Category Archives: Technology

Push versus Pull in the Productivity Zone

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Push versus Pull in the Productivity Zone

GUEST POST from Geoffrey A. Moore

Digital transformation is hardly new. Advances in computing create more powerful infrastructure which in turn enables more productive operating models which in turn can enable wholly new business models. From mainframes to minicomputers to PCs to the Internet to the Worldwide Web to cloud computing to mobile apps to social media to generative AI, the hits just keep on coming, and every IT organization is asked to both keep the current systems running and to enable the enterprise to catch the next wave. And that’s a problem.

The dynamics of productivity involve a yin and yang exchange between systems that improve efficiency and programs that improve effectiveness. Systems, in this model, are intended to maintain state, with as little friction as possible. Programs, in this model, are intended to change state, with maximum impact within minimal time. Each has its own governance model, and the two must not be blended.

It is a rare IT organization that does not know how to maintain its own systems. That’s Job One, and the decision rights belong to the org itself. But many IT organizations lose their way when it comes to programs — specifically, the digital transformation initiatives that are re-engineering business processes across every sector of the global economy. They do not lose their way with respect to the technology of the systems. They are missing the boat on the management of the programs.

Specifically, when the CEO champions the next big thing, and IT gets a big chunk of funding, the IT leader commits to making it all happen. This is a mistake. Digital transformation entails re-engineering one or more operating models. These models are executed by organizations outside of IT. For the transformation to occur, the people in these organizations need to change their behavior, often drastically. IT cannot — indeed, must not — commit to this outcome. Change management is the responsibility of the consuming organization, not the delivery organization. In other words, programs must be pulled. They cannot be pushed. IT in its enthusiasm may believe it can evangelize the new operating model because people will just love it. Let me assure you — they won’t. Everybody endorses change as long as other people have to be the ones to do it. No one likes to move their own cheese.

Given all that, here’s the playbook to follow:

  1. If it is a program, the head of the operating unit that must change its behavior has to sponsor the change and pull the program in. Absent this commitment, the program simply must not be initiated.
  2. To govern the program, the Program Management Office needs a team of four, consisting of the consuming executive, the IT executive, the IT project manager, and the consuming organization’s program manager. The program manager, not the IT manager, is responsible for change management.
  3. The program is defined by a performance contract that uses a current state/future state contrast to establish the criteria for program completion. Until the future state is achieved, the program is not completed.
  4. Once the future state is achieved, then the IT manager is responsible for securing the system that will maintain state going forward.

Delivering programs that do not change state is the biggest source of waste in the Productivity Zone. There is an easy fix for this. Just say No.

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

Image Credit: Unsplash

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We Need to Solve the Productivity Crisis

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We Need to Solve the Productivity Crisis

GUEST POST from Greg Satell

When politicians and pundits talk about the economy, they usually do so in terms of numbers. Unemployment is too high or GDP is too low. Inflation should be at this level or at that. You get the feeling that somebody somewhere is turning knobs and flicking levers in order to get the machine humming at just the right speed.

Yet the economy is really about our well being. It is, at its core, our capacity to produce goods and services that we want and need, such as the food that sustains us, the homes that shelter us and the medicines that cure us, not to mention all of the little niceties and guilty pleasures that we love to enjoy.

Our capacity to generate these things is determined by our productive capacity. Despite all the hype about digital technology creating a “new economy,” productivity growth for the past 50 years has been tremendously sluggish. If we are going to revive it and improve our lives we need to renew our commitment to scientific capital, human capital and free markets.

Restoring Scientific Capital

In 1945, Vannevar Bush, delivered a report, Science, The Endless Frontier, that argued that the US government needed to invest in “scientific capital” and through basic research and scientific education. It would set in motion a number of programs that would set the stage for America’s technological dominance during the second half of the century.

Bush’s report led to the development of America’s scientific infrastructure, including agencies such as the National Science Foundation (NSF), National Institutes of Health (NIH) and DARPA. Others, such as the National Labs and science programs at the Department of Agriculture, also contribute significantly to our scientific capital.

The results speak for themselves and returns on public research investment have been shown to surpass those in private industry. To take just one example, it has been estimated that the $3.8 billion invested in the Human Genome Project resulted in nearly $800 billion in economic impact and created over 300,000 jobs in just the first decade.

Unfortunately, we forgot those lessons. Government investment in research as a percentage of GDP has been declining for decades, limiting our ability to produce the kinds of breakthrough discoveries that lead to exciting new industries. What passes for innovation these days displaces workers, but does not lead to significant productivity gains.

So the first step to solving the productivity puzzle would be to renew our commitment to investing in the type of scientific knowledge that, as Bush put it, can “turn the wheels of private and public enterprise.” There was a bill before congress to do exactly that, but unfortunately it got bogged down in the Senate due to infighting.

Investing In Human Capital

Innovation, at its core, is something that people do, which is why education was every bit as important to Bush’s vision as investment was. “If ability, and not the circumstance of family fortune, is made to determine who shall receive higher education in science, then we shall be assured of constantly improving quality at every level of scientific activity,” he wrote.

Programs like the GI Bill delivered on that promise. We made what is perhaps the biggest investment ever in human capital, sending millions to college and creating a new middle class. American universities, considered far behind their European counterparts earlier in the century, especially in the sciences, came to be seen as the best in the world by far.

Today, however, things have gone horribly wrong. A recent study found that about half of all college students struggle with food insecurity, which is probably why only 60% of students at 4-year institutions and even less at community colleges ever earn a degree. The ones that do graduate are saddled with decades of debt

So the bright young people who we don’t starve we are condemning to decades of what is essentially indentured servitude. That’s no way to run an entrepreneurial economy. In fact, a study done by the Federal Reserve Bank of Philadelphia found that student debt has a measurable negative impact on new business creation.

Recommitting Ourselves To Free and Competitive Markets

There is no principle more basic to capitalism than that of free markets, which provide the “invisible hand” to efficiently allocate resources. When market signals get corrupted, we get less of what we need and more of what we don’t. Without vigorous competition, firms feel less of a need to invest and innovate, and become less productive.

There is abundant evidence that is exactly what has happened. Since the late 1970s antitrust enforcement has become lax, ushering in a new gilded age. While digital technology was hyped as a democratizing force, over 75% of industries have seen a rise in concentration levels since the late 1990s, which has led to a decline in business dynamism.

The problem isn’t just monopoly power dominating consumers, either, but also monopsony, or domination of suppliers by buyers, especially in labor markets. There is increasing evidence of collusion among employers designed to keep wages low, while an astonishing abuse of non-compete agreements that have affected more than a third of the workforce.

In a sense, this is nothing new. Adam Smith himself observed in The Wealth of Nations that “Our merchants and master-manufacturers complain much of the bad effects of high wages in raising the price, and thereby lessening the sale of their goods both at home and abroad. They say nothing concerning the bad effects of high profits. They are silent with regard to the pernicious effects of their own gains. They complain only of those of other people.”

Getting Back On Track

In the final analysis, solving the productivity puzzle shouldn’t be that complicated. It seems that everything we need to do we’ve done before. We built a scientific architecture that remains unparalleled even today. We led the world in educating our people. American markets were the most competitive on the planet.

Yet somewhere we lost our way. Beginning in the early 1970s, we started reducing our investment in scientific research and public education. In the early 1980s, the Chicago school of competition law started to gain traction and antitrust enforcement began to wane. Since 2000, competitive markets in the United States have been in serious decline.

None of this was inevitable. We made choices and those choices had consequences. We can make other ones. We can choose to invest in discovering new knowledge, educate our children without impoverishing them, to demand our industries compete and hold our institutions to account. We’ve done these things before and can do so again.

All that’s left is the will and the understanding that the economy doesn’t exist in the financial press, on the floor of the stock markets or in the boardrooms of large corporations, but in our own welfare as well as in our ability to actualize our potential and realize our dreams. Our economy should be there to serve our needs, not the other way around.

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

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Are You Continuing to Stop and Start the Hard Way?

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Are You Continuing to Stop and Start the Hard Way?

GUEST POST from Mike Shipulski

The stop, start, continue method (SSC) is a simple, yet powerful, way to plan your day, week and year. And though it’s simple, it’s not simplistic. And though it looks straightforward, it’s onion-like in its layers.

Stop, start, continue (SSC) is interesting in that it’s forward-looking, present-looking, and rearward-looking at the same time. And its power comes from the requirement that the three time perspectives must be reconciled with each other. Stopping is easy, but what will start? Starting is easy, unless nothing is stopped. Continuing is easy, but it’s not the right thing if the rules have changed. And starting can’t start if everything continues.

Stop. With SSC, stopping is the most important part. That’s why it’s first in the sequence. When everyone’s plates are full and every meeting is an all-you-can-eat buffet, without stopping, all the new action items slathered on top simply slip off the plate and fall to the floor. And this is double trouble because while it’s clear new action items are assigned, there’s no admission that the carpet is soiled with all those recently added action items.

Here’s a rule: If you don’t stop, you can’t start.
And here’s another: Pros stop, and rookies start.

With continuous improvement, you should stop what didn’t work. But with innovation, you should stop what was successful. Let others fan the flames of success while you invent the new thing that will start a bigger blaze.

Start. With SSC, starting is the easy part, but it shouldn’t be. Resources are finite, but we conveniently ignore this reality so we can start starting. The trouble with starting is that no one wants to let go of continuing. Do everything you did last year and start three new initiatives. Continue with your current role, but start doing the new job so you can get the promotion in three years.

Here’s a rule: Starting must come at the expense of continuing.
And here’s another: Pros do stop, start, continue, and rookies do start, start, start.

Continue. With SSC, continue is underrated. If you’re always starting, it’s because you have nothing good to continue. And if you’ve got a lot of continuing to do, it’s because you’ve got a lot of good things going on. And continuing is efficient because you’re not doing something for the first time. And everyone knows how to do the work and it goes smoothly.

But there’s a dark side to continue – it’s called the status quo. The status quo is a powerful, one-trick pony that only knows how to continue. It hates stopping and blocks all starting. Continuing is the mortal enemy of innovation.

Here’s a rule: Continuing must stop, or starting can’t start.
And here’s another: Pros continue and stop before they start, and rookies start.

SSC is like juggling three balls at once. Just as it’s not juggling unless it’s three balls at the same time, it’s not SSC unless it’s stop, start, continue all done at the same time. And just as juggling two balls at once isn’t juggling, it’s not SSC if it’s just two out of the three. And just as dropping two of the three balls on the floor isn’t juggling, it’s not SSC if it’s starting, starting, starting.

Image credit: Pexels

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Coping with the Chasm

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Coping with the Chasm

GUEST POST from Geoffrey A. Moore

I’ve been talking about crossing the chasm incessantly for over thirty years, and I’m not likely to stop, but it does beg the question, how should you operate when you are in the chasm? What is the chasm itself about, and what actions is it likely to reward or punish?

The chasm is a lull in the Technology Adoption Life Cycle, one that comes after the enthusiasts and visionaries have made their splash and before the pragmatists are willing to commit. At this time the new category is on the map, people are talking about it, often quite enthusiastically, but no one has budgeted for it as yet. That means that conventional go-to-market efforts, based on generating and pursuing qualified leads with prospects who have both budget and intent to purchase, cannot get traction. It does not mean, however, that they won’t entertain sales meetings and demos. They actually want to learn more about this amazing new thing, and so they can keep your go-to-market engine humming with activity. They just won’t buy anything.

Crossing the Chasm says it is time for you to select a beachhead market segment with a compelling reason to buy and approach them with a whole product that addresses an urgent unsolved problem. All well and good, but what if you don’t know enough about the market (or your own product for that matter) to make a sound choice? What if you are stuck in the chasm and have to stay there for a while? What can you do?

First of all, take good care of the early adopter customers you do have. Give them more service than you normally would, in part because you want them to succeed and be good references, but also because in delivering that service, you can get a closer look at their use cases and learn more about the ones that might pull you out of the chasm.

Second, keep your go-to-market organization lean and mean. You cannot sell your way out of the chasm. You cannot market your way out either. The only way out is to find that targetable beachhead segment with the compelling use case that they cannot address through any conventional means. This is an exercise in discovery, so your go-to-market efforts need to be provocative enough to get the meeting (this is where thought leadership marketing is so valuable) and your sales calls need to be intellectually curious about the prospect’s current business challenges (and not presentations about how amazing your company is or flashy demos to show off your product). In short, in the chasm, you are a solution looking for a problem.

Third, get your R&D team directly in contact with the customer, blending engineering, professional services, and customer success all into one flexible organization, all in search of the beachhead use case and the means for mastering its challenges. You made it to the chasm based on breakthrough technology that won the hearts of enthusiasts and visionaries, but that won’t get you across. You have to get pulled out of the chasm by prospective customers who will make a bet on you because they are desperate for a new approach to an increasingly vexing problem, and you have made a convincing case that your technology, product, talent, and commitment can fill the bill.

Finally, let’s talk about what you should not do. You cannot perform your way out of the chasm. You have no power. So, this is not a time to focus on execution. Instead, you have to find a way to increase your power. In the short term, you can do this through consulting projects—you have unique technology power that people want to consume; they just don’t want to consume through a product model at this time. They are happy to pay for bespoke projects, however, and that is really what the Early Market playbook is all about. Of course, projects don’t scale, so they are not a long-term answer, but they do generate income, and they do keep you in contact with the market. What you are looking for is solution power, tying your technology power to a specific use case in a specific segment, one that you could deliver on a repeatable basis and get you out of the chasm. Often these use cases are embedded in bespoke projects, just a part of the visionary’s big picture, but with more than enough meat on the bone to warrant a pragmatist’s attention.

Sooner or later you have to make a bet. You can recognize a good opportunity by the following traits:

  • There is budget to address the problem, and it is being spent now.
  • The results the prospect is getting are not promising and, if anything, the situation is deteriorating.
  • You know from at least one of your projects that you can do a lot better.

That’s about all the data you are going to get. That’s why we call crossing the chasm a high-risk, low-data decision. But it beats staying in the chasm by a long shot.

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

Image Credit: Microsoft Copilot

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Creating the Ultimate Customer Experience with AI

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Delivering Real Value the Key

Creating the Ultimate Customer Experience with AI

GUEST POST from Shep Hyken

Whenever I get the chance to interview the CEO of a major CX company, I jump at the chance. I recently conducted a second interview with Alan Masarek, the CEO of Avaya, a company focused on creating customer experience solutions for large enterprises.

My first interview covered an amazing turnaround that Masarek orchestrated in his first year at Avaya, taking the company through Chapter 11 and coming out strong. Masarek admits that even with his extensive financial background, he’s always been a product person, and it’s the combination of the two mindsets that makes him the perfect leader for Avaya.

In our discussion, he shared his view on AI and how it must deliver value in the contact center. What follows is a summary of the main points of our interview, followed by my commentary.

Why Customer Service and CX Are Important: Thanks to the internet, it’s harder for brands to differentiate themselves. Within minutes, a customer can compare prices, check availability, find a company that can deliver the product within a day or two, or find comparable products from other retailers, vendors and manufacturers. Furthermore, while the purchasing experience needs to be positive, it’s what happens beyond the purchase that becomes most important. Masarek says, “Brands are now trying to differentiate based upon the experience they provide. So any tool that can help the brand achieve this is the winner.”

Customer Service Is Rooted in Communications: Twenty years ago, the primary way to communicate with a company was on the phone. While we still do that, the world has evolved to what is referred to as omni-channel, which includes voice, chat, email, brand apps, social media and more. As we move from the phone to alternative channels of communication, companies and brands must find ways to bring them all together to create a seamless journey for the customer.

Organizations Want to Minimize Voice: According to Masarek, companies want to move away from traditional voice communication, which is a human on the phone. That “one-to-one” is very expensive. With digital solutions, you have one-to-many. Masarek says, “It’s asynchronous. And the beauty is you can introduce AI utilities into the customer experience, which creates greater efficiency. You’re solving so many things either digitally or deflecting it altogether via the chatbot, the voice bot or what have you.”

AI Will Not Eliminate Jobs: Masarek says, “There’s a bull and a bear case for an employment point of view relative to AI. Will it be a destroyer of jobs, a bear case, or will it grow jobs, the bull case?” He shared an example that perfectly describes the situation we’re in today. In the 1960s, Barclay’s Bank introduced the ATM. Everyone thought it would be the end of tellers working at banks. That never happened. What did happen is that tellers took on a more important role, going beyond just cashing checks or depositing money. It’s the same in the customer service world. AI technologies will take care of simple tasks, freeing customer service agents to help with more complicated issues. (For more on how AI will not eliminate jobs, read this Forbes article from September 2023.)

The Employee Experience Drives the Customer Experience: AI is not just about supporting the customer. It can also support the agent. When the agent is talking to a customer, generative AI technology can listen in the background, search through a company’s knowledge base and feed the agent information in real time. Masarek said, “Think about what a pleasant experience that is for both the agent and the customer!”

Innovation Without Disruption: A company may invest in a better customer experience, but sometimes, that causes stress to the organization. Masarek is proud of Avaya’s value proposition, which is to add innovation without disruption. This means there’s a seamless integration versus total replacement of existing systems and processes. Regarding the upgrade, Masarek says, “The last thing you want is to rip it all out.”

The Customer-In Approach: As we wrapped up our interview, I asked Masarek for one final nugget of wisdom. He shared his Customer-In approach. Not that long ago, you could compete on product, price and availability. Today, that’s table stakes. What separates one brand from another is the experience. Masarek summarized this point by saying, “You have to set your North Star on as few things as possible. Focus wins. And so, if you’re always thinking Customer First and all your decisions are rooted in that concept, your business will be successful. At the end of the day, brands win on how they make the customer feel. It’s no longer just about product, price and availability.”

Image Credits: Pixabay

This article was originally published on Forbes.com.

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AI Requires Conversational Intelligence

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AI Requires Conversational Intelligence

GUEST POST from Greg Satell

Historically, building technology had been about capabilities and features. Engineers and product designers would come up with new things that they thought people wanted, figure out how to make them work and ship “new and improved” products. The result was often things that were maddeningly difficult to use.

That began to change when Don Norman published his classic, The Design of Everyday Things and introduced concepts like dominant design, affordances and natural mapping into industrial design. The book is largely seen as pioneering the user-centered design movement. Today, UX has become a thriving field.

Yet artificial intelligence poses new challenges. We speak or type into an interface and expect machines to respond appropriately. Often they do not. With the popularity of smart speakers like Amazon Alexa and Google Home, we have a dire need for clear principles for human-AI interactions. A few years ago, two researchers at IBM embarked on a journey to do just that.

The Science Of Conversations

Bob Moore first came across conversation analysis as an undergraduate in the late 1980s, became intensely interested and later earned a PhD based on his work in the field. The central problems are well known to anybody who has ever watched Seinfeld or Curb Your Enthusiasm, our conversations are riddled with complex, unwritten rules that aren’t always obvious.

For example, every conversation has an unstated goal, whether it is just to pass the time, exchange information or to inspire an emotion. Yet our conversations are also shaped by context. For example, the unwritten rules would be different for a conversation between a pair of friends, a boss and subordinate, in a courtroom setting or in a doctor’s office.

“What conversation analysis basically tries to reveal are the unwritten rules people follow, bend and break when engaging in conversations,” Moore told me and he soon found that the tech industry was beginning to ask similar questions. So he took a position at Xerox PARC and then Yahoo! before landing at IBM in 2012.

As the company was working to integrate its Watson system with applications from other industries, he began to work with Raphael Arar, an award-winning visual designer and user experience expert. The two began to see that their interests were strangely intertwined and formed a partnership to design better conversations for machines.

Establishing The Rules Of Engagement

Typically, we use natural language interfaces, both voice and text, like a search box. We announce our intention to seek information by saying, “Hey Siri,” or “Hey Alexa,” followed by a simple query, like “where is the nearest Starbucks.” This can be useful, especially when driving or walking down the street,” but is also fairly limited, especially for more complex tasks.

What’s far more interesting — and potentially far more useful — is being able to use natural language interfaces in conjunction with other interfaces, like a screen. That’s where the marriage of conversational analysis and user experience becomes important, because it will help us build conventions for more complex human-computer interactions.

“We wanted to come up with a clear set of principles for how the various aspects of the interface would relate to each other,” Arar told me. “What happens in the conversation when someone clicks on a button to initiate an action?” What makes this so complex is that different conversations will necessarily have different contexts.

For example, when we search for a restaurant on our phone, should the screen bring up a map, information about pricing, pictures of food, user ratings or some combination? How should the rules change when we are looking for a doctor, a plumber or a travel destination?

Deriving Meaning Through Preserving Context

Another aspect of conversations is that they are highly dependent on context, which can shift and evolve over time. For example, if we ask someone for a restaurant nearby, it would be natural for them to ask a question to narrow down the options, such as “what kind of food are you looking for?” If we answer, “Mexican,” we would expect that person to know we are still interested in restaurants, not, say, the Mexican economy or culture.

Another issue is that when we follow a particular logical chain, we often find some disqualifying factor. For instance, a doctor might be looking for a clinical trial for her patient, find one that looks promising but then see that that particular study is closed. Typically, she would have to retrace her steps to go back to find other options.

“A true conversational interface allows us to preserve context across the multiple turns in the interaction,” Moore says. “If we’re successful, the machine will be able to adapt to the user’s level of competence, serving the expert efficiently but also walking the novice through the system, explaining itself as needed.”

And that’s the true potential of the ability to initiate more natural conversations with computers. Much like working with humans, the better we are able to communicate, the more value we can get out of our relationships.

Making The Interface Disappear

In the early days of web usability, there was a constant tension between user experience and design. Media designers were striving to be original. User experience engineers, on the other hand, were trying to build conventions. Putting a search box in the upper right hand corner of a web page might not be creative, but that’s where users look to find it.

Yet eventually a productive partnership formed and today most websites seem fairly intuitive. We mostly know where things are supposed to be and can navigate things easily. The challenge now is to build that same type of experience for artificial intelligence, so that our relationships with the technology become more natural and more useful.

“Much like we started to do with user experience for conventional websites two decades ago, we want the user interface to disappear,” Arar says. Because when we aren’t wrestling with the interface and constantly having to repeat ourselves or figuring out how to rephrase our questions, we can make our interactions much more efficient and productive.

As Moore put it to me, “Much of the value of systems today is locked in the data and, as we add exabytes to that every year, the potential is truly enormous. However, our ability to derive value from that data is limited by the effectiveness of the user interface. The more we can make the interface become intelligent and largely disappear, the more value we will be able unlock.”

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

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Errors You May Be Making in Your Customer Experience

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Errors You May Be Making in Your Customer Experience

GUEST POST from Howard Tiersky

Maintaining a website or mobile experience with a high degree of usability is essential to maximize business outcomes, and people who are frustrated often take for granted how easy it is in the digital world to simply click over to a competitor. Even worse are cases where determined customers simply cannot figure out how to proceed to complete a transaction, or otherwise achieve their goals.

At FROM, we regularly conduct both in person and online usability tests for our clients to observe “real” users engaging with their digital experience. This gives us enormous insight into where users are encountering frustration, confusion, or other difficulties, and while we are huge believers in robust usability testing as a tool to identify and prioritize which aspects of a digital touch point should be optimized (and really, it’s not terribly time-consuming or expensive), there is a little-known trick that can start to identify many problems. While not as comprehensive as user testing, it’s generally much faster, and therefore, a great place to start!

What is that place? The server’s error logs.

While it may not sound super sexy, your error logs contain a treasure trove of data.

First, the server will typically log if a page doesn’t load properly, errors occur, or if transactions fail to complete. Naturally, usability is hampered if your customers are receiving errors because the system not functioning properly, and yet it’s amazing how often server logs don’t get looked at. And since error logs can generally be viewed by browser and device, it’s not uncommon to find that a new version of Chrome or Edge is causing errors that previously didn’t exist, so this is something that need regular attention. In addition, many systems rely on external cloud services, increasing the points of failure. By monitoring server errors, you can make sure you are aware if your site is “breaking,” a simple but often overlooked part of managing an effective digital experience.

Second, we have errors of user validation, i.e., a user enters an invalid email or phone number, tries to complete a transaction without checking the “terms and conditions” acceptance box, etc. Now, on the one hand, you might say “That’s not my fault, my site worked. It was the user made a mistake!” Bzzzzt. Wrong answer. Especially if there are a lot of these types of errors, or if the number suddenly spikes.

It’s our job to design a solution that makes it unlikely that users will make errors. If they’re frequently overlooking something, or misunderstanding what they are meant to do, it’s a sign we need to look at that screen or field and consider how to redesign it to reduce confusion. It might be as simple as rewriting the instructions or moving a button.

One nuance we like to look for is circular errors. What’s a circular error? It’s when, during a single session, a user sends the same input multiple times and receives the same error. For example, a user submits a page, and the email is determined to be invalid (a logged error.) Then the user submits again, with the same email (and maybe then a third time, again with the same email.) These types of circular errors usually mean the error messaging system in your application is flawed. Perhaps the error text appears at the top of the screen, and the field itself is below the fold, so the user may not even be seeing the error text.

The third type of error is failed search or out of stock messages. The user wants to rent a car with a pickup at 2 am but that location is closed, or the user wants the pants in a 42 waist, but you don’t have any in stock. Or, the user is searching your site for information on bed wetting, but no articles match that term. These types of errors indicate a missed opportunity to meet a customer need, and you should scour these types of messages to consider what steps can be taken to meet commonly requested unmet needs.

All of this is based on the assumption that your site’s back-end code is logging errors properly. This is a standard coding practice, but just because it’s standard doesn’t mean it can’t get omitted, or that certain errors might not have code that logs them. It’s important to check with your technical team; if your site is not logging most errors, or not logging them with sufficient detail, this code can generally be added.

Additionally, you may include logging at different levels of your system, and therefore have multiple log files. For example, the web server may have one log file, the commerce layer may have a separate log file, and your security/authentication layer may have its own log files, and that’s fine. There are great tools that can combine them together and make them easy to analyze, filter, sort, etc.

The logging I’ve been referring to is generally done on the server. However, with each new generation of digital experiences, we push more and more code (including more and more error checking) to the client. Whether it’s javascript (in the case of web pages), or Java code (in the case of mobile apps.) These types of error events can be logged as well, it just requires a separate effort or technology (but it’s well worth it!) You can use analytics packages like Google Analytics to record “events” when certain things (like error messages) happen in the interface.

A one or two-day analysis of error logs can help you focus in on specific, frequently occurring error states that were previously off your radar. Sometimes, it’s still necessary to do user testing to figure out what the deeper reason for the confusion is, but even still, it’s helpful to know where the errors are occurring, so you can focus your testing there. In other cases, it’s easy to guess what’s tripping your users up, once the errors are there to act as signposts.

This article originally appeared on the Howard Tiersky blog

Image Credits: Pixabay

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You Are Probably Not Prepared to Innovate

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You Are Probably Not Prepared to Innovate

GUEST POST from Greg Satell

Becoming a successful executive is a fairly linear path. You start at the bottom and learn to solve basic problems in your field or industry. As you gain experience and improve your skills you are given more responsibility, begin to manage teams and work diligently to set up the practices and processes to help your team succeed.

The best executives make those around them better, by fostering a positive work environment, minimizing drama and providing strategy and direction that will enable the team meet its objectives. That’s how you deliver consistent results and continue to rise up through the ranks to the top of your profession.

At some point, however, you need to do more than just plan and execute strategy, you have to innovate. Every business model is disrupted eventually. Changes in technology, competitive landscape and customer needs make that inevitable and, unfortunately, executive experience doesn’t equip your for it. Here’s four things that will help you make the shift from operations to innovation.

1. Learn How To Be The Dumbest Guy In The Room

Good executives are often the smartest guys in the room. Through years of experience solving tough problems, they learn to be masters of their craft and are able to mentor those around them. A great operational manager is a great coach, guiding others around them to achieve more than they thought they could.

Unfortunately, innovation isn’t about what you know, but what you don’t. It requires you to explore, push boundaries and venture into uncharted areas in which there often are no true experts. You’re basically flying blind, which can be incredibly uncomfortable, especially to those who have had a strong track record of success in a structured environment.

That’s why the first step to making the shift from operations to innovation is to learn how to become the dumbest guy in the room instead of the smartest. Admit to yourself that you don’t know what you need to succeed and begin to explore. Actively seek out those who know and understand things that you don’t.

Being the smartest guy in the room helps you operate smoothly, but being the dumbest guy in the room helps you learn. The best way to start is by seeking out new rooms to spend time in.

2. Create A Bias For Action

Operations thrive on predictability. People need to know what to expect and what’s expected of them so that things can run smoothly. Every great operation needs to coordinate activities between a diverse set of stakeholders, including team members, partners and customers. That level of interoperability doesn’t just happen by itself.

Over the years, a variety of methods, such as Total Quality Management (TQM) and Six Sigma have arisen that use rigorous statistical methods to optimize for established metrics. The idea is to hone processes continuously in order to elevate them to paragons of efficiency.

When you seek to innovate, however, established metrics are often of little use, because you are trying to do something new and change the basis of competition. Again, you are venturing into the unknown, doing things you and your organization have not developed the knowledge and skills to do well. Instead of seeking excellence, you need to dare to be crap.

The key to making this work is not to abandon all sense of restraint and accountability, but to manage risk by reducing scale. In an operational setting you always want to look for the largest addressable market you can find, but when you are trying to do something truly new, you need to find a hair on fire use case — a customer who needs a problem solved so badly that they are willing to work through the inevitable glitches and snafus with you.

3. Solve The Monkey First

Every good operational project has a roadmap, whether that is an ordinary budget, a project plan or a defined strategy. The early stages of a plan are usually the easiest. You want to get everybody on board, build momentum and then begin to tackle tougher problems. When you are trying to do something new and different, however, you often want to do exactly the opposite.

Every significant innovation involves something that’s never been done before, so you can’t be sure how long it will take or even if the core objectives can be achieved at all. So it’s best to get started working on the toughest problems early, because until you resolve those unknowns, the whole project is unworkable.

At Google’s X division, the company’s “moonshot factory,” the mantra is #MonkeyFirst. The idea is that if you want to get a monkey to recite Shakespeare on a pedestal, you’d better start by training the monkey, not building the pedestal, because training the monkey is the hard part. Anyone can build a pedestal.

Operational executives like to build pedestals so that they can show early progress against a timeline. Unfortunately, when you are striking out into the unknown, building a pedestal gets you nowhere. Unless you can actually train the monkey, working on the pedestal is wasted effort. You have to learn how to train monkeys.

4. Move from Metrics To Mission

Good operational executives sweat the numbers. They work within existing frameworks and hone operations to improve performance against established metrics. Yet when you are trying to do something truly new, established metrics often tell you little. The goal isn’t to play the game better, but to change it entirely.

In fact, established businesses often get disrupted precisely because they are focusing on outdated metrics. For example, when digital cameras first came out, they performed poorly by traditional standards of quality. They did, however, perform much better in terms of convenience and, as the quality of the pictures improved, replaced the earlier technology.

In a similar vein, while traditional brokerages focused on service, Charles Schwab offered minimal service at a far lower price. At first, it didn’t seem like a threat to incumbents, but as technology improved, it was able to improve service and keep the low flat fees. The model ended up transforming the industry.

So it’s important to not get blinded by metrics and focus on your mission. True innovation never happens in a straight line or proceeds at a measured pace. That’s why there is a basic tradeoff between innovation and optimization and very few people can do both. The best executives, however, learn how to bridge that gap.

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

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Racing Towards Electrical Vehicle Innovation

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Racing Towards Electrical Vehicle Innovation

GUEST POST from Art Inteligencia

Since the inception of automotive racing over a century ago, the roar of engines and the telltale scent of burning rubber have been synonymous with the thrill and excitement of motorsport. Yet, in 2014, the landscape began to transform dramatically with the launch of Formula E, an all-electric street racing series that has not only revolutionized the sport but also acted as a catalyst for innovation in the electric vehicle (EV) industry. This pivotal shift has paved the way for a convergence of sustainability, cutting-edge technology, and exhilarating competition on the asphalt. Let’s take a closer look at the evolution of Formula E from its inception to today, and delve into the remarkable advancements across Gen1, Gen2, Gen3, and the anticipated Gen4 cars.

The Genesis – Gen1: Sparking a New Era (2014-2017)

When Formula E made its debut in 2014, skepticism was rife. Could electric cars truly capture the imagination of racing enthusiasts? However, the Gen1 cars quickly silenced doubters with their impressive capabilities. These vehicles boasted a maximum power output of 200 kW (equivalent to about 268 horsepower), accelerating from 0 to 100 km/h in approximately three seconds. Despite their limitations—such as the need for mid-race car swaps due to battery constraints—the Gen1 cars showcased the immense potential of electric propulsion.

Here is a video of the inaugural race:

The Gen1 era highlighted the importance of efficient energy management, as teams and drivers grappled with balancing speed and battery life. Every race turned into a strategic battle of conservation versus performance, laying the groundwork for the monumental shifts that would follow.

Gen2: Revolutionizing Range and Power (2018-2022)

The arrival of Gen2 vehicles brought with it a surge of advancements that propelled Formula E into a thrilling new chapter. With an enlarged battery capacity, these cars could now complete entire races without the need for a mid-race swap. The power output increased to a maximum of 250 kW (around 335 horsepower), delivering improved acceleration and peak speeds.

In addition to increased power and range, Gen2 cars introduced the iconic Halo safety device—a crucial step in enhancing driver safety. The cars also introduced “Attack Mode,” which allowed drivers to momentarily access an extra boost of power, adding another layer of strategic depth to the races.

With a sleeker, more aggressive design, the Gen2 cars began to bridge the gap between traditional motorsport and futuristic innovation. Fans started to see Formula E as more than just an experiment; it was now a viable and exciting racing series in its own right.

Gen3: The Dawn of Efficiency and Sustainability (2023-Present)

The current era, marked by the introduction of Gen3 cars, represents a quantum leap in efficiency, technology, and sustainability. Gen3 cars boast an even greater power output—over 350 kW (roughly 470 horsepower)—and feature regenerative braking systems that can recover almost half of the energy consumed during a race. This innovation not only prolongs battery life but also significantly reduces the environmental impact of the races.

Moreover, Gen3 cars are designed with sustainability at their core. The car’s carbon footprint has been minimized with the use of sustainable and recyclable materials, aligning with Formula E’s mission to create a greener planet. The additional power has also made the races faster and more competitive, increasingly captivating audiences around the world.

Here is a video highlighting some of the new developments in the Gen3 car:

The Gen3 era underscores the sport’s commitment to a future where high performance and environmental responsibility coexist harmoniously. Formula E’s push towards using more sustainable materials and reducing emissions has set a new benchmark not just in racing but across the entire automotive industry.

Looking Ahead – Gen4: The Future Beckons

Anticipation is already building for the next leap forward with Gen4 cars, expected to hit the tracks in the not-so-distant future. While official specifications remain under wraps, the trajectory of innovation hints at even lighter, more powerful (boost from 350kw to 600kw), and more efficient vehicles (increase from 600kw to 700kw max regen). We can expect further advancements in battery technology, potentially doubling the range and enabling more aggressive and continuous racing.

Potential improvements in AI and autonomous driving technologies could further redefine the strategic and technical landscape of Formula E. The integration with smart city ecosystems, dynamic in-race adjustments, and real-time energy management are all buzzing as possible features of the Gen4 evolution.

Conclusion

The journey from Gen1 to Gen3 has shown how Formula E is not just a racing series but a transformative force, accelerating the adoption of electric vehicle technology and fostering a new age of sustainable racing. Each generation of cars has pushed the boundaries of what’s possible, marrying performance with efficiency and environmental stewardship.

As we race towards the Gen4 era, Formula E continues to encourage global automakers to innovate, experiment, and excel. In doing so, it not only redefines the landscape of motorsport but also paves the way for a greener, faster, and more electrifying future for all.

The evolution of Formula E demonstrates that the future of racing—and perhaps the automotive world at large—is electric. Hold on tight, because the checkered flag heralds not the end of the race but the beginning of an electrifying new journey.

Image credit: FIA Formula E, Wikimedia Commons – Nico Müller (SUI, ABT Cupra Formula E Team)

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Who Are the Most Important People in Your Company?

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Who Are the Most Important People in Your Company?

GUEST POST from Mike Shipulski

When the fate of your company rests on a single project, who are the three people you’d tap to drag that pivotal project over the finish line? And to sharpen it further, ask yourself “Who do I want to lead the project that will save the company?” You now have a list of the three most important people in your company. Or, if you answered the second question, you now have the name of the most important person in your company.

The most important person in your company is the person that drags the most important projects over the finish line. Full stop.

When the project is on the line, the CEO doesn’t matter; the General Manager doesn’t matter; the Business Leader doesn’t matter. The person that matters most is the Project Manager. And the second and third most important people are the two people that the Project Manager relies on.

Don’t believe that? Well, take a bite of this. If the project fails, the product doesn’t sell. And if the product doesn’t sell, the revenue doesn’t come. And if the revenue doesn’t come, it’s game over. Regardless of how hard the CEO pulls, the product doesn’t launch, the revenue doesn’t come, and the company dies. Regardless of how angry the GM gets, without a product launch, there’s no revenue, and it’s lights out. And regardless of the Business Leader’s cajoling, the project doesn’t cross the finish line unless the Project Manager makes it happen.

The CEO can’t launch the product. The GM can’t launch the product. The Business Leader can’t launch the product. Stop for a minute and let that sink in. Now, go back to those three sentences and read them out loud. No, really, read them out loud. I’ll wait.

When the wheels fall off a project, the CEO can’t put them back on. Only a special Project Manager can do that.

There are tools for project management, there are degrees in project management, and there are certifications for project management. But all that is meaningless because project management is alchemy.

Degrees don’t matter. What matters is that you’ve taken over a poorly run project, turned it on its head, and dragged it across the line. What matters is you’ve run a project that was poorly defined, poorly staffed, and poorly funded and brought it home kicking and screaming. What matters is you’ve landed a project successfully when two of three engines were on fire. (Belly landings count.) What matters is that you vehemently dismiss the continuous improvement community on the grounds there can be no best practice for a project that creates something that’s new to the world. What matters is that you can feel the critical path in your chest. What matters is that you’ve sprinted toward the scariest projects and people followed you. And what matters most is they’ll follow you again.

Project Managers have won the hearts and minds of the project team.

The Project manager knows what the team needs and provides it before the team needs it. And when an unplanned need arises, like it always does, the project manager begs, borrows, and steals to secure what the team needs. And when they can’t get what’s needed, they apologize to the team, re-plan the project, reset the completion date, and deliver the bad news to those that don’t want to hear it.

If the General Manager says the project will be done in three months and the Project Manager thinks otherwise, put your money on the Project Manager.

Project Managers aren’t at the top of the org chart, but we punch above our weight. We’ve earned the trust and respect of most everyone. We aren’t liked by everyone, but we’re trusted by all. And we’re not always understood, but everyone knows our intentions are good. And when we ask for help, people drop what they’re doing and pitch in. In fact, they line up to help. They line up because we’ve gone out of our way to help them over the last decade. And they line up to help because we’ve put it on the table.

Whether it’s IoT, Digital Strategy, Industry 4.0, top-line growth, recurring revenue, new business models, or happier customers, it’s all about the projects. None of this is possible without projects. And the keystone of successful projects? You guessed it. Project Managers.

Image credit: Unsplash

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