employees often felt disengaged due to a lack of communication and growth opportunities.

Recognizing the signs, Company X engaged in a comprehensive independent experience audit. The audit discovered two key issues:

  • Customer Experience: Customers reported a lack of personalization in their shopping journey, expressing frustration over disconnected in-store and online experiences.
  • Employee Experience: Employees felt unappreciated, with inadequate feedback channels and professional development options.

Armed with these insights, Company X implemented a strategy that enhanced personalized shopping experiences using AI-driven recommendations and integrated both digital and physical stores for seamless customer journeys. Simultaneously, they developed a robust internal communication framework that empowered employees through regular feedback and offered career progression pathways.

Within six months post-intervention, Company X witnessed a 15% increase in customer satisfaction scores and a 20% decrease in employee turnover—solidifying the importance of independent experience audits.

Case Study 2: A Success Story – Company Y

Company Y, on the other hand, already valued independent customer and employee experience audits as a vital component of their corporate strategy. As a result, they experienced steady growth and minimal churn rates despite operating in the highly competitive tech industry.

Company Y conducts bi-annual audits using a company like HCLTech, reviewing user interactions with their software products and collecting feedback through employee surveys intertwined with one-on-one interviews. They discovered that:

  • Customer Experience: The need for improved user interface intuitiveness was prevalent, prompting a user-centered design overhaul that optimized performance and usability.
  • Employee Experience: Although engagement levels were high, team collaboration across departments showed potential for enhancement.

By proactively addressing these issues, Company Y not only improved its software product, which increased customer retention by 25%, but also invested in team-building exercises and diversified project teams, leading to more innovative solutions and a dynamic organizational culture.

How to Implement Experience Audits in Your Organization

To avoid the common pitfalls highlighted, businesses need to incorporate independent experience audits into their regular strategic evaluations. Here’s a simplified approach to getting started:

  1. Define Objectives: Clearly identify what you aim to discover with the audit. Are you focusing on loyalty, satisfaction, efficiency, or a combination?
  2. Select a Partner: Choose an independent resource that is experienced, trustworthy and thorough in their activities to assess and document their findings as they walk the critical components of your customer and employee experiences.
  3. Gather Data: Utilize surveys, interviews, focus groups, and data analytics to collect comprehensive insights.
  4. Analyze Findings: Categorize feedback to identify consistent patterns, pain points, and potential areas for improvement.
  5. Develop an Action Plan: Prioritize issues by impact and feasibility, then devise a strategy that aligns with your company’s goals.
  6. Implement Changes: Address the identified opportunities with targeted interventions, ensuring stakeholders are engaged and informed.
  7. Measure Impact: Continuously track the effectiveness of changes and refine strategies as necessary.

Conclusion

Independent experience audits are not just a ‘nice to have’ but a strategic necessity. Companies can no longer afford to be complacent; they must take actionable insights from these audits to craft memorable and meaningful experiences for their customers and employees. Companies like Y that put independent experience audits at the heart of their strategy invariably found themselves robust against industry challenges, offering lessons that the broader business community should heed.

“Companies that excel at customer experience are 60% more profitable than their peers.” – Gartner

If you would like to engage an unbiased external person like Braden Kelley to conduct a customer experience and/or employee audit for you this year to join the 1% leapfrogging their competition, contact us!

Bottom line: Futurology is not fortune telling. Futurists use a scientific approach to create their deliverables, but a methodology and tools like those in FutureHacking™ can empower anyone to engage in futurology themselves.

Image credit: Pixabay

Subscribe to Human-Centered Change & Innovation WeeklySign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.

Uncertainty Isn’t Always Bad

Uncertainty Isn't Always Bad

GUEST POST from Mike Shipulski

If you think you understand what your customers want, you don’t.

If you’re developing a new product for new customers, you know less.

If you’re developing a new technology for a new product for new customers, you know even less.

If you think you know how much growth a new product will deliver, you don’t.

If that new product will serve new customers, you know less.

If that new product will require a new technology, you know even less.

If you have to choose between project A and B, you’ll choose the one that’s most like what you did last time.

If project A will change the game and B will grow sales by 5%, you’ll play the game you played last time.

If project A and B will serve new customers, you’ll change one of them to serve existing customers and do that one.

If you think you know how the market will respond to a new product, it won’t make much of a difference.

If you don’t know how the market will respond, you may be onto something.

If you don’t know which market the product will serve, there’s a chance to create a whole new one.

If you know how the market will respond, do something else.

When we have a choice between certainty and upside, the choice is certain.

When we choose certainty over upside, we forget that the up-starts will choose differently.

When we have a lot to lose, we chose certainty.

And once it’s lost, we start over and choose uncertainty.

Image credits: Pexels

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.