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Top 10 Human-Centered Change & Innovation Articles of September 2023

Top 10 Human-Centered Change & Innovation Articles of September 2023 Drum roll please…

At the beginning of each month, we will profile the ten articles from the previous month that generated the most traffic to Human-Centered Change & Innovation. Did your favorite make the cut?

But enough delay, here are September’s ten most popular innovation posts:

The Malcolm Gladwell Trap — by Greg Satell
Where People Go Wrong with Minimum Viable Products — by Greg Satell
Our People Metrics Are Broken — by Mike Shipulski
Why You Don’t Need An Innovation Portfolio — by Robyn Bolton
Do you have a fixed or growth mindset? — by Stefan Lindegaard
Building a Psychologically Safe Team — by David Burkus
Customer Wants and Needs Not the Same — by Shep Hyken
The Hard Problem of Consciousness is Not That Hard — by Geoffrey A. Moore
Great Coaches Do These Things — by Mike Shipulski
How Not to Get in Your Own Way — by Mike Shipulski

BONUS – Here are five more strong articles published in August that continue to resonate with people:

Three Maps to Innovation Success — by Robyn Bolton
Aligning Your Culture for Digital Transformation — by Geoffrey A. Moore
Who is to Blame for Poor Customer Service? — by Shep Hyken
3 Secrets To Good Teamwork — by David Burkus
Innovation Management ISO 56000 Series Explained — by Diana Porumboiu

If you’re not familiar with Human-Centered Change & Innovation, we publish 4-7 new articles every week built around innovation and transformation insights from our roster of contributing authors and ad hoc submissions from community members. Get the articles right in your Facebook, Twitter or Linkedin feeds too!

Have something to contribute?

Human-Centered Change & Innovation is open to contributions from any and all innovation and transformation professionals out there (practitioners, professors, researchers, consultants, authors, etc.) who have valuable human-centered change and innovation insights to share with everyone for the greater good. If you’d like to contribute, please contact me.

P.S. Here are our Top 40 Innovation Bloggers lists from the last three years:

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Measuring Employee Engagement Accurately

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Measuring Employee Engagement Accurately

GUEST POST from David Burkus

Employee engagement has been a hot topic for several decades. And for good reason. Business teams with highly engaged employees have a 59 percent lower turnover rate than those with less engaged staff. Highly engaged teams are 17 percent more productive. Engaged teams receive 10 percent higher customer reviews. And yes, businesses with engaged employees have higher profit margins than non-engaged competitors.

But getting employees to feel engaged is no small feat. Even how to measure employee engagement can be a difficult question to answer for many leaders. But there are good reasons to try. Measuring employee engagement helps identify cultural strengths for the organization. Done well measuring employee engagement builds trust through the company. And measuring employee engagement helps understand and respond to potential trends, both in the organization and across the industry.

In this article, we’ll outline how to measure employee engagement through the most commonly used method and offer the strengths and weaknesses of each method.

Surveys

The first method used to measure employee engagement is surveys. And this is also the most commonly used method as well—mostly for commercial reasons. After the Gallup Organization launched their original Q12 survey of engagement, dozens of competing companies with competing surveys sprung up all promising a different and better way to measure employee engagement. Most of these surveys present a series of statements and ask participants to rate how much they agree or disagree on a 5- or 7-point “Likert” scale. Some include a few open-ended questions as well.

The biggest strength of the survey method is that it scales easily. For an organization with hundreds or thousands of employees, emailing out a survey invitation and letting the system do the rest of the work saves a lot of time. In addition, surveys allow for objective comparisons between teams and divisions, or between the company and an industry benchmark. But while the comparisons may be objective, the data itself may not be. That’s the biggest weakness of surveys, they most often rely on self-reported data. And as a result, those taking the survey may not be completely honest, either because they want to feel more engaged or because they don’t trust the survey to be truly anonymous.

Proxies

The second method used to measure employee engagement is proxies—meaning other metrics that serve as a proxy for engagement. Because we know that employee engagement correlates to other measurements, we can assume a certain level of engagement based off those measurements. For example, productivity has a strong correlation to employee engagement when looking at teams or entire organizations. So, if productivity is high, it’s safe to assume employee engagement isn’t low. Likewise, absenteeism and turnover tend to rise as employee engagement falls, so changes over time on those metrics point to changes over time in engagement. (And comparisons between engagement in departments/teams can sometimes be made based on these proxies.)

The big strength of proxies is that they’re usually measurements that are already being captured. Larger organizations are already tracking productivity, turnover, and more and so the data are already there. The weaknesses of proxy measurements, however, are that they’re not a perfect correlation. It’s possible to be productive but not engaged, and there are often other reasons certain roles have higher turnover than others beyond employee engagement. In addition, some of these proxies are lagging indicators—if turnover is increasing than engagement has already fallen—and so they don’t provide leaders a chance to respond as fast.

Interviews

The third method used to measure employee engagement is interviews. And this method is the least common one but it’s growing in usage. Sometimes these are called “stay” interviews, in contrast to the exit interviews that are common practice in organizations. The idea is to regularly interview employees who are staying about how the company (and leaders) are doing and how things could be improved. While the questions used should provide some structure, the open-ended nature allows leaders to discover potentially unknown areas for improvement.

The biggest strength of stay interviews is that they’re a useful method for team leaders who may not have senior leader support for measuring engagement. Conducting stay interviews with ones’ team doesn’t require senior leadership approval or data from Human Resources. So, it’s available to leaders at all levels. And while that’s true, the weakness of stay interviews is that they’re hard to scale. Training thousands of managers on conducting a stay interview isn’t as easy as emailing out a survey. Moreover, because different managers would conduct these interviews differently, cross-comparison would be subject to bias. Stay interviews are a powerful way to measure engagement on a team, but they’re most potent when they’re used by managers who truly want the feedback their team provides (and not merely because they were told to conduct interviews).

Conclusion

While all three methods are a way to measure employee engagement, it’s not enough to merely measure. We measure things so we can improve them. So once the measurement is done, leaders need to have a plan in place make progress. That plan should include sharing out the results of the measurement and sharing the lessons learned from analyzing those results. In addition, leaders should share what changes are planned based on those lessons. And while it doesn’t need to be shared, it’s worth thinking ahead of time how the effects of those changes will be themselves be measured.

Done well, these measurements and the resulting plans will create an environment where everyone can do their best work ever.

Image credit: Pixabay

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Our People Metrics Are Broken

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Our People Metrics Are Broken

GUEST POST from Mike Shipulski

We get what we measure and, generally, we measure what’s easy to measure and not what will build a bridge to the right behavior.

Timeframe. If we measure people on a daily pitch, we get behavior that is maximized over eight hours. If a job will take nine hours, it won’t get done because the output metrics would suffer. It’s like a hundred-meter sprint race where the stopwatch measures output at one hundred meters. The sprinter spends all her energy sprinting one hundred meters and then collapses. There’s no credit for running further than one hundred meters, so they don’t. Have you ever seen a hundred-meter race where someone ran two hundred meters?

Do you want to sprint one hundred meters five days a week? If so, I hope you only need to run five hundred meters. Do you want to run twenty-five miles per week? If so, you should slow down and run five miles per day for five days. You can check in every day to see if the team needs help and measure their miles on Friday afternoon. And if you want the team to run six miles a day, well, you probably have to allocate some time during the week so they can get stronger, improve their running stride, and do preventative maintenance on their sneakers. For several weeks prior to running six miles a day, you’ve got to restrict their running to four miles a day so they have time to train. In that way, your measurement timeframe is months, not days.

Over what timeframe do you measure your people? And, how do you feel about that?

Control Volume. If you have a fish tank, that’s the control volume (CV) for the fish. If you have two fish tanks, you two control volumes – control volume 1 (CV1) and control volume 2 (CV2). With two control volumes, you can optimize each control volume independently. If tank 1 holds red fish and tank 2 holds blue fish, based on the number of fish in the tanks, you put the right amount of fish food in tank 1 for the red fish and the right amount in tank 2 for the blue fish. The red fish of CV1 live their lives and make baby fish using the food you put in CV1. And to measure their progress, you count the number of red fish in CV1 (tank 1). And it’s the same for the blue fish in CV2.

With the two CVs, you can dial in the recipe to grow the most red fish and dial in a different recipe to grow blue fish. But what if you don’t have enough food for both tanks? If you give more food to the blue fish and starve the red fish, the red fish will get angry and make fewer baby fish. And they will be envious of the blue fish. And, likely, the blue fish will gloat. When CV1 gets fewer resources than CV2, the fish notice.

But what if you want to make purple fish? That would require red fish to jump into the blue tank and even more food to shift from CV1 to CV2. Now the red fish in CV1 are really pissed. And though the red fish moved to tank 2 do their best to make purple guppies with the blue fish, neither color know how to make purple fish. They were never given the tools, time, and training to do this new work. And instead of making purple guppies, usually, they eat each other.

We measure our teams over short timeframes and then we’re dissatisfied when they can’t run marathons. It’s time to look inside and decide what you want. Do you want short-term performance or long-term performance? And, no, you can’t have both from the same team.

And we measure our teams on the output of their control volumes and yet ask them to cooperate and coordinate across teams. That doesn’t work because any effort spent to help another control volume comes at the expense of your own. And the fish know this. And we don’t give them the tools, time, and training to work across control volumes. And the fish know this, too.

Image credit: Unsplash

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The Innovation Dashboard

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Visualizing the Impact of Your People-First Approach

The Innovation Dashboard

GUEST POST from Art Inteligencia

In the relentless pursuit of progress, businesses often fall into the trap of measuring what’s easy, not what’s important. We meticulously track KPIs for revenue, efficiency, and market share, yet when it comes to innovation, our metrics often devolve into vague notions of “idea counts” or “project pipeline.” This is a fundamental flaw, especially for leaders committed to Human-Centered Change. To truly light your Innovation Bonfire, you need a different kind of visibility: an Innovation Dashboard that vividly illustrates the impact of your people-first approach.

Innovation isn’t a solitary act of genius; it’s a collective endeavor fueled by psychological safety, diverse perspectives, and empowered individuals. The challenge isn’t just to innovate, but to prove that investing in your people—their well-being, their ideas, their agency—is the most potent catalyst for breakthrough. This dashboard isn’t just about tracking ideas; it’s about visualizing human potential unleashed.

Beyond Output: Measuring Inputs and Outcomes

A truly effective Innovation Dashboard moves beyond simple output metrics (e.g., # of patents) to encompass both the inputs that foster innovation and the outcomes that demonstrate its impact on both people and profit:

1. Inputs: Cultivating the Innovation Environment

This section quantifies the health of your innovation ecosystem—the conditions that allow people to thrive and create. Key metrics here include:

Psychological Safety Index: Measured through anonymous surveys, pulse checks, or sentiment analysis, assessing how safe employees feel to speak up, challenge ideas, and take risks without fear of retribution. This is the bedrock of innovation.
Cross-Functional Collaboration Score: Tracking the frequency and effectiveness of interactions between different teams or departments, indicating how well ideas flow across silos.
“Purpose Alignment” Score: An internal measure of how well employees understand and connect with the organization’s overarching mission, ensuring innovation is guided by a shared “Why.”
Learning & Development Engagement: Tracking participation rates in skill-building workshops, hackathons, or knowledge-sharing sessions related to new technologies or methodologies.

2. Outputs & Outcomes: Impacting People and Performance

This section links the innovation efforts directly to tangible results, both for the business and for the people involved:

Employee-Generated Idea Conversion Rate: Tracking the percentage of employee-submitted ideas that move from concept to pilot, demonstrating a culture of action and feedback.
Time-to-Market for New Initiatives (Employee-Led): A measure of efficiency for innovations that originated from internal teams, highlighting agility.
Customer Satisfaction (CSAT) / Net Promoter Score (NPS) Impact from Innovations: Directly linking new products/services to improvements in customer experience metrics.
Employee Retention & Engagement for Innovators: Monitoring how well you retain and engage employees who are actively involved in innovation projects, recognizing that involvement often leads to higher satisfaction.
Revenue/Cost Savings Attributed to Innovation: Quantifying the financial impact of successful new offerings or process improvements.

Case Study 1: The “Engagement to Innovation” Link at a Tech Giant

A prominent technology company was struggling with innovation stagnation despite having a vast R&D budget. Their existing dashboards focused purely on project milestones and patent filings. Recognizing this flaw, the Chief People Officer partnered with the Head of Innovation to create a new, human-centric dashboard.

They started tracking “internal mobility” (movement between teams), “mentorship participation,” and crucially, a “Friction Score” derived from employee feedback channels, measuring systemic obstacles to creativity. They cross-referenced these with traditional innovation metrics. What they found was revelatory: teams with high psychological safety, frequent cross-functional exchanges, and low “Friction Scores” consistently produced higher-quality, market-ready innovations, even if they had fewer initial “ideas.”

The dashboard visually demonstrated that investing in employee well-being and psychological safety was a direct precursor to increased innovation output. This wasn’t just correlation; the data showed causation. It allowed leadership to justify a reallocation of resources from purely project-centric funding to culture-centric investments, proving that a robust internal ecosystem was their most powerful innovation engine. This led to a 15% increase in successful new product launches within two years, directly tied back to improved employee experience metrics.

Case Study 2: Designing for Impact in a Service Organization

I worked with a large, geographically dispersed service organization that needed to rapidly innovate its customer service model. Their initial approach was top-down, but it lacked traction. Human-Centered Design frameworks advocated for empowering front-line employees to drive solutions. To track this, we built a lean Innovation Dashboard focused on Employee-Led Solution Deployment.

Instead of just counting ideas, the dashboard visualized the journey of ideas from conception through pilot to full implementation. Key metrics included: “Time from Idea Submission to Pilot,” “Front-line Employee Participation Rate,” and “CSAT Impact of Employee-Led Solutions.” A critical visual component was a “Feedback Loop Health” indicator, showing how quickly and constructively ideas received feedback, reflecting the psychological safety to fail fast and learn.

The dashboard revealed that localized teams, given autonomy and rapid feedback, were prototyping and deploying solutions significantly faster than centralized initiatives. It highlighted specific branches and managers who were particularly effective at fostering internal innovation. This visibility allowed leadership to replicate best practices, provide targeted support, and, most importantly, celebrate the human architects of change. The result was a 10% improvement in first-call resolution and a significant jump in employee engagement for teams actively contributing to the innovation process.

“You cannot manage what you do not measure, but more importantly, you cannot inspire what you do not make visible. The Innovation Dashboard turns the intangible power of people into a strategic reality.”

Designing Your Impactful Dashboard

Creating your Innovation Dashboard is an exercise in Human-Centered Design itself. It should be:

Visually Intuitive: Easy to understand at a glance, with clear trends and actionable insights.
Balanced: Reflecting both the human inputs and the business outcomes.
Dynamic: Constantly updated and iterated based on what truly drives your organization’s innovation culture.
Empowering: Not just for executives, but for every team member to see their contribution and the collective progress.

By shifting your focus from simply tracking projects to visualizing the health of your innovation ecosystem and the impact of your empowered people, you provide not just data, but a compelling narrative. This Innovation Dashboard becomes a powerful tool for strategic decision-making, stakeholder alignment, and, most critically, for celebrating the human spirit that fuels all true progress.

Extra Extra: 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: Google Gemini

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How Do You Measure Power?

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How Do You Measure Power?

GUEST POST from Geoffrey A. Moore

In a recent blog, I argued that management needs to be accountable not only for delivering current performance but also for investing in power initiatives that will fuel future performance. Compensation systems that focus solely on the former too often result in a hollowing out of the enterprise, as we have seen with any number of iconic companies that have “performed” their way to the sidelines.

But this begs a key question—how do you measure power? Specifically, what kind of metrics could supply a stable foundation for management accountability and executive compensation?

In my book Escape Velocity, when discussing managing for shareholder value, we introduced a framework called the Hierarchy of Powers. The idea is that investors, who are buying a share of your enterprise’s future performance, value your company based on how much power they think it has relative to other investments they could be making. In this context, we claimed there were five classes of power that got evaluated in the following order of priority:

Category Power. Is your core business in a category that is growing, stable, or declining? This, we claimed, is the single biggest predictor of future performance.

Company Power. Within that category, where is your company in the pecking order of companies? If you are number one, that is a huge advantage. If you are number two, it also provides tailwinds. After that, there are no more tailwinds to be had.

Market Power. For companies that focus on one or more vertical markets, is your company the default choice for major prospects and customers in that segment? Wherever this is the case, it gives a material boost to your sales momentum and thus your company’s valuation.

Offer Power. Do you get preference and/or premium pricing due to the differentiation of your offer? Do you win the lion’s share of any competitive bake-offs?

Execution Power. Do you have a history of meeting or beating guidance on a consistent basis?

The model has stood up well over the years, but there is still the question of how to ensure accountability for investing in power when so much of our attention (and compensation) is focused on creating the next quarter’s performance. To that end, my colleague Philip Lay and I have been sorting through objective measures that signal material gains in power, ones that executive teams could readily track, and compensation programs could use to calibrate bonuses.

Here’s what we propose should be the top two metrics for each class of power:

Category Power. The focus here is on portfolio valuation—how many categories does the enterprise participate in, and how is each category faring. Meaningful changes in category power typically come through M&A, often supplementing organic innovation that is looking to scale quickly. Top two metrics for each category assessed:

Category Maturity Life Cycle status. The key stages are secular growth, cyclical growth, stagnant, and declining.
Technology Adoption Life Cycle status. This model focuses specifically on the period of secular growth, breaking it up into the following stages: Early Market, Chasm, Beachhead, Bowling Alley, Tornado, and Main Street. The two big valuation changers are winning a beachhead market segment in the Bowling Alley and participating with meaningful share in the Tornado.

Company Power. In high-growth categories, the focus is on bookings growth and competitive win rates. In mature categories, it is on the stability of the installed base as well as bargaining power both with suppliers and with customers. The top two metrics are:

Market share within each category. By far the most important metric, as market ecosystems organize around and give preference to the category leader.
Balanced mix of power and performance categories. For global enterprises, in particular, portfolio balance creates optionality to deal with both bull and bear markets.

Market Power. In emerging categories, dominating a target market segment, as opposed to merely participating in it, is critical to crossing the chasm and creating a sustainable franchise. In mature categories, target market segment focus is key to creating above-market growth. The top two metrics are:

Segment share. The most important metric because ecosystems that serve market segments organize around a segment leader only when it has dominant segment share.
Growth rates within target market segments. This is particularly important in any economic downturn that impacts different market segments to highly varying extents.

Offer Power. This metric and the next are closely aligned with delivering performance in the current fiscal year. That said, they still signal successful investments in power. The top two power metrics are:

Magic quadrant status. This is the most widely circulated third-party measure of offer power.
Win/loss record in head-to-head competitions. This is the most credible measure of offer power.

Execution Power. This really is the land of performance, but there is still power in reputation. Top two metrics are:

History of “meeting or beating” commits, be they forecast or, release dates. This is what gives confidence to customers and partners to give your team the nod.
Customer success metrics. These include Net Expansion Rate, Net Retention Rate, and Promoter Score, all of which validate that you are keeping your sales promises.

Guidelines for Using the Metrics

Metrics are a device to ensure visibility and accountability, and nowhere is this more important than when dealing with something as abstract as power. The key is to associate the right metrics with the right people, the ones who can have the most impact on the level of power in question. This works out as follows:

Top Executives: Category Power, Company Power. The two key levers here are using M&A to strategic advantage and using the annual budgeting process to allocate resources asymmetrically to achieve strategic objectives.

Middle Management: Market Power, Offer Power. The two key levers here are using market segmentation to strategic advantage and allocating the resources under your control asymmetrically to achieve dominant shares in target market segments.

Front Line: Execution Power. The key lever here is to align and focus the resources under your control or influence them in order to deliver the performance you have committed to.

For purposes of compensation, promotion, and overall alignment, these metrics align well with OKR objectives and can be used wherever OKRs are focused on increasing power. Again, the goal is not to replace performance metrics but rather to complement them.

That’s what Philip and I think. What do you think?

Image Credit: Unsplash

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Metrics for Purpose-Driven Innovation

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Measuring What Matters

Metrics for Purpose-Driven Innovation

GUEST POST from Art Inteligencia

In the innovation world, we often fall into the trap of measuring what is easy, not what is essential. We celebrate vanity metrics—the number of patents filed, the size of the R&D budget, or the raw number of ideas generated—while the true measures of impact, those tied to human value and organizational purpose, remain stubbornly abstract. As a human-centered change and innovation thought leader, I am here to argue that the way we measure innovation fundamentally dictates the kind of innovation we pursue. If your metrics are focused solely on short-term financial returns, you will stifle the kind of purpose-driven, deeply impactful innovation that drives long-term success and true societal change. Measuring what matters means placing human outcomes at the heart of your data strategy.

Purpose-driven innovation requires a shift from Output Metrics (e.g., number of projects launched, revenue from new products) to Outcome Metrics (e.g., reduction in customer effort, improvement in employee well-being, quantifiable social impact). The goal is to create a holistic measurement system that tracks not just the financial success of an innovation, but its measurable contribution to the company’s stated mission and its impact on the people it serves. This is about establishing a direct, measurable link between your innovation efforts and your commitment to a future that is not just more profitable, but more human-centered.

The Purpose-Driven Metrics Framework

To accurately measure purpose-driven innovation, leaders must look beyond the balance sheet and adopt a three-tiered framework that captures the human, organizational, and strategic value being created:

1. Human Impact Metrics (The “Heart”): These metrics quantify the change in user and employee experience. They are the strongest signal of purpose alignment. Examples include:
- Customer Effort Score (CES): Did the innovation make the customer’s life measurably easier?
- Well-being Index: How did the innovation impact employee stress, engagement, or capacity for deep work?
- Reduction in Friction: Quantifying the time or steps saved for the user/employee.
2. Learning & Agility Metrics (The “Mind”): These metrics track the efficiency and intelligence of the innovation pipeline itself, rewarding the behaviors that drive continuous change. Examples include:
- Failure Rate of Experiments: A *healthy* failure rate (e.g., 7 out of 10 ideas fail) shows the team is taking enough risks.
- Cycle Time Reduction: The time elapsed from ideation to testing.
- Innovation Literacy Score: A measure of how well employees understand and engage with the innovation process.
3. Purpose Alignment Metrics (The “Mission”): These metrics link innovation directly to the organization’s greater purpose, often encompassing Environmental, Social, and Governance (ESG) factors. Examples include:
- Resource Efficiency: Reduction in waste, water, or energy use per unit of output.
- Inclusion Score: Percentage of new products/services designed to explicitly serve previously underserved communities.
- Social Value Creation (SVC): A quantifiable measure of positive social impact tied to the innovation’s core function.

“What you measure is what you become. Measure only money, and you’ll create a short-sighted organization. Measure purpose, and you’ll create a resilient future.”

Case Study 1: Patagonia – Measuring Environmental Footprint as a Core Metric

The Challenge:

For decades, Patagonia’s core mission has been “Build the best product, cause no unnecessary harm, use business to inspire and implement solutions to the environmental crisis.” The challenge was how to measure the success of innovation—a new jacket, a revised supply chain—against this specific purpose, rather than just against sales figures.

The Purpose-Driven Solution:

Patagonia innovated its measurement system by making environmental and social impact metrics non-negotiable in the product development lifecycle. They treat their Footprint Chronicles — a detailed public record of the environmental and social impact of their products, from raw material to delivery — as a core innovation metric. For any new product or material, the innovation team is primarily measured on metrics such as:

Percentage of Recycled Content: Did the innovation increase the use of recycled or regenerative materials?
Reduction in Water/Energy Use: Did the new manufacturing process measurably decrease resource intensity?
Fair Trade Certification: Is the innovation elevating the social standard of the supply chain?

The financial success of the product is a secondary, supportive metric. The primary goal is to minimize environmental harm, making purpose the leading indicator for investment.

The Human-Centered Result:

By prioritizing Purpose Alignment Metrics, Patagonia consistently drives innovations like the use of recycled polyester, organic cotton, and radical supply chain transparency. This strategic alignment has fostered fierce customer loyalty and premium pricing, proving that measuring and achieving purpose is the most effective path to enduring financial success.

Case Study 2: Microsoft – Quantifying AI’s Impact on Employee Productivity and Well-being

The Challenge:

Microsoft’s massive investment in AI and tools like Copilot threatened to fall into the classic trap of only measuring adoption or revenue. The true innovation challenge was demonstrating that AI didn’t just automate tasks, but measurably improved the human experience of work — making employees more creative, more focused, and less burdened by “digital debt.”

The Purpose-Driven Solution:

Microsoft developed sophisticated Learning & Agility and Human Impact Metrics to quantify the value of AI in a human-centered way. They moved beyond simple usage rates to metrics like:

Focus Time Recovery: Quantifying the number of uninterrupted work hours AI tools helped to create.
Meeting Load Reduction: Measuring the percentage decrease in unnecessary or redundant meetings.
Cognitive Load Score (in internal studies): Measuring the perceived mental effort required to complete tasks before and after AI integration.

These metrics directly link the technological innovation of AI to the human outcome of enhanced well-being and creativity.

The Human-Centered Result:

By measuring the quality of life improvements, Microsoft ensures its AI innovations are human-centered by design. This strategy allows them to prove that the core value of their technology is not just in efficiency, but in empowering human potential — freeing up time and mental capacity for the uniquely human tasks of judgment, creativity, and empathy. The emphasis on these metrics guides their development teams to optimize for human outcomes, creating a powerful feedback loop for purpose-driven innovation.

Conclusion: The Moral Compass of Measurement

The innovation landscape is complex, but the path to meaningful, resilient growth is clear: Measure your purpose first, and the profits will follow. Your metrics are your moral compass. If you measure only financial return, you will only create financial products. If you measure social impact, employee empowerment, and environmental stewardship, you will create innovations that build a better, more resilient future for everyone.

Leaders must champion this shift, insisting that every new project, product, or pivot carries a dedicated set of Human Impact and Purpose Alignment Metrics. This commitment moves your organization beyond simple performance and into the realm of true significance, proving that the greatest innovations are those that measure and maximize the value they create for humanity.

Image credit: Pexels

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Using Leading and Lagging Indicators to Drive Your Business Forward

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You get what you measure, so make sure you’re tracking the right things.

Using Leading and Lagging Indicators to Drive Your Business Forward

GUEST POST from Soren Kaplan

I’ve seen a lot of organizations create strategies, programs, and projects focused on optimizing operations, streamlining processes, and driving innovation. Leadership teams put lots of energy coming up with the next big thing. But amazingly few teams think about how they’ll measure results. They may say they want revenue growth or cost savings, but that’s about the extent of it. Digging into the details by defining the specific metrics that will help track progress and forecast whether they’re going to achieve their goals in the future often gets neglected.

I’ve used this Key Performance Indicators template to address this challenge. Here’s the basis of why it’s important to use KPIs for your strategy and innovation initiatives, and how to use the template.

Strategy Without Successful Execution Is Just Brainstorming

Between developing strategy and executing it, there’s a step that requires creativity coupled with analytical thinking. It’s defining leading and lagging indicators. Many manufacturing companies and organizations that embrace Six Sigma know the importance of the metrics. Metrics help you quantify success, so you know when you’re achieving it and when you’re not.

Most companies focus on lagging indicators, like how much revenue they made in the last quarter, how many products they sold, or how many new customers they acquired. That’s important information, but those measures are obtained by looking in the rear-view mirror of what’s already happened. In addition to these things, you also need leading indicators to help you predict what will happen in the future. Here’s how to use both of these indicators to translate strategy into tangible implementation plans.

Leading Indicators Help You Predict the Future

Leading Indicators predict how you will perform in the future. They are more easily managed than lagging indicators but are harder to define. For example, if you’re looking to increase sales, you might measure the number of emails you send or sales calls you make. If you know that one in 10 calls results in a sale, the more contacts you make, the higher your sale forecast. Same goes for if you’re running a manufacturing organization. Leading Indicators for a manufacturing plant might include number of incidents that cause production slowdowns or the availability of specific materials in the supply chain.

Lagging Indicators Tell You How You Did

Lagging Indicators are easier to measure because they quantify what happened in the past. For example, a lagging indicator for sales would be measuring the number of products sold last month or number of new customers that signed up for a service. This information is usually easy to obtain and measure. Lagging Indicators are essential for charting progress but are not necessarily that helpful when looking at the inputs needed for achieving your overall desired results.

Create Your Dashboard

If you want innovation, reduced costs, and greater performance, you need to figure out how to do it, and what it looks like when you get it. Creating a set of lagging indicators gives you targets to achieve. But lagging indicators without leading indicators won’t provide focus around what to do–or early warning signals that things might be off track. If you’re manufacturing products, for example, if you’re not measuring whether your suppliers are delivering your materials on time, you might get surprised one day when you realize you don’t have the raw materials you need to achieve your manufacturing targets.

Here’s how to create a simple dashboard that contains both leading and lagging indicators:

Convene your team and identify the specific quantifiable targets that you need to achieve (your lagging indicators). Ask: What does success look like and how do we measure it?
Once you have your lagging indicators, define the inputs needed to achieve them. Ask: What specific things need to happen for us to achieve these targets and how do we measure those things? (your leading indicators)
With your lagging and leading indicators defined, use specific tools to gather and report on your data, whether a spreadsheet or online dashboard.

Management guru Peter Drucker once said, “What’s measured, improves.” If you want to improve your processes and business, figure out what you’re measuring. If you measure only the outputs (lagging indicators), your success will be far less predictable than if you’re also measuring the things that will get you where you want to go.

Image Credit: Praxie.com

This article was originally published on Inc.com and has been syndicated for this blog.

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Top 10 Human-Centered Change & Innovation Articles of October 2022

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Top 10 Human-Centered Change & Innovation Articles of October 2022 Drum roll please…

At the beginning of each month, we will profile the ten articles from the previous month that generated the most traffic to Human-Centered Change & Innovation. Did your favorite make the cut?

But enough delay, here are October’s ten most popular innovation posts:

Bridging the Gap Between Strategy and Reality — by Braden Kelley

How Do You Judge Innovation: Guilty or Innocent? — by Robyn Bolton

Scaling New Heights – Building Resilience — by Teresa Spangler

What Great Transformational Leaders Learn from Their Failures — by Greg Satell

Your Brand Isn’t the Problem — by Mike Shipulski

What’s Next – Through the Looking Glass — by Braden Kelley

Don’t Blame Quiet Quitting for a Broken Business Strategy — by Soren Kaplan

The Ways Inflection Points Define Our Future — by Greg Satell

How to Use TikTok for Marketing Your Business — by Shep Hyken

Making Innovation the Way We Do Business (easy as ABC) — by Robyn Bolton

BONUS – Here are five more strong articles published in September that continue to resonate with people:

What’s Next – The Only Way Forward is Through — by Braden Kelley

Four Things You Need to Succeed in The Good Place — by Robyn Bolton

You Can’t Innovate Without This One Thing — by Robyn Bolton

The Power of Praise — by Mike Shipulski

Importance of Measuring Your Organization’s Innovation Maturity — by Braden Kelley

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P.S. Here are our Top 40 Innovation Bloggers lists from the last two years:

Top 40 Innovation Bloggers of 2020

Top 40 Innovation Bloggers of 2021

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Top 10 Human-Centered Change & Innovation Articles of September 2022

Leave a reply

Drum roll please…

At the beginning of each month we will profile the ten articles from the previous month that generated the most traffic to Human-Centered Change & Innovation. Did your favorite make the cut?

But enough delay, here are September’s ten most popular innovation posts:

You Can’t Innovate Without This One Thing — by Robyn Bolton

Importance of Measuring Your Organization’s Innovation Maturity — by Braden Kelley

3 Ways to Get Customer Insights without Talking to Customers
— by Robyn Bolton

Four Lessons Learned from the Digital Revolution — by Greg Satell

Are You Hanging Your Chief Innovation Officer Out to Dry? — by Teresa Spangler

Why Good Job Interviews Don’t Lead to Good Job Performance — by Arlen Meyers, M.D.

Six Simple Growth Hacks for Startups — by Soren Kaplan

Why Diversity and Inclusion Are Entrepreneurial Competencies
— by Arlen Meyers, M.D.

The Seven P’s of Raising Money from Investors — by Arlen Meyers, M.D.

What’s Next – The Only Way Forward is Through — by Braden Kelley

BONUS – Here are five more strong articles published in August that continue to resonate with people:

How Consensus Kills Innovation — by Greg Satell

6 Ways to Leverage Virtual Tools to Create an Innovation Culture — by Soren Kaplan

Unlocking the Power of Cause and Effect — by Greg Satell

Driving the Next Era of Growth: Leveraging Data to Innovate — by Teresa Spangler

The Phoenix Checklist – Strategies for Innovation and Regeneration — by Teresa Spangler

Have something to contribute?

P.S. Here are our Top 40 Innovation Bloggers lists from the last two years:

Top 40 Innovation Bloggers of 2020

Top 40 Innovation Bloggers of 2021

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Quantifying the Value of Empathy and Collaboration

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The Untapped Metrics

Quantifying the Value of Empathy and Collaboration - The Untapped Metrics

GUEST POST from Chateau G Pato

In the data-driven world of modern business, we have become masterful at measuring the tangible. We track ROI, KPIs, and market share with an almost religious fervor. But what if the most powerful drivers of innovation and long-term success are the very things we struggle to quantify? This is the paradox of empathy and collaboration—they are the invisible forces that fuel human-centered innovation, yet they are rarely captured on a dashboard. It’s time to move beyond this oversight and develop a new framework for measuring what truly matters.

We’ve long held a bias toward what’s easy to count: revenue growth, cost reduction, and time-to-market. These metrics are important, but they only tell a part of the story. They measure the output of an organization, but they fail to capture the health of the engine—the human element. A company with high empathy and strong collaboration is an engine that is well-oiled, resilient, and primed for continuous innovation. A company without it is a machine running on fumes, prone to burnout, internal conflict, and a failure to connect with its customers.

The challenge lies in making the intangible tangible. We must develop a new set of metrics that allow us to gauge the strength of our human connections. This isn’t about replacing traditional business metrics; it’s about complementing them with a deeper understanding of the organizational and cultural health that underpins all successful change. By actively measuring and managing the soft skills that drive hard results, we can create a more powerful and sustainable innovation culture. The metrics we need to tap into include:

Empathy Quotient (EQ) Scores: Measuring the ability of teams to truly understand and feel the customer’s experience. This can be done through surveys, observational studies, and qualitative feedback.
Collaboration Velocity: Tracking the speed and effectiveness with which diverse teams can come together to solve a problem. This involves analyzing communication patterns, project handoffs, and feedback loops.
Psychological Safety Index: Gauging whether employees feel safe to take risks, voice dissenting opinions, and admit mistakes without fear of retribution. This is foundational for a truly innovative culture.
Customer Experience (CX) Depth: Moving beyond simple satisfaction scores to understand the emotional journey of the customer and the depth of their connection to your brand.
Cross-Functional Innovation Rate: Measuring the percentage of successful innovations that originated from collaboration between different departments or teams.

Case Study 1: The Healthcare Innovator and Empathy as a Metric

The Challenge: A Disconnected Patient Experience

A large hospital system was struggling with declining patient satisfaction scores, even though their clinical outcomes were excellent. The data showed that patients felt disconnected and unheard during their visits. The problem wasn’t a lack of medical expertise, but a lack of empathy in the patient-facing process. The organizational culture was focused on efficiency and procedures, with little attention paid to the emotional experience of the patient.

The New Metric and Innovation:

The hospital’s leadership team, in a human-centered change initiative, decided to make **Empathy** a core metric. They created an “Empathy Index” by integrating a new set of questions into patient surveys, focusing on qualitative feedback about how they were listened to and how well their concerns were addressed. They also conducted observational studies to see how staff interacted with patients in real-time. This new metric, along with qualitative feedback, led to a simple but profound innovation: the “Patient Story” program. Staff meetings and training sessions were no longer just about protocols; they began with a personal story from a patient or a family member, reminding the staff of the human impact of their work. Furthermore, they launched a “Listening Skills” training program, explicitly teaching doctors and nurses how to actively listen and respond with empathy.

The Results:

Within a year, the hospital’s patient satisfaction scores saw a dramatic turnaround. The Empathy Index showed a significant increase, and the qualitative feedback was overwhelmingly positive. By making empathy a measurable and celebrated metric, the hospital shifted its culture, leading to a more connected patient experience and, ultimately, better health outcomes. It proved that a soft skill could drive hard, measurable business results.

Key Insight: By creating a quantifiable metric for empathy, organizations can drive cultural and behavioral changes that lead to significant improvements in customer experience and business results.

Case Study 2: The Tech Giant’s Collaboration Velocity

The Challenge: Siloed Innovation and Slow Development

A leading technology company was an acknowledged innovator, but its sheer size had created a problem: its teams were working in silos. A new product idea would often get bogged down as it moved from engineering to marketing to sales, with each department operating on its own timeline and with its own metrics. The result was a slow, inefficient development cycle and a high percentage of promising projects being abandoned due to a lack of cross-functional alignment.

The New Metric and Innovation:

The company’s leadership team recognized that a lack of collaboration was their biggest barrier to growth. They introduced a new metric: **Collaboration Velocity**, which measured the speed at which cross-functional teams could move a project from ideation to launch. They tracked the number of inter-departmental meetings, the frequency of cross-team knowledge sharing, and the speed of project handoffs. This data revealed the key bottlenecks. As an innovation, they introduced a “Fusion Team” model. Instead of having a project move sequentially through departments, a small, multi-disciplinary team with representatives from engineering, design, and marketing was assigned to a project from day one, with shared goals and metrics. Furthermore, they used a “Project Pulse” tool to track the sentiment and psychological safety within these teams, ensuring the collaboration was healthy and productive.

The Results:

The results were immediate and impactful. The company’s Collaboration Velocity improved by over 40% in the first year. The Fusion Teams were able to launch new products in half the time of the traditional model, with far greater internal alignment and market success. The company’s overall innovation output increased, and the new metric gave leaders a clear, data-driven way to prove the value of breaking down silos and investing in collaborative team structures. The intangible value of collaboration became a powerful, measurable driver of competitive advantage.

Key Insight: Measuring the health and speed of collaboration provides a clear path to breaking down organizational silos and accelerating the pace of innovation.

The Path Forward: A New Era of Measurement

The future of innovation belongs to those who are brave enough to expand their definition of what can be measured. We must stop treating empathy and collaboration as unquantifiable “soft skills” and start seeing them as the strategic, measurable assets they truly are. By developing and integrating these new metrics into our dashboards, we are not just adding to our data; we are gaining a richer, more holistic understanding of our organizational health. This allows us to make more informed decisions, nurture a culture of trust and psychological safety, and, most importantly, build a more resilient and human-centered engine for continuous innovation. It’s time to stop flying blind and start quantifying the forces that are truly driving us forward.

Extra Extra: Because innovation is all about change, Braden Kelley’s human-centered change methodology and tools are the best way to plan and execute the changes necessary to support your innovation and transformation efforts — all while literally getting everyone all on the same page for change. Find out more about the methodology and tools, including the book Charting Change by following the link. Be sure and download the TEN FREE TOOLS while you’re here.

Image credit: Pixabay

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