How to Avoid AI Project Failures

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How To Avoid AI Project Failures

GUEST POST from Greg Satell

A survey a few years ago by Deloitte of “aggressive adopters” of cognitive technologies found that 76% believe that they will “substantially transform” their companies within the next three years. There probably hasn’t been this much excitement about a new technology since the dotcom boom years in the late 1990s.

The possibilities would seem to justify the hype. AI isn’t just one technology, but a wide array of tools, including a number of different algorithmic approaches, an abundance of new data sources and advancement in hardware. In the future, we will see new computing architectures, like quantum computing and neuromorphic chips, propel capabilities even further.

Still, there remains a large gap between aspiration and reality. Gartner estimated that 85% of big data projects fail. There have also been embarrassing snafus, such as when Dow Jones reported that Google was buying Apple for $9 billion and the bots fell for it or Microsoft’s Tay chatbot went berserk on Twitter. Here’s how to transform the potential of AI into real results.

Make Your Purpose Clear

AI does not exist in a vacuum, but in the context of your business model, processes and culture. Just as you wouldn’t hire a human employee without an understanding of how he or she would fit into your organization, you need to think clearly about how an artificial intelligence application will drive actual business results.

“The first question you have to ask is what business outcome you are trying to drive,” Roman Stanek, CEO at GoodData, told me. “All too often, projects start by trying to implement a particular technical approach and not surprisingly, front-line managers and employees don’t find it useful. There’s no real adoption and no ROI.”

While change always has to be driven from the top, implementation is always driven lower down. So it’s important to communicate a sense of purpose clearly. If front-line managers and employees believe that artificial intelligence will help them do their jobs better, they will be much more enthusiastic and effective in making the project successful.

“Those who are able to focus on business outcomes are finding that AI is driving bottom-line results at a rate few had anticipated,” Josh Sutton, CEO of Agorai.ai, told me. He pointed to a McKinsey study from a few years ago that pegs the potential economic value of cognitive tools at between $3.5 trillion and $5.8 trillion as just one indication of the possible impact.

Choose The Tasks You Automate Wisely

While many worry that cognitive technologies will take human jobs, David Autor, an economist at MIT, sees the the primary shift as one of between routine and nonroutine work. In other words, artificial intelligence is quickly automating routine cognitive processes much like industrial era machines automated physical labor.

To understand how this can work, just go to an Apple store. Clearly, Apple is a company that clearly understands how to automate processes, but the first thing you see when you walk into an Apple store you see is a number employees waiting to help you. That’s because it has chosen to automate background tasks, not customer interactions.

However, AI can greatly expand the effectiveness of human employees. For example, one study cited by a White House report during the Obama Administration found that while machines had a 7.5 percent error rate in reading radiology images and humans had a 3.5% error rate, when humans combined their work with machines the error rate dropped to 0.5%.

Perhaps most importantly, this approach can actually improve morale. Factory workers actively collaborate with robots they program themselves to do low-level tasks. In some cases, soldiers build such strong ties with robots that do dangerous jobs that they hold funerals for them when they “die.”

Data Is Not Just An Asset, It Can Also Be A Liability

For a long time more data was considered better. Firms would scoop up as much of it as they could and then feed it into sophisticated algorithms to create predictive models with a high degree of accuracy. Yet it’s become clear that’s not a great approach.

As Cathy O’Neil explains in Weapons of Math Destruction, we often don’t understand the data we feed into our systems and data bias is becoming a massive problem. A related problem is that of over-fitting. It may sound impressive to have a model that is 99% accurate, but if it is not robust to changing conditions, you might be better off with one that is 70% accurate and simpler.

Finally, with the implementation of GDPR in Europe and the likelihood that similar legislation will be adopted elsewhere, data is becoming a liability as well as an asset. So you should think through which data sources you are using and create models that humans can understand and verify. “Black boxes” serve no one.

Shift Humans To Higher Value Tasks

One often overlooked fact about automation is that once you automate a task, it becomes largely commoditized and value shifts somewhere else. So if you are merely looking to use cognitive technologies to replace human labor and cut costs, you are most probably on the wrong track.

One surprising example of this principle comes from the highly technical field of materials science. A year ago, I was speaking to Jim Warren of the Materials Genome Initiative about the exciting possibility of applying machine learning algorithms to materials research. More recently, he told me that this approach has increasingly become a focus of materials research.

That’s an extraordinary shift in one year. So should we be expecting to see a lot of materials scientists at the unemployment office? Hardly. In fact, because much of the grunt work of research is being outsourced to algorithms, the scientists themselves are able to collaborate more effectively. As George Crabtree, Director of the Joint Center for Energy Storage Research, which has been a pioneer in automating materials research put it to me, “We used to advance at the speed of publication. Now we advance at the speed of the next coffee break.”

And that is the key to understanding how to implement cognitive technologies effectively. Robots are not taking our jobs, but rather taking over tasks. That means that we will increasingly see a shift in value from cognitive skills to social skills. The future of artificial intelligence, it seems, is all too human.

— Article courtesy of the Digital Tonto blog and previously appeared on Harvard Business Review
— Image credits: Pexels

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Secrets to Overcoming Resistance to Change

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Secrets to Overcoming Resistance To Change

GUEST POST from David Burkus

Employee resistance to change is one of the most perplexing and challenging issues that business executives encounter. Senior leaders have mapped out a change initiative and, in the process, gotten themselves excited about the future only to find the rest of the organization doesn’t share their enthusiasm.

This resistance manifests in various ways, such as decreased productivity, higher rates of employee turnover and transfer requests, attitudes, unauthorized strikes, or work slowdowns. And in trying to overcome resistance to change, leaders often make structured, logical arguments for why the change is needed.

Arguments that fail to persuade.

What’s often overlooked is that employee resistance to change is most likely due to the emotions behind the change, not the change itself. And in examining those emotions, the late Carl Frost offered four key questions that people ask themselves when they’re being asked to change. The answers to these questions determine their excitement, or resistance, to change.

In this article, we will explore how to overcome resistance to change by addressing the emotions behind it and we’ll offer advice on how leaders can answer those four questions.

Question 1: Do we know where we’re going?

A clear and compelling vision of the future is necessary to overcome resistance to change. It is important to paint a clear picture of what the future of the organization looks like and include the individual being asked to make the change in that vision. When employees can see themselves as a part of the future, they are more likely to embrace the change. Additionally, it is crucial to ensure that the vision is shared at every level of the organization. This helps create a sense of unity and purpose, making it easier for employees to align themselves with the change.

By providing a clear direction and involving employees in the vision, leaders can address the uncertainty and fear that often accompany change. When employees have a clear understanding of where the organization is heading, they are less likely to resist and more likely to actively participate in the change process.

Question 2: Do we know why we’re going there?

Communicating the reasons for the change effectively is essential in overcoming resistance. Employees need to understand the changes in regulation, competition, or the economy that necessitate the change. It is important to avoid nostalgia for the old times before the change, as this can hinder progress. Instead, leaders should focus on selling people on why the change is necessary and beneficial.

By clearly explaining the rationale behind the change, leaders can address any doubts or concerns employees may have. When employees understand the need for change and how it will positively impact the organization, they are more likely to embrace it and actively contribute to its success.

Question 3: Do we know we can get there?

Confidence in the organization’s ability to achieve the vision is crucial for overcoming resistance to change. Leaders must build belief in the organization’s capacity to reach the new future. This can be done by addressing concerns about skills, resources, and capabilities. It is important to create a plan to acquire necessary skills and resources, ensuring that employees have the support and tools they need to succeed.

By addressing concerns and providing the necessary resources, leaders can instill confidence in employees and alleviate their fears about the change. When employees believe that the organization has the capability to achieve the vision, they are more likely to embrace the change and actively work towards its realization.

Question 4: Do we know that there is better than here?

Individuals need to believe that the change will benefit them personally in order to lessen their resistance. Leaders should paint a compelling picture of the change in their role and how it will be better. It is important to show how the change will result in personal growth and improvement. Additionally, leaders should address concerns about sacrifices, extra time, and learning new skills.

By addressing the personal benefits of the change and addressing personal concerns, leaders can help employees see the value in embracing the change. When employees understand how the change will positively impact their own lives, they are more likely to overcome resistance and actively engage in the change process.

Overcoming resistance to change is crucial for successful change initiatives. By addressing the emotions behind the change and answering the four questions, leaders can increase excitement, self-efficacy, and confidence in the change. That helps the organizational change itself find success and (hopefully) that success empowers every employee to do their best work ever.

Image credit: Pexels

Originally published on DavidBurkus.com on August 7, 2023

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Time to Make a Difference

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Time to Make a Difference

GUEST POST from Mike Shipulski

When it’s time to make meaningful change, there’s no time for consensus.

When the worn path of success must be violated, use a small team.

When it’s time for new thinking, create an unreasonable deadline, and get out of the way.

The best people don’t want the credit, they want to be stretched just short of their breaking point.

When company leadership wants you to build consensus before moving forward, they don’t think the problem is all that important or they don’t trust you.

When it’s time to make unrealistic progress, it’s time for fierce decision making.

When there’s no time for consensus, people’s feelings will be hurt. But there’s no time for that either.

When you’re pissed off because there’s been no progress for three years, do it yourself.

When it’s time to make a difference, permission is not required. Make a difference.

The best people must be given the responsibility to use their judgment.

When it’s time to break the rules, break them.

When the wheels fall off, regardless of the consequences, put them back on.

When you turn no into yes and catch hell for violating protocol, you’re working for the wrong company.

When everyone else has failed, it’s time to use your discretion and do as you see fit.

When you ask the team to make rain and they balk, you didn’t build the right team.

When it’s important and everyone’s afraid of getting it wrong, do it yourself and give them the credit.

The best people crave ridiculous challenges.

When the work must be different, create an environment that demands the team acts differently.

When it’s time for magic, keep the scope tight and the timeline tighter.

When the situation is dire and you use your discretion, to hell with anyone who has a problem with it.

When it’s time to pull a rabbit out of the hat, you get to decide what gets done and your special team member gets to decide how to go about it. Oh, and you also get to set an unreasonable time constraint.

When it’s important, to hell with efficiency. All that matters is effectiveness.

The best people want you to push them to the limit.

When you think you might get fired for making a difference, why the hell would you want to work for a company like that?

When it’s time to disrespect the successful business model, it’s time to create harsh conditions that leave the team no alternative.

The best people want to live where they want to live and do impossible work.

Image credit: Unsplash

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Five Keys to Personalizing the Customer Experience

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Five Keys to Personalizing the Customer Experience

GUEST POST from Shep Hyken

Earlier this year, we surveyed more than 1,000 consumers in the U.S. for our 2024 State of Customer Service and Customer Experience (CX) Study. We asked about the importance of a personalized experience. We found that 81% of customers prefer companies that offer a personalized experience, and 70% say a personalized experience in which the employee knows who they are and their history with the company (past purchases, buying patterns, support calls and more) is important. They also want the experience to go beyond people and include the platforms where they prefer to do business.

For a recent episode of Amazing Business Radio, I talked with Elizabeth Tobey, head of Marketing, Digital & AI of NICE, which helps companies apply AI to manage customer experience. The focus of the discussion was personalization. Here are some of the highlights from the interview:

1. Channel of Choice: This is where the modern-day concept of personalization begins. Tobey said, “In a world where people carry computers in their pockets (also known as mobile phones), it’s important to meet your customers when and where they want to be met.” Customers used to have two main choices when communicating with a brand. They could either walk into a store or call on the phone. Today, there are multiple channels and platforms. They can still visit in person or call, but they can also go to a website with self-service options, visit a social channel like Facebook, conduct business using an app, communicate with a brand’s chatbot and more. Customers want convenience, and part of that is being able to connect with a brand the way they want to connect. Some companies and brands do that better than others. The ones that get it right have educated customers on what they should expect, in effect raising the bar for all others who haven’t yet recognized the importance of communication.

2. Communicate on the Customer’s Terms: Tobey shared a frustrating personal experience that illustrated how some customers like to communicate but a brand falls short. Tobey was getting home late from an event. She contacted a company through its support channel on its website and was communicating with a customer support agent via chat. It was late, and she said, “I have to go to sleep,” expecting she could continue the chat the next morning with another agent. But, when she went to resume the conversation, she was forced to restart the process. She logged back into the website and repeated the authentication process, which was expected, but what she didn’t expect was having to start over with a new agent, repeating her conversation from the beginning as if she had never called before. Tobey made a case for technology that allows for asynchronous conversations on the customer’s timeline, eliminating the need for “over-authentication” and forcing the customer to start over, wasting time and creating an experience marred with friction.

3. Eliminate Friction: How could an interview with an executive at a technology company like NICE not bring up the topic of AI? In the story Tobey told about having to start over with a new agent, going through the authentication process again and repeating her issue, there is a clear message, which is to eliminate unnecessary steps. I shared an experience about visiting a doctor’s office where I had to fill out numerous forms with repeat information: name, address, date of birth, etc. Why should any patient have to fill in the same information more than once? The answer to the question, according to Tobey, is AI. She says, “Take all data that’s coming in from a customer journey and feed it into our AI so that the engine is continuously learning, growing and getting smarter. That means for every customer interaction, the automation and self-service can evolve.” In other words, once AI has the customer’s information, it should be used appropriately to eliminate needless steps (also known as friction) to give the customer the easiest and most convenient experience.

4. It’s Not Just About the Customer: In addition to AI supporting the customer’s self-service and automated experience, any data that is picked up in the customer’s journey can be fed to customer support agents, supervisors and CX leaders, changing how they work and making them more agile with the ability to make decisions faster. Agents get information about the customer, enabling them to provide the personalized experience customers desire. Tobey says, “Agents get a co-pilot or collaborator who listens to every interaction, offers them the best information they need and gives them suggestions.” For supervisors and CX leaders, they get information that makes them more agile and helps them make decisions faster.

5. Knowledge Management: To wrap up our interview, Tobey said, “AI management is knowledge management. Your AI is only as good as your data and knowledge. If you put garbage in, you might get garbage out.” AI should constantly learn and communicate the best information and data, allowing customers, agents and CX leaders to access the right information quickly and create a better and more efficient experience for all.

This article originally appeared on Forbes.com

Image Credits: Unsplash

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Nine Actions for Building a Great Team

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Which Resonates with You?

Nine Actions for Building a Great Team

GUEST POST from Stefan Lindegaard

Building a strong team is a multifaceted journey, and there are several key actions that can contribute to the growth and success of a team.

Reflecting on the nine actions for building a great team, which ones do you find your team focuses on the most?

Would you say it is:

1. Cultivating a growth mindset?

2. Enhancing psychological safety?

3. Mapping and engaging stakeholders?

4. Mastering difficult conversations?

5. Improving feedback processes?

6. Addressing individual motivations?

7. Injecting fun into your work environment?

8. Developing networking and learning opportunities?

9. Identifying trust drivers and barriers?

Share your experiences and let’s inspire each other on actions that can shape the dynamics and achievements of your team!

Team Building Stefan Lindegaard

Image Credit: Pexels, Stefan Lindegaard

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Breaking Free From Stagnation

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Breaking Free From Stagnation

GUEST POST from Robyn Bolton

As a leader in your organization, you’re under tremendous stress. Not only do you need to deliver against a “growth strategy” that demands constant increases in revenue and profit, but you also need to cut costs and support employees who are more disengaged and burned out than ever before.  If it feels like you’re working harder and running faster than ever to maintain the status quo, then I have good and bad news for you.

Bad news: You’re right. 

The feeling of working harder or moving faster simply to stay in the same place is called the Red Queen effect or hypothesis.  The hypothesis asserts “that species must constantly adapt, evolve, and proliferate in order to survive while pitted against ever-evolving opposing species.”  Its name is inspired by the Red Queen in Lewis Carroll’s Through the Looking Glass, who explains to Alice, “here, you see, it takes all the running you can do, to keep in the same place.”

You probably feel the same need to adapt to survive “while pitted against ever-evolving opposing species” every time you see new technologies, read about another new management framework, or hear news from your competitors. You also understand that your organization needs to grow and often hear that it needs to do so at all costs, so you buckle down, work hard, and pull off quarterly miracles.

Good for you! You’re reward?  You get to do it all over again, and faster, this quarter.  And, to add insult to injury, all that growth you’re working harder and harder to achieve is a mirage.

75% of companies do not grow.

HBS professor Gary P. Pisano examined the growth rate of 10,897 publicly held US companies between 1976 and 2019.  When adjusted for inflation, the top quartile grew 11.8% yearly, but the other 75% showed little to negative growth. 

Being in that top quartile was no guarantee of success, as only 15% (3% of the total sample) were able to sustain a growth rate of 0.3%+ for 30 years. In fact, only SEVEN companies—Walmart, UPS, Southwest, Publix, Johnson & Johnson, Danaher, and Berkshire Hathaway—were top-quartile growth companies throughout the thirty years studied.

If you worked at one of those 7 companies, congrats!  Your hard work delivered real and repeatable growth.  If you worked at any of the other 10,890, I hope they offer great benefits?

We know why.

Every good academic knows you can’t just throw out some data without trying to find a causal link, and Professor Pisano is a good academic

“I have found that while the usual explanations for slow or minimal growth—market forces and technological changes such as disruptive innovation—play a role, many companies’ growth problems are self-inflicted. Specifically, firms approach growth in a highly reactive, opportunistic manner. When market demand is booming, they go on hiring binges, throw resources at developing new capacity, and build out organizational infrastructure without thinking through the implications… In the process of chasing growth, companies can easily destroy the things that made them successful in the first place, such as their capacity for innovation, their agility, their great customer service, or their unique cultures. When demand slows, pressures to maintain historical growth rates can lead to quick-fix solutions such as costly acquisitions or drastic cuts in R&D, other capabilities, and training. The damage caused by these moves only exacerbates the growth problems.”

(Bold text added by me)

Good news: You Can Do Something About It

In fact, as a leader in your organization, you’re among the few who have any prayer of pulling your organization out of the Red Queen’s race and putting it on track to real and sustainable growth. Achieving this incredible success requires you (and your colleagues) to decide three things:

  1. How fast to grow (target rate of growth)
  2. Where to find sources of new demand (direction of growth)
  3. How to assemble the resources required to grow (method of growth)

Together, these three decisions comprise your growth strategy and enable your organization to achieve the “delicate balance” between demand and supply required to sustain profitable growth.

Getting to these decisions isn’t easy, but neither is slaying the Jabberwocky.  So, as this brief rest stop in your race comes to an end, who do you choose to be – Alice, who works hard and deals with a bit of nonsense to progress, or the Red Queen, content to work harder to stay in the same place?

Image credit: Unsplash

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Only One Type of Innovation Will Win the Future

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Only One Type of Innovation Will Win the Future

GUEST POST from Greg Satell

Very few businesses last. While we like to think we live in a particularly disruptive era, this has always been true. Entrepreneurs start businesses because they see opportunity and build skills, practices and processes to leverage it. Yet as the world changes, these strengths often become vulnerabilities.

The problem is that the past is not always a good guide to the future. Business models, even the successful ones, are designed for inertia. They are great for leveraging past insights, but are often resistant to change. Success does not, in fact, always breed more success, sometimes it breeds failure.

That’s why every business needs to innovate. Yet innovation is not, as some would have us believe, just about moving fast and breaking things. It’s about solving the problems you need to create a better future. What most fail to grasp is that a key factor of success is how you source problems, build a pipeline and, ultimately, choose which ones you will work on.

1. Getting Better At What You Already Do

Every year, Apple comes up with a new iPhone. That’s not as exciting as it used to be, but it’s still key to the company maintaining its competitive edge. Every model is a bit faster, more secure and has new features that make it more capable. It’s still an iPhone, but better.

Some self-appointed ‘innovation gurus” often scoff at this type of innovation as “incremental” and favor new technologies that are more “radical” or “disruptive,” but the truth is that this is where you derive the most value from innovation — getting better at what you already do and selling to customers what you already know.

So the first line of defense against irrelevance is to identify ways to improve performance in current practices and processes. The challenge, of course, with this type of innovation is that your competitors will be working on the same problems you are and it takes no small amount of agility and iteration to stay ahead. Even then, any victory is short-lived.

Still, most technologies can be improved for a long time. Moore’s Law, for example, has been around for almost 50 years and is just ending now.

2. Applying What You’re Already Good At To A Different Context

Amazon started out selling books online. It then applied its approach to other categories, such as electronics and toys. That took enormous investments in technology, which it then used to create new businesses, such as Amazon Web Services (AWS), Kindle tablets and its Echo line of smart speakers.

In each case, the company took what it already did well and expanded to an adjacent set of markets or capabilities, often with great success. The Kindle helped the company dominate e-books and strengthened its core business. AWS is far more profitable than online retail and accounts for virtually all of Amazon’s operating income.

Still, adjacent opportunities are can be risky. Amazon, despite its huge successes, has had its share of flops too. Whenever you go into a new business you are, to a greater or lesser extent, charting a course into the unknown. So you need to proceed with some caution. When you launch a new business into an adjacency, you are basically launching a startup and most of those fail.

3. Finding A Completely New Problem To Solve

Besides getting better at what you already do and applying things you already know to a different market or capability, you can also look for a new problem to solve. Clearly, this the most uncertain type of opportunity, because no one knows what a good solution will look like.

To return to the Moore’s law example, everybody knows what a 20% performance improvement in computer chips looks like. Metrics for speed and power consumption have long been established, so there is little ambiguity around what would constitute success. Customers will instantly recognize the improvement as having a specific market value.

On the other hand, no one knows what the value of a quantum computer will be. It’s a fundamentally new kind of technology that will solve new types of problems. So customers will have to explore the technology and figure out how to use it to create better products and services.

Despite the uncertainty though, I found in the research that led to my book, Mapping Innovation, that this type of exploration is probably the closest thing to a sure bet that you’re going to find. Every single organization I studied that invested in exploration found that it paid off big, with extremely high returns even accounting for the inevitable wrong turns and blind alleys.

The 70-20-10 Rule

Go to any innovation conference and you will find no shortage of debates about what type of approach creates the most value, usually ending with no satisfying conclusion. The truth is that every organization needs to improve what they already do, search for opportunities in adjacencies and explore new problems. The key is how you manage resources.

One popular approach is the 70-20-10 rule, which prescribes investing 70% of your innovation resources in improving existing technologies, 20% in adjacent markets and capabilities and 10% in markets and capabilities that don’t exist yet. That’s more of a rule of thumb than a physical law and should be taken with a grain of salt, but it’s a good guide.

Practically speaking, however, I have found that the exploration piece is the most neglected. All too often, in our over-optimized business environment, any business opportunity that can’t be immediately quantified in considered a non-starter. So we fail to begin to explore new problems until their market value has been unlocked by someone else. By that point, we are already behind the curve.

Make no mistake. The next big thing always starts out looking like nothing at all. Things that change the world always arrive out of context for the simple reason that the world hasn’t changed yet. But if you do not explore, you will not discover. If you do not discover, you will not invent. And if you do not invent, you will be disrupted. It’s just a matter of time.

— Article courtesy of the Digital Tonto blog and previously appeared on Inc.com
— Image credits: Pixabay

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Are We Doing Social Innovation Wrong?

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Are We Doing Social Innovation Wrong?

GUEST POST from Geoffrey A. Moore

The Volume Operations business model kicks in when you have hundreds of thousands of users and goes up from there. 100,000, for those of us who are not math majors, is 10 to the power of 5. Uber-successful volume ops businesses operate at 10 to the power of 9 and up—millions of users or customers. But if you are a start-up, you are looking at 10 or maybe 100. How do you get from here to there?

The key thought to keep in mind is the old chestnut “what got you here won’t get you there.” That is, whatever operating model you have, keep in mind it can scale to two exponents but never to three. That means for every two exponents you have to change operating models, which likely means you have to change executive leadership in order to go forward.

To illustrate this idea, I’d like to focus on the non-profit sector and ask the question, what would it take to really solve for any widespread social problem? Homelessness was the first one that came to mind, but hunger is another obvious one, drug addiction a second, street crime a third. They are all seemingly intractable issues that, despite the best intentions of a whole raft of people, and regardless of how much funding is supplied, stubbornly resist any sustainable improvement.

The question I want to address is not what programs would work—because I actually think a whole lot of programs would work—but rather, how could we organize to deploy these programs successfully at scale.

Following our principle of what got you here won’t get you there, we need a ladder of operating models that can take us, exponent by exponent, from 10 to the power of 1 to, say, 10 to the power of 7. What might that look like?

Scaling Social Innovation

Consider this a straw man, a place to start, something to edit. It conveys a key lesson from the high-tech sector, namely that the fastest way to kill a disruptive innovation is to race to scale by skipping over one or more of these “exponential steps.” It just doesn’t work. There are too many emergent factors at each new level you must learn to cope with in order to succeed. The only reliable way to scale is to ratchet your way up this staircase, adapting your systems and operations as you go.

Unfortunately, that’s not what politicians do. They want to make a big impact right away. That means they start everything on one of the upper stairs. Driven by impatience, they ignore the dynamics of adoption and demand mass deployment from the get-go. They think the problem is simply one of getting enough funding. It’s not. It’s one of operational innovation. Scaling prematurely simply wastes the funding. And then when programs do flounder, as they inevitably will, they blame it on execution when in reality they simply did not do the hard, time-consuming work of building up their foundation step by step from below.

One of the implications of this framework is that social services should be incubated in the private sector where freedom from regulatory constraints supports agile innovation. But as they scale, the importance of regulatory oversight increases and more communal engagement is required. The goal should be to keep this oversight as local as possible as long as possible, doing as much as we can to empower the people delivering the service itself. Once that operating model solidifies, then, and only then, is there a proper foundation for scaling to state and federal programs.

Today, we do not lack the empathy to support social services. Nor do we lack the funding. But we are failing nonetheless. We can do better. We need to do better.

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

Image Credit: Pexels

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50% Off of Charting Change This Weekend Only

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50% Off of Charting Change This Weekend Only

Wow! Exciting news!

My publisher is having a 24 hour flash sale that will allow you to get the hardcover or the digital version (eBook) of my latest best-selling book Charting Change for 50% off!

What People Are Saying

Phil McKinney “Braden Kelley and his merry band of guest experts have done a nice job of visualizing in Charting Change how to make future change efforts more collaborative. Kelley shows how to draw out the hidden assumptions and land mines early in the change planning process, and presents some great techniques for keeping people aligned as a change effort or project moves forward.”
– Phil McKinney, retired CTO for Hewlett-Packard and author of Beyond the Obvious
Daniel H Pink “There’s no denying it: Change is scary. But it’s also inevitable. In Charting Change, Braden Kelley gives you a toolkit and a blueprint for initiating and managing change in your organization, no matter what form it takes.”
– Daniel H. Pink, author of Drive and To Sell is Human
Marshall Goldsmith “Higher employee retention? Increased revenue? Process enhancements? Whatever your change goal, Charting Change is full of bright ideas and invaluable visual guides to walk you through change in any area where your organization needs it.”
– Marshall Goldsmith is the #1 New York Times bestselling author of Triggers, MOJO and What Got You Here Won’t Get You There

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Top 10 Human-Centered Change & Innovation Articles of July 2024

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Top 10 Human-Centered Change & Innovation Articles of July 2024Drum 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 July’s ten most popular innovation posts:

  1. Organizational Debt Syndrome Poses a Threat — by Stefan Lindegaard
  2. Do Nothing More Often — by Robyn Bolton
  3. Is Disruption About to Claim a New Victim? — by Robyn Bolton
  4. What Top Innovators Do Differently — by Greg Satell
  5. Four Hidden Secrets of Innovation — by Greg Gatell
  6. Rise of the Atomic Consultant — by Braden Kelley
  7. Do You Bring Your Whole Self to Work? — by Mike Shipulski
  8. Giving Your Team a Sense of Shared Purpose — by David Burkus
  9. Creating Effective Digital Teams — by Howard Tiersky
  10. Smarter Risk Taking — by Janet Sernack

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

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 four years:

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