Tag Archives: Artificial Intelligence

Creating the Ultimate Customer Experience with AI

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

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Drum roll please…

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

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

SpaceX is a Masterclass in Innovation Simplification — by Pete Foley
Secrets to Overcoming Resistance to Change — by David Burkus
Five Things Most Managers Don’t Know About Innovation — by Greg Satell
Are We Doing Social Innovation Wrong? — by Geoffrey A. Moore
Only One Type of Innovation Will Win the Future — by Greg Satell
What Your Website Reveals About Your Brand — by Howard Tiersky
The Coming Leadership Confidence Crisis — by Robyn Bolton
Adjacent Innovation is the Key to Growth and Risk — by Robyn Bolton
Bringing Emotional Energy and Creative Thinking to AI — by Janet Sernack
Delivering Customer Value is the Key to Success — by Mike Shipulski

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

Organizational Debt Syndrome Poses a Threat — by Stefan Lindegaard
Finding Innovation in the Humble Garbage Can — by Howard Tiersky
Four Forms of Team Motivation — by David Burkus
What is Your Purpose? — by Robyn Bolton
Your Strategy Must Reach Beyond Markets to Ecosystems — by Greg Satell

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

Have something to contribute?

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

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

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Bringing Emotional Energy and Creative Thinking to AI

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Bringing Emotional Energy and Creative Thinking to AI

GUEST POST from Janet Sernack

The impact of disruption, hyper-connectivity, and uncertainty, coupled with the pace of change, is causing many people to feel fearful and anxious. They become defensive and reactive and ‘go under’ emotionally and ‘go inwards’ cognitively by ruminating about their past and what bad things may happen in the future. Dwelling on past mistakes, failures, and poor performance also causes them to disengage emotionally, take flight and move away, avoid taking action, fight, or freeze and become inert, paralyzed, and immobilized. The outcome is resistance to the possibilities and creative changes using Generative AI might bring. Because they lack the vital creative and emotional energy to generate creative thinking in partnership with AI, they will resist innovation-led change and stay ‘stuck’ in their habitual, safe and conventional roles, capabilities and identities.

Emotional energy is the catalyst that fuels the creative process. Understanding and harnessing this energy inspires and motivates individuals to explore and embrace creative thinking strategies in partnership with AI.

When a person’s emotional energy has contracted, it results in constrained, negative, pessimistic, and even catastrophic thinking habits.

Where there is no space, doorway, or threshold to take on anything new, novel, or different or to imagine what might be possible in an uncertain future to evolve, advance, or transform their personal or professional lives.

Emotional energy catalyzes people’s hope, positivity and optimism to approach their worlds differently.

When people are constrained from becoming hopeful, positive, and optimistic, they cannot apply foresight to explore future possibilities and opportunities at the accelerating pace that Generative AI tools offer in unleashing the human ingenuity and generating creative thinking required to solve challenges and increasingly complex problems.

Augmenting human creativity

Generative AI, as highlighted in a recent Harvard Business Review article, How Generative AI can Augment Human Creativity, has the potential to assist humans in creating innovative solutions. Its role is not to replace humans but to augment their creativity, helping them generate and identify novel ideas and improve the quality of raw ideas.

To empower individuals to make intelligent decisions and solve complex problems, it is crucial to notice, disrupt, dispute and deviate from their unresourceful default patterns or habitual ways of doing things.

Because emotional energy is the catalyst that fuels the creative process, it is crucial to help people find ways to re-ignite their emotional energy.

Empowering, enabling, and equipping them to embody and take on new, more resourceful emotional states and traits that allow them to break free from the constraints by identifying and letting go of old, irrelevant roles, capabilities, and identities. To take on new ones to facilitate positive changes, solve challenges, and deliver highly valued innovative solutions in partnership with Generative AI to generate creative thinking.

Generating the power of questions in problem-solving

I applied and implemented three key strategies to partner with Generative AI during the six-month coaching partnership. I used creative thinking strategies to develop a comprehensive life-coaching plan for a coaching client that serendipitously co-created a range of transformational outcomes.

Identify the key challenges, strengths, and systemic nature of the core problem and set a goal for change.

Encouraged to experiment with coaching in partnership with Generative AI, I created a comprehensive summary of what my client and I agreed her core problem was. We defined a goal for effecting positive and constructive change and outlined evidence of achieving a successful outcome. I incorporated these elements into a descriptive paragraph and uploaded it into the Generative AI platform.

Develop a range of catalytic questions.

I focused on designing four key catalytic questions, to evoke and provoke creative thinking strategies. I requested the platform to design and develop a life-coaching plan to achieve our goal and solve her unique problem:

Integration involves showing that two things which appear to be different are actually the same:

What might be some key existing transformational coaching elements that can be integrated into the new life-coaching plan I am trying to create to solve this problem?
Splitting involves seeing how two things that look the same might actually be different and can be divided into useful parts, like an assembly line:
What might be some key components of transformational coaching plans that can be combined to connect with a life-coaching plan to help solve this problem?
Figure-ground reversal involves realizing that what is crucial is in the background and not in the foreground, like the invention of Slack.
What might be some of the missing parts in the transformational and life coaching processes that might be included to help solve this problem?
Distal thinking involves imagining things different from the present, like the Tesla electric car.
What could be possible without boundaries, rules or limitations in harnessing the emotional energy required to partner with my client in our coaching relationship?
How might I create value for my client? What key constraints in her whole system relate to life coaching, and how might I leverage these to solve the problem differently?

It took less than a minute and consisted of a comprehensive, step-by-step, detailed plan that would have taken me at least half a day to consider and construct.

I was delighted to have an evidence-based example of successfully augmenting human creativity, partnering with Generative AI to generate creative thinking to advance my coaching partnership.

Partner with applying a transformational process.

It took less than a minute and consisted of a comprehensive, step-by-step, detailed plan that would have taken me at least half a day of using my pause-power to construct. I was delighted to have an evidence-based example of successfully augmenting human creativity to experiment with when partnering with Generative AI to generate creative thinking in my coaching partnership:

Generating and identifying a range of novel ideas towards improving her well-being.
Exploring and improving the range and quality of the initial raw ideas by applying pause power to incubate, illuminate, and generate creative thinking.
Identifying and developing a range of options for my client to choose from, allowing her to let go of what was depleting her emotional energy and retain her hopefulness, positivity, and optimism.
Identifying and developing a range of options for my client to choose from, to take on to manifest the desired future state of well-being and re-energize her emotional energy.

What was the outcome?

By co-creating a safe and collective holding space with my client, we supported her in re-energizing emotionally and applying future-oriented creative thinking strategies. We partnered with Generative AI to innovate my coaching approach and maximize our intelligence.

The outcome was personally transformative and sustained by:

Ensuring she re-ignited and identified strategies and new habits to sustain her emotional energy and make the necessary changes and future choices.
Applying circuit breakers and divergent thinking strategies to disrupt and dispute unresourceful beliefs, biases and behavior patterns.
Creating a safe space allowed her to deviate from her feelings, thoughts, and mindset to identify what new roles, capabilities, and identities to take on in the future and how they could benefit her and add value to the quality of her life.
Assisting in creating various ideas and options to refine when making significant lifestyle change choices.

It was a powerful learning experience for both my client and myself, reinforcing and validating that “Generative AI’s greatest potential is not replacing humans; it is to assist humans in their individual and collective efforts to create hitherto unimaginable solutions. It can truly democratize innovation.”

Please find out more about our work at ImagineNation™.

Check out our learning products and tools, including The Coach for Innovators, Leaders, and Teams Certified Program, presented by Janet Sernack. It is a collaborative, intimate, and profoundly personalised innovation coaching and learning program supported by a global group of peers over nine weeks and can be customised as a bespoke corporate learning program. Please find out more about our products and tools.

Image Credit: Pixabay

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Five Keys to Personalizing the Customer Experience

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Five Keys to Personalizing the Customer Experience

GUEST POST from Shep Hyken

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

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

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

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

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

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

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

This article originally appeared on Forbes.com

Image Credits: Unsplash

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Harnessing the Secrets of Successful Customer Engagement

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Harnessing the Secrets of Successful Customer Engagement

GUEST POST from Shep Hyken

What is customer engagement? A Google search will provide plenty of definitions to consider. Here are a few to give you a clear understanding:

Qualtrics defines it as “the emotional connection between a customer and a brand.”
A recent Forbes article defines it as “building relationships with customers at every touch point.”
Wikipedia offers numerous definitions from multiple sources, including this one from Forrester from 2008: “creating deep connections with customers that drive purchase decisions, interaction, and participation over time.”

All of these (and more) are correct. They work. As a modern marketer, the new question is about how to deliver on the foundational definition of customer engagement using current tools and technology.

I had a chance to do an Amazing Business Radio interview with Spencer Burke, SVP of Growth at Braze, a customer engagement platform that offers messaging solutions to multiple communication channels. We discussed the innovative ways marketers and customer experience (CX) leaders are taking customer engagement to new levels.

Burke simplified the marketing and customer engagement definition to four words: connecting brands to consumers. That’s really the job of a marketer. The result is that customers want to buy, come back and buy more.

It may sound simple, but there are obstacles to the optimal customer engagement experience. According to Burke, “Marketers have a lot on their plate. It’s not easy to be creative when there’s an emphasis on Key Performance Indicators (KPIs) and time-draining routine tasks.”

To support this statement, Burke shared findings from the 2024 Global Customer Engagement Review, in which Braze interviewed 1,900 marketing decision makers to learn about challenges and opportunities in the marketing and CX industries.

Marketing used to be about finding creative ways to engage with customers, but today’s marketers are burdened in four areas:

1. Too much emphasis on KPIs: Forty-two percent of marketers surveyed felt KPIs inhibit a focus on creativity. Numbers/KPIs are important. After all, you can’t manage what you don’t or can’t measure (Peter Drucker). But if they get in the way of creativity, then consider pushing the numbers aside for a moment—or maybe just focus on one or two KPIs.
2. Too many routine tasks: Forty-two percent feel too much time is spent on “business as usual execution and tasks,” leaving less time for creative work. If there’s something that can be automated, then automate it. Don’t waste any employee’s time—especially when they are trying to be creative—with tasks and processes that drain energy and take up too much time.
3. Lack of technology: Forty-one percent feel that a lack of technology hinders the execution of creative ideas. This is where Artificial Intelligence (AI) can support the process. How can AI make a creative marketer’s life easier? There are many, many ways!
4. Return on Investment (ROI) is hard to track: Forty percent said it’s hard to demonstrate the ROI impact of creativity. Leadership wants ROI. Often, they demand to know the ROI before a project starts. You can’t fight this. Start with the end in mind. Understanding the benefits of the work that is to be performed is important to getting leadership to buy in and support the marketing and customer engagement efforts.

AI should be used as a tool to free up time and let creatives be creative, according to Burke. For example, he says, “When you know what you want to say but haven’t been able to articulate it just right, it’s a huge confidence booster to know you can drop the message into an AI interface to get feedback and suggestions for improvement.” The time savings are substantial, but more importantly, the marketer’s focus gets to be on creating, not finishing.

Burke also mentioned how important personalization is in today’s customer engagement strategy. There’s a tremendous amount of data on customers, and brands must be thoughtful in how they manage that data. AI can help interpret the data and deliver insights about customers that can be useful. For instance, Netflix learns its customers’ viewing habits and suggests TV shows and movies. Amazon remembers what its customers have bought in the past and can accurately predict when the customer should order more. The best brands use the data AI provides to create a better experience. But, it’s a balancing act. Too much personalization creates “the creepiness factor,” where being too detailed or specific has the opposite effect of what marketers want to achieve.

Astha Malik, Chief Business Officer of Braze, says, “Today’s marketers face growing expectations from increasingly connected consumers, who want value in exchange for their attention. In response, we see a growing number of marketers tapping into first-party data and utilizing AI to ignite creativity and craft personalized experiences that both resonate with consumers and foster brand loyalty.”

In addition, brands need to be consistent with their personalization. You can’t recognize a customer one time and not know them the next time. That defeats the entire personalization campaign.

All of this takes us back to Burke’s original definition of customer engagement, which is connecting brands to consumers. That connection must be consistent and accurate. There are more and better tools than ever to help create an optimal customer engagement experience, one interaction at a time. The closer you can get to meeting customers where they are, when they need you and provide value in those interactions, the more likely they will see you as a trusted brand and say, “I’ll be back!”

Image Credits: Pexels, Shep Hyken

This article originally appeared on Forbes.com

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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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Artificial Intelligence is a No-Brainer

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Why innovation management needs co-intelligence

Artificial Intelligence is a No-Brainer

GUEST POST from John Bessant

Long fuse, big bang. A great descriptor which Andrew Hargadon uses to describe the way some major innovations arrive and have impact. For a long time they exist but we hardly notice them, they are confined to limited application, there are constraints on what the technology can do and so on. But suddenly, almost as if by magic they move center stage and seem to have impact everywhere we look.

Which is pretty much the story we now face with the wonderful world of AI. While there is plenty of debate about labels — artificial intelligence, machine learning, different models and approaches — the result is the same. Everywhere we look there is AI — and it’s already having an impact.

More than that; the pace of innovation within the world of AI is breath-taking, even by today’s rapid product cycle standards. We’ve become used to seeing major shifts in things like mobile phones, change happening on a cycle measured in months. But AI announcements of a breakthrough nature seem to happen with weekly frequency.

That’s also reflected in the extent of use — from the ‘early days’ (only last year!) of hearing about Chat GPT and other models we’ve now reached a situation where estimates suggest that millions of people are experimenting with them. Chat GPT has grown from a handful of people to over 200 million in less than a year; it added its first million subscribers within five days of launch! Similar figures show massive and rapid take -up of competing products like Anthropic’s Claude and Google’s Gemini, etc. It’s pretty clear that there’s a high-paced ‘arms race’ going on and it’s drawing in all the big players.

This rapid rate of adoption is being led by an even faster proliferation on the supply side, with many new players entering the market , especially in niche fields. As with the apps market there’s a huge number of players jumping on the bandwagon, and significant growth in the open source availability of models. And many models now allow for users to create their own custom versions — mini-GPTs’ and ‘Co-pilots’ which they can deploy for highly specific needs.

Not surprisingly estimates suggest that the growth potential in the market for AI technologies is vast, amounting to around 200 billion U.S. dollars in 2023 and expected to grow to over 1.8 trillion U.S. dollars by 2030.

Growth in Artificial Intelligence

There’s another important aspect to this growth. As Ethan Mollick suggests in his excellent book ‘Co-intelligence’, everything that we see AI doing today is the product of a far-from-perfect version of the technology; in very short time, given the rate of growth so far, we can expect much more power, integration and multi-modality.

The all-singing, dancing and doing pretty much anything else version of AI we can imagine isn’t far off. Speculation about when AGI — artificial general intelligence — will arrive is still just that — speculative — but the direction of travel is clear.

Not that the impact is seen as entirely positive. Whilst there have been impressive breakthroughs, using AI to help understand and innovate in fields as diverse as healthcare , distribution and education these are matched by growing concern about, for example, privacy and data security, deep-fake abuse and significant employment effects.

With its demonstrable potential for undertaking a wide range of tasks AI certainly poses a threat to the quality and quantity of a wide range of jobs — and at the limit could eliminate them entirely. And where earlier generations of technological automation impacted simple manual operations or basic tasks AI has the capacity to undertake many complex operations — often doing so faster and more effectively than humans.

AI models like Chat GPT can now routinely pass difficult exams for law or medical school, they can interpret complex data sets and spot patterns better than their human counterparts and they can quickly combine and analyze complex data to arrive at decisions which may often be better quality than those made by even experienced practitioners. Not surprisingly the policy discussion around this potential impact has proliferated at a similarly fast rate, echoing growing public concern about the darker side of AI.

But is it inevitable going to be a case of replacement, with human beings shunted to the side-lines? No-one is sure and it is still early days. We’ve had technological revolutions before — think back fifty years to when we first felt the early shock waves of what was to become the ‘microelectronics revolution’. Newspaper headlines and media programs with provocative titles like ‘Now the chips are down’ prompted frenzied discussion and policy planning for a future world staffed by robots and automated to the point where most activity would be undertaken by automated systems, overseen by one man and a dog. The role of the dog being to act as security guard, the role of the man being confined to feeding the dog.

Automation Man and Dog

This didn’t materialize; as many commentators pointed out at the time and as history has shown there were shifts and job changes but there was also compensating creation of new roles and tasks for which new skills were needed. Change yes — but not always in the negative direction and with growing potential for improving the content and quality of remaining and new jobs.

So if history is any guide then there are some grounds for optimism. Certainly we should be exploring and anticipating and particularly trying to match skills and capacity building to likely future needs.

Not least in the area of innovation management. What impact is AI having — and what might the future hold? It’s certainly implicated in a major shift right across the innovation space in terms of its application. If we take a simple ‘innovation compass’ to map these developments we can find plenty of examples:

Exploring Innovation Space

Innovation in terms of what we offer the world — our products and services — here AI already has a strong presence in everything from toys through intelligent and interactive services on our phones through to advanced weapon systems

And it’s the same story if we look at process innovation — changes in the ways we create and deliver whatever it is we offer. AI is embedded in automated and self-optimizing control systems for a huge range of tasks from mining, through manufacturing and out to service delivery.

Position innovation is another dimension where we innovate in opening up new or under-served markets, and changing the stories we tell to existing ones. AI has been a key enabler here, helping spot emerging trends, providing detailed market analysis and underpinning so many of the platform businesses which effectively handle the connection between multi-sided markets. Think Amazon, Uber, Alibaba or AirBnB and imagine them without the support of AI.

And innovation is possible through rethinking the whole approach to what we do, coming up with new business models. Rethinking the underlying value and how it might be delivered — think Spotify, Netflix and many others replacing the way we consume and enjoy our entertainment. Once again AI step forward as a key enabler.

AI is already a 360 degree solution looking for problems to attach to. Importantly this isn’t just in the commercial world; the power of AI is also being harnessed to enable social innovation in many different ways.

But perhaps the real question is not about AI-enabled innovations but one of how it affects innovators — and the organizations employing them? By now we know that innovation isn’t some magical force that strikes blindly in the light bulb moment. It’s a process which can be organized and managed so that we are able to repeat the trick. And after over 100 years of research and documenting hard-won experience we know the kind of things we need to put in place — how to manage innovation. It’s reached the point where we can codify it into an international standard — ISO 56001- and use this as a template to check out the ways in which we build and operate our innovation management systems.

So how will AI affect this — and, more to the point, how is it already doing so? Let’s take our helicopter and look down on where and how AI playing a role in the key areas of innovation management systems.

Typically the ‘front end’ of innovation involves various kinds of search activity, picking up strong and weak signals about needs and opportunities for change. And this kind of exploration and forecasting is something which AI has already shown itself to be very good at — whether in the search for new protein forms or the generation of ideas for consumer products.

Frank Piller’s research team published an excellent piece last year describing their exploration of this aspect of innovation. They looked at the potential which AI offered and tested their predictions out by tasking Chat GPT with a number of prompts based on the needs of a fictitious outdoor activities company. They had it monitoring and picking up on trends, scraping online communities for early warning signals about new consumer themes and, crucially, actually doing idea generation to come up with new product concepts. Their results mimic many other studies which suggest that AI is very good at this — in fact, as Mollick reports, it often does the job better than humans.

Of course finding opportunities is only the start of the innovation process; a key next stage is some kind of strategic selection. Out of all the possibilities of what we could do, what are we going to do and why? Limited resources mean we have to make choices — and the evidence is that AI is pretty helpful here too. It can explore and compare alternatives, make better bets and build more viable business models to take emerging value propositions forward. (At least in the test case where it competed against MBA students…!)

Innovation Process John Bessant

And then we are in the world of implementation, the long and winding road to converting our value proposition into something which will actually work and be wanted. Today’s agile innovation involves a cycle of testing, trial and error learning, gradually pivoting and homing in on what works and building from that. And once again AI is good at this — not least because it’s at the heart of how it does what it does. There’s a clue in the label — machine learning is all about deploying different learning and improvement strategies. AI can carry out fast experiments and focus in, it can simulate markets and bring to bear many of the adoption influences as probabilistic variables which it can work with.

Of course launching a successful version of a value proposition converted to a viable solution is still only half the innovation journey. To have impact we need to scale — but here again AI is likely to change the game. Much of the scaling journey involves understanding and configuring your solution to match the high variability across populations and accelerate diffusion. We know a lot about what influences this (not least thanks to the extensive work of Everett Rogers) and AI has particular capabilities in making sense of the preferences and predilections of populations through studying big datasets. It’s record in persuasion in fields like election campaigning suggests it has the capacity to enhance our ability to influence the innovation adoption decision process.

Scaling also involves complementary assets — the ‘who else?’ and ‘what else?’ which we need to have impact at scale. We need to assemble value networks, ecosystems of co-operating stakeholders — but to do this we need to be able to make connections. Specifically finding potential partners, forming relationships and getting the whole system to perform with emergent properties, where the whole is greater than the sum of the parts.

And here too AI has an growing track record in enabling recombinant innovation, cross-linking, connecting and making sense of patterns, even if we humans can’t always see them.

So far, so disturbing — at least if you are a practicing innovation manager looking over your shoulder at the AI competition rapidly catching up. But what about the bigger picture, the idea of developing and executing an innovation strategy? Here our concern is with the long-term, managing the process of accumulating competencies and capabilities to create long term competitiveness in volatile and unpredictable markets?

It involves being able to imagine and explore different options and make decisions based on the best use of resources and the likely fit with a future world. Which is, once again, the kind of thing which AI has shown itself to be good at. It’s moved a long way from playing chess and winning by brute calculating force. Now it can beat world champions at complex games of strategy like Go and win poker tournaments, bluffing with the best of them to sweep the pot.

Artificial Intelligence Poker Player

So what are we left with? In many ways it takes us right back to basics. We’ve survived as a species on the back of our imaginations — we’re not big or fast, or able to fly, but we are able to think. And our creativity has helped us devise and share tools and techniques, to innovate our way out of trouble. Importantly we’ve learned to do this collectively — shared creativity is a key part of the puzzle.

We’ve seen this throughout history; the recent response to the Covid-19 pandemic provides yet another illustration. In the face of crisis we can work together and innovate radically. It’s something we see in the humanitarian innovation world and in many other crisis contexts. Innovation benefits from more minds on the job.

So one way forward is not to wring our hands and say that the game is over and we should step back and let the AI take over. Rather it points towards us finding ways of working with it — as Mollick’s book title suggests, learning to treat it as a ‘co-intelligence’. Different, certainly but often in in complementary ways. Diversity has always mattered in innovation teams — so maybe by recruiting AI to our team we amplify that effect. There’s enough to do in meeting the challenge of managing innovation against a background of uncertainty; it makes sense to take advantage of all the help we can get.

AI may seem to point to a direction in which our role becomes superfluous — the ‘no-brain needed’ option. But we’re also seeing real possibilities for it to become an effective partner in the process.

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Image credits: Dall-E via Microsoft CoPilot, John Bessant

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AI Can Help Attract, Retain and Grow Customer Relationships

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AI Can Help Attract, Retain and Grow Customer Relationships

GUEST POST from Shep Hyken

How do you know what your customers want if they don’t tell you? It’s more than sending surveys and interpreting data. Joe Tyrrell is the CEO of Medallia, a company that helps its customers tailor experiences through “intelligent personalization” and automation. I had a chance to interview him on Amazing Business Radio and he shared how smart companies are using AI to build and retain customer relationships. Below are some of his comments followed by my commentary:

The generative AI momentum is so widespread that 85% of executives say the technology will be interacting directly with customers in the next two years. AI has been around for longer than most people realize. When a customer is on a website that makes suggestions, when they interact with a chatbot or get the best answers to frequently asked questions, they are interacting with AI-infused technology, whether they know it or not.
While most executives want to use AI, they don’t know how they want to use it, the value it will bring and the problems it will solve. In other words, they know they want to use it, but don’t know how (yet). Tyrrell says, “Most organizations don’t know how they are going to use AI responsibly and ethically, and how they will use it in a way that doesn’t introduce unintended consequences, and even worse, unintended bias.” There needs to be quality control and oversight to ensure that AI is meeting the goals and intentions of the company or brand.
Generative AI is different than traditional AI. According to Tyrrell, the nature of generative AI is to, “Give me something in real time while I’m interacting with it.” In other words, it’s not just finding answers. It’s communicating with me, almost like human-to-human. When you ask it to clarify a point, it knows exactly how to respond. This is quite different from a traditional search bar on a website—or even a Google search.
AI’s capability to personalize the customer experience will be the focus of the next two years. Based on the comment about how AI technology currently interacts with customers, I asked Tyrrell to be more specific about how AI will be used. His answer was focused on personalization. The data we extract from multiple sources will allow for personalization like never before. According to Tyrrell, 82% of consumers say a personalized experience will influence which brand they end up purchasing from in at least half of all shopping situations. The question isn’t whether a company should personalize the customer experience. It is what happens if they don’t.
Personalization isn’t about being seen as a consumer, but as a person. That’s the goal of personalization. Medallia’s North Star, which guides all its decisions and investments, is its mission to personalize every customer experience. What makes this a challenge is the word every. If customers experience this one time but the next time the brand acts as if they don’t recognize them, all the work from the previous visit along with the credibility built with the customer is eroded.
The next frontier of AI is interpreting social feedback. Tyrrell is excited about Medallia’s future focus. “Surveys may validate information,” says Tyrrell, “but it is often what’s not said that can be just as important, if not even more so.” Tyrrell talked about Medallia’s capability to look everywhere, outside of surveys and social media comments, reviews and ratings, where customers traditionally express themselves. There is behavioral feedback, which Tyrrell refers to as social feedback, not to be confused with social media feedback. Technology can track customer behavior on a website. What pages do they spend the most time on? How do they use the mouse to navigate the page? Tyrell says, “Wherever people are expressing themselves, we capture the information, aggregate it, translate it, interpret it, correlate it and then deliver insights back to our customers.” This isn’t about communicating with customers about customer support issues. It’s mining data to understand customers and make products and experiences better.

Tyrrell’s insights emphasize the opportunities for AI to support the relationship a company or brand has with its customers. The future of customer engagement will be about an experience that creates customer connection. Even though technology is driving the experience, customers appreciate being known and recognized when they return. Tyrrell and I joked about the theme song from the TV sitcom Cheers, which debuted in 1982 and lasted 11 seasons. But it really isn’t a joke at all. It’s what customers want, and it’s so simple. As the song title suggests, customers want to go to a place Where Everybody Knows Your Name.

Image Credits: Unsplash

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

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Drum roll please…

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

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