Category Archives: Technology

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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Innovation or Not – Oklahoma State Football Helmets Seek to Revolutionize NIL

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GUEST POST from Art Inteligencia

In the rapidly changing landscape of collegiate athletics, the Name, Image, and Likeness (NIL) revolution is creating both challenges and opportunities. Oklahoma State University (OSU) is taking a bold step to embrace this shift by introducing a unique, possibly groundbreaking concept – integrating NIL into their football helmets.

The Concept

OSU’s idea is straightforward yet revolutionary: use the football helmet as a platform for NIL branding. Instead of traditional school logos or player numbers, the helmets will display personal brand logos and endorsements. This turns every game into a live advertisement for players, directly tying their on-field performance to their marketability.

Key Elements of the Concept

Player-Centric Branding: Helmets will feature personalized logos or endorsements chosen by players, subject to NIL agreements.
Dynamic Advertising: The design can change weekly or according to the duration of individual endorsement deals.
Visibility and Impact: Enhances the visibility of players’ personal brands during high-visibility game broadcasts.

Potential Benefits

This innovative approach could have several major advantages:

For Players

Increased earning potential through personalized brand endorsements.
Enhanced marketability by combining athletic performance with brand visibility.
Empowerment in controlling their personal brand narrative.

For Schools

Attracting top talent by offering a unique platform for NIL opportunities.
Strengthening alumni and fan base connection through support of player-driven initiatives.
Potential new revenue streams through partnerships with brands aligned with athletes.

Challenges and Considerations

However, this initiative is not without its challenges. Key concerns include:

Ensuring fair and equitable opportunities for all players, regardless of their profile or position on the team.
Navigating NCAA regulations and maintaining compliance with NIL guidelines.
Managing potential conflicts between school sponsorship agreements and individual player deals.
Addressing potential aesthetic criticisms from traditionalists who prefer team-centric designs.

Integrating QR Codes for Enhanced Engagement

OSU is not stopping at logo-based branding; they are keen on leveraging technology to amplify the impact of their NIL initiative. The next phase of this bold experiment involves integrating QR codes onto the helmets and distributing them at local bars and restaurants.

Details of the QR Code Initiative

Helmet QR Codes: Each player’s helmet will sport a unique QR code that fans can scan with their smartphones. This will redirect them to the player’s personalized NIL content, including social media profiles, merchandise, and sponsorship deals.
Local Business Partnerships: QR codes will also be placed on tables at bars and restaurants around Stillwater, Oklahoma. This aims to create a seamless connection between the local business community and the athletic program.

Benefits of QR Code Integration

Increased Fan Interaction: Fans can engage more deeply with their favorite players by easily accessing content and offers through QR scans.
Boosting Local Economy: Encouraging local fans and visitors to frequent businesses supporting OSU athletics helps keep revenue within the community.
Augmented Revenue Streams: Creates additional opportunities for NIL deals, as businesses directly benefit from increased foot traffic and fan engagement.

Conclusion

OSU’s innovative approach to integrating NIL into football helmets represents a bold step into the future of collegiate athletics. It exemplifies the evolving dynamics of sports marketing, where athletes are increasingly seen as individual brands. While there are challenges to address, this initiative underscores the importance of embracing change and fostering creativity in an ever-competitive landscape.

Whether this will be a fleeting experiment or a long-lasting transformation remains to be seen. For now, OSU is at the forefront of redefining how college athletes can capitalize on their fame and pave the way for a more equitable sharing of revenues generated by their incredible talents and efforts.

Innovation or not, the journey of NIL in sports has only just begun, and Oklahoma State’s helmets might just be the catalyst for the revolution we’ve been waiting for.

Innovation or not?

Image credit: Oklahoma State University Athletics via ArizonaSports.com

This photo provided by Oklahoma State Athletics shows a QR code on an Oklahoma State NCAA college football helmet, Thursday, Aug. 15, 2024, at Boone Pickens Stadium in Stillwater, Okla. (Bruce Waterfield/OSU Athletics via AP)

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What We Have Learned About Digital Transformation Thus Far

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What We Have Learned About Digital Transformation Thus Far

GUEST POST from Geoffrey A. Moore

We are well into our first decade of digital transformation, with both the successes and the scars to show for it, and we can see there is a long way to go. Realistically, there is probably never a finish line, so I think it is time for us to pause and take stock of what we have learned, and how best we can proceed from here. Here are three lessons to take to heart.

Lesson 1: There are three distinct levels of transformation, and operating model transformation is the one that deserves the most attention.

The least disruptive transformation is to the infrastructure model. This should be managed within the Productivity Zone, where to be fair, the disruption will be considerable, but it should not require much in the way of behavior change from the rest of the enterprise. Moving from data centers to cloud computing is a good example, as are enabling mobile applications and remote work centers. The goal here is to make employees more efficient while lowering total cost of IT ownership. These transformations are well underway, and there is little confusion about what next steps to take.

By contrast, the most disruptive transformation is to the business model. Here a company may be monetizing information derived from its operating model, as the SABRE system did for American Airlines, or overlaying a digital service on top of its core offering, as the automotive makers are seeking to do with in-car entertainment. The challenge here is that the economics of the new model have little in common with the core model, which creates repercussions both with internal systems and external ecosystem relationships. Few of these transformations to date can be said to be truly successful, and my view is they are more the exception than the rule.

The place where digital transformation is having its biggest impact is on the operating model. Virtually every sector of the economy is re-engineering its customer-facing processes to take advantage of ubiquitous mobile devices interacting with applications hosted in the cloud. These are making material changes to everyday interactions with customers and partners in the Performance Zone, where the priority is to improve effectiveness first, efficiency second. The challenge is to secure rapid, consistent, widespread adoption of the new systems from every employee who touches them. More than any other factor, this is the one that separates the winners from the losers in the digital transformation game.

Lesson 2: Re-engineer operating models from the outside in, not the inside out.

A major challenge that digital transformation at the operating model level must overcome is the inertial resistance of the existing operating model, especially where it is embedded in human behaviors. Simply put, people don’t like change. (Well, actually, they all want other people to change, just not themselves.) When we take the approach of internal improvement, things go way too slowly and eventually lose momentum altogether.

The winning approach is to focus on an external forcing function. For competition cultures, the battle cry should be, this new operating model poses an existential threat to our future. Our competitors are eating our lunch. We need to change, and we need to do it now! For collaboration cultures, the call to action should be, we are letting our customers down because we are too hard to do business with. They love our offers, but if we don’t modernize our operating model, they are going to take their business elsewhere. Besides, with this new digital model, we can make our offers even more effective. Let’s get going!

This is where design thinking comes in. Forget the sticky notes and lose the digital whiteboards. This is not about process. It is about walking a mile in the other person’s shoes, be that an end user, a technical buyer, a project sponsor, or an implementation partner, spending time seeing what hoops they have to go through to implement or use your products or simply to do business with you. No matter how good you were in the pre-digital era, there will be a ton of room for improvement, but it has to be focused on their friction issues, not yours. Work backward from their needs and problems, in other words, not forward from your intentions or desires.

Lesson 3: Digital transformations cannot be pushed. They must be pulled.

This is the hardest lesson to learn. Most executive teams have assumed that if they got the right digital transformation leader, gave them the title of Chief Transformation Officer, funded them properly, and insured that the project was on time, on spec, and on budget, that would do the trick. It makes total sense. It just doesn’t work.

The problem is one endemic to all business process re-engineering. The people whose behavior needs to change—and change radically—are the ones least comfortable with the program. When some outsider shows up with a new system, they can find any number of things wrong with it and use these objections to slow down deployment, redirect it into more familiar ways, and in general, diminish its impact. Mandating adoption can lead to reluctant engagement or even malicious compliance, and the larger the population of people involved, the more likely this is to occur.

So what does work? Transformations that are driven by the organization that has to transform. These start with the executive in charge who must galvanize the team to take up the challenge, to demand the digital transformation, and to insert it into every phase of its deployment. In other words, the transformation has to be pulled, not pushed.

Now, don’t get me wrong. There is still plenty of work on the push side involved, and that will require a strong leader. But at the end of the day, success will depend more on the leader of the consuming organization than that of the delivery team.

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

Image Credit: Pexels

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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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How to Pursue a Grand Innovation Challenge

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How to Pursue a Grand Innovation Challenge

GUEST POST from Greg Satell

All too often, innovation is confused with agility. We’re told to “adapt or die” and encouraged to “move fast and break things.” But the most important innovations take time. Einstein spent ten years on special relativity and then another ten on general relativity. To solve tough, fundamental problems, we have to be able to commit for the long haul.

As John F. Kennedy put it in his moonshot speech, “We choose to go to the moon in this decade and do the other things, not because they are easy, but because they are hard, because that goal will serve to organize and measure the best of our energies and skills.” Every organization should pursue grand challenges for the same reason.

Make no mistake. Innovation needs exploration. If you don’t explore, you won’t discover. If you don’t discover you won’t invent and if you don’t invent you will be disrupted. It’s just a matter of time. Unfortunately, exploration can’t be optimized or iterated. That’s why grand challenges don’t favor the quick and agile, but the patient and the determined.

1. Don’t Bet The Company

Most grand challenges aren’t like the original moonshot, which was, in large part, the result of the space race with the Soviets that began with the Sputnik launch in 1957. That was a no-holds-barred effort that consumed the efforts of the nation, because it was widely seen as a fundamental national security issue that represented a clear and present danger.

For most organizations, those type of “bet-the-company” efforts are to be avoided. You don’t want to bet your company if you can avoid it, for the simple reason that if you lose you are unlikely to survive. Most successful grand challenges don’t involve a material investment. They are designed to be sustainable.

“Grand challenges are not about the amount of money you throw at the problem, Bernard Meyerson, IBM’s Chief Innovation Officer, told me. “To run a successful grand challenge program, failure should not be a material risk to the company, but success will have a monumental impact. That’s what makes grand challenges an asymmetric opportunity.”

Take, for example Google’s X division. While the company doesn’t release its budget, it appeared to cost the company about $3.5 billion in 2018, which is a small fraction of its $23 billion in annual profits at the time. At the same time, just one project, Waymo, may be worth $70 billion (2018). In a similar vein, the $3.8 billion invested in the Human Genome Project generated nearly $800 billion of economic activity as of 2011.

So the first rule of grand challenges is not to bet the company. They are, in fact, what you do to avoid having to bet the company later on.

2. Identify A Fundamental Problem

Every innovation starts out with a specific problem to be solved. The iPod, for example, was Steve Jobs’s way of solving the problem of having “a thousand songs in my pocket.” More generally, technology companies strive to deliver better performance and user experience, drug companies aim to cure disease and retail companies look for better ways to drive transactions. Typically, firms evaluate investment based on metrics rooted in past assumptions

Grand challenges are different because they are focused on solving fundamental problems that will change assumptions about what’s possible. For example, IBM’s Jeopardy Grand Challenge had no clear business application, but transformed artificial intelligence from an obscure field to a major business. Later, Google’s AlphaGo made a similar accomplishment with self-learning. Both have led to business opportunities that were not clear at the time.

Grand challenges are not just for technology companies either. MD Anderson Cancer Center has set up a series of Moonshots, each of which is designed to have far reaching effects. 100Kin10, an education nonprofit, has identified a set of grand challenges it has tasked its network with solving.

Talia Milgrom-Elcott, Executive Director of 100Kin10, told me she uses the 5 Whys as a technique to identify grand challenges. Start with a common problem, keep asking why it keeps occurring and you will eventually get to the root problem. By focusing your efforts on solving that, you can make a fundamental impact of wide-ranging consequence.

3. Commit To A Long Term Effort

Grand challenges aren’t like normal problems. They don’t conform to timelines and can’t effectively be quantified. You can’t justify a grand challenge on the basis of return on investment, because fundamental problems are too pervasive and ingrained to surrender themselves to any conventional form of analysis.

Consider The Cancer Genome Atlas, which eventually sequenced and published over 10,000 tumor genomes When Jean Claude Zenklusen first came up with the idea in 2005, it was highly controversial, because although it wasn’t particularly expensive, it would still take resources away from more conventional research.

Today, however, the project is considered to be a runaway success, which has transformed the field, greatly expanding knowledge and substantially lowering costs to perform genetic research. It has also influenced efforts in other fields, such as the Materials Genome Initiative. None of this would have been possible without commitment to a long-term effort.

And that’s what makes grand challenges so different. They are not business as usual and not immediately relevant to present concerns. They are explorations that expand conventional boundaries, so cannot be understood within them.

An Insurance Policy Against A Future You Can’t Yet See

Typically, we analyze a business by extrapolating current trends and making adjustments for things that we think will be different. So, for example, if we expect the market to pick up, we may invest in more capacity to profit from greater demand. On the other hand, if we expect a softer market, we’d probably start trimming costs to preserve margins.

The problem with this type of analysis is that the future tends to surprise us. Technology changes, customer preferences shift and competitors make unexpected moves. Nobody, no matter how diligent or smart, gets every call right. That’s why every business model fails sooner or later, it’s just a matter of time.

It’s also what makes pursuing grand challenges is so important. They are basically an insurance policy against a future we can’t yet see. By investing sustainably in solving fundamental problems, we can create new businesses to replace the ones that will inevitably falter. Google doesn’t invest in self-driving cars to improve its search business, it invests because it knows that the profits from search won’t last forever.

The problem is that there is a fundamental tradeoff between innovation and optimization, so few organizations have the discipline to invest in exploration today for a uncertain payoff tomorrow. That’s why so few businesses last.

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

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Rise of the Atomic Consultant

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Or the Making of a Superhero

Rise of the Atomic Consultant

by Braden Kelley

In today’s rapidly evolving world, the consulting landscape is undergoing a profound transformation. I was recently asked a series of questions to capture my thoughts on how the consulting industry and its employees will need to evolve to thrive in the coming years – including my thoughts on the creation of “superhero” consultants. The emergence of the “superhero” consultant is not merely a result of advanced tools and technologies, but rather the cultivation of essential skills and capabilities. As we navigate through this era of unprecedented change, it is imperative for consulting firms to foster a culture of flexibility, growth, and continuous learning. The future of consulting lies in the hands of those who can seamlessly integrate human expertise with artificial intelligence (AI), build meaningful connections in a hybrid work environment, and facilitate diverse perspectives to drive innovation. This article delves into the key attributes that will define the next generation of consultants and explores the obstacles that must be overcome to unlock their full potential.

Here are the questions:

1) What are the tools and technologies that a consultant should use to become a “superhero” consultant? Why are these specific tools/technologies important? How should these tools be used most effectively?

This is the wrong question. It is not tools and technologies that will enable “superhero” consultants, but instead the development of the right skills and capabilities. The future of consulting will require consulting firms to hire and develop employees that are:

Flexible and growth minded – the world is changing at an accelerating rate and consultants more than ever before will need to be lifelong learners, comfortable with knowledge gaps and eager to become an expert in something on behalf of the client with each new project
AI Taskmasters – the future of work is man and machine working together and consultants skilled at breaking down work to the right size (atomizing work) and assigning it to both human and AI workers
Socially Savvy – remote and hybrid work is here to stay and even clients have soured on having consultants travel in every week, so “superhero” consultants must excel at building connections and relationships via internal, external and client social tools to both distribute/execute work and to source new work
Skilled facilitators – as data and AI-generated work products become plentiful, sense-making rises in importance along with a diversity of perspectives – often in workshops facilitated by consultants
Open Sourced – gone are the days of rinse and repeat projects powered by proprietary frameworks and IP, instead “superhero” consultants will excel at identifying the right tools and frameworks to bring to bear – from FutureHacking™ to Design Thinking to the Change Planning Toolkit™

The capabilities of tools and technologies will grow over time and new ones will emerge. The best consultants will constantly be scanning the horizon for new tools, technologies, and capabilities and leverage the above skills and capabilities to unlearn and then re-learn the best ways to create value for their clients.

2) What are the biggest obstacles that prevent consultants from being able to access or learn the steps needed to become a “superhero” consultant? What should be done to remove these obstacles to help make this transformation easier for more consultants?

The biggest obstacles that prevent consultants from becoming “superheroes” are internal – to both the consultants themselves and the firms they work for. Companies will need to examine their own policies, procedures, and training programs to right-size them for this emerging new reality. Firms will need to allow consultants to pick the right frameworks, tools and technologies for addressing client challenges – instead of limiting them to those owned by the firm. Consultants will need to shift their mindset from being experts in a particular tool or technology and towards being masters of the above skills and capabilities and experts in achieving key client outcomes. Firms will need to invest in the training and the technology necessary to provide AI’s built for purpose to accelerate the ability of consultants to more efficiently and effectively solve client challenges. Firms will also need to update their tools and methods for capturing and sharing knowledge to leverage AI capabilities at the same time.

3) What specific areas of consulting (eg. IT, finance, marketing, etc.) have the greatest potential to produce this new brand of “superhero” consultants? Why?

This new brand of “superhero” consultants will excel in a number of different disciplines because they will be able to not only find more efficient and effective ways to execute work traditionally performed by consultants (technology implementations, analytical work, etc.), but as they are helping clients transform the ways they perform different types of work, they will also be able to help clients identify new activities that will be made possible by the transformation and the new technologies and ways of working they bring with it. The reason is their focus on building skills and capabilities into which tools and technologies plug in – somewhat interchangeably.

Conclusion

The journey to becoming a “superhero” consultant is not without its challenges, but the rewards are immense. By embracing a mindset of lifelong learning and adaptability, consultants can harness the power of emerging technologies to deliver unparalleled value to their clients. The future of consulting is not about rigid frameworks or proprietary tools, but about the ability to unlearn and relearn, to innovate and collaborate, and to drive meaningful change. As we look ahead, it is clear that the most successful consultants will be those who can navigate the complexities of a dynamic world with agility and foresight. Let us continue to push the boundaries of what is possible and strive to create a brighter future for the consulting industry. Keep innovating!

p.s. Be sure and follow both my personal account and the Human-Centered Change and Innovation community on LinkedIn.

Image credit: Bing Copilot (Microsoft Designer)

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