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 March’s ten most popular innovation posts:
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!
SPECIAL BONUS: While supplies last, you can get the hardcover version of my first bestselling book Stoking Your Innovation Bonfire for 44% OFF until Amazon runs out of stock or changes the price. This deal won’t last long, so grab your copy while it lasts!
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:
We’ve all experienced long hold times, repeating our “story” because we get transferred from one customer service rep to another, etc. It’s an ugly customer service experience that gives many customers FORO, or the Fear of Reaching Out.
FORO is the result of an experience marred with time-wasting friction that makes customers dislike having to reach out to customer support. Our annual customer service and Customer Experience (CX) research (sponsored by RingCentral) finds that 43% of customers would rather clean a toilet than call customer support. The support experience can be so bad that 60% of customers admit to hanging up on a support agent, 34% admit to yelling at an agent, and 21% admit to cussing at an agent. So, it does make sense that customers would have FORO due to poor experiences they have had with some companies and brands (not all) in the past.
Because some companies get it right and others don’t, the inconsistency makes the customer wonder what the next time will be like. Their past frustration, anxiety or memory of a bad experience creates the unwillingness to call.
I had a chance to interview Gaurav Passi on Amazing Business Radio, and he introduced me to the concept of FORO. Passi is the co-founder and CEO of Zingly.AI, a platform that empowers customers to collaborate with a company, either through AI or with direct human-to-human interactions, to have their questions answered and problems resolved. Below are some of Passi’s most intriguing points, followed by my commentary:
The next 15-20 years will be about the end customer experience. Many consulting companies and business experts recognize that customer experience is more important than a company’s product. Most customers can buy the same product—or at least similar products—from many different sources. What differentiates the companies and brands that sell these products is the experience. Passi agrees and adds that the way companies deliver support is changing. The future of CX is a blend of AI, digital and human/live support. That prompted me to ask Passi a question that concerns many people, especially customer support agents, “Do you see AI replacing live agents in that time frame?” He answered, “I don’t see a world where humans are completely taken out.”
Customers don’t want to talk to a human being — until they do. Passi says that customers often don’t want to talk to a human. They just want an answer as quickly and efficiently as possible. If they can’t get it, then they want to talk to a human … as quickly and efficiently as possible. Even with many customers desiring this self-service approach, Passi cautions that companies should not make the mistake of 100% deflection to digital self-service. He asks, “Even if you achieve 100% deflection, what will happen to your customer satisfaction (CSAT) scores?” Passi shared an example of a client who had chosen to deflect 100% of customer support to digital self-service and had an outage. Because of the outage, the employees ended up talking to customers, human-to-human. Amazingly — or not — CSAT went up. Why? Passi says, “Because there was a human touch when needed.”
Customer patience is at an all-time low. This is a primary symptom of FORO. Customers don’t have the time or patience to go online to a company’s website, find the customer support number, wait on hold, get authenticated, etc. They want, as Passi calls it, a “One shot, one kill experience.” Using the company’s self-service options, often fueled by AI, you ask a question, and an answer comes back. It’s as simple as that. The customer appreciates not having to get on the phone, wait on hold, etc., etc.
AI is not the final answer! While AI is revolutionizing customer service and support by enabling businesses to scale their operations efficiently, maintaining the human touch with customers to foster genuine relationships is still important. But the human touch doesn’t have to kick in until it’s needed. And in the perfect world, the platform will recognize customers’ reactions when they aren’t getting the answers they need. Passi is proud of what he refers to as “the most magical component we’ve created in the past three years,” which is a technology that understands when the customer is not getting the right answer and seamlessly passes them to a human agent to take over.
If you’ve been following my work, you know I’m focused on helping my clients create amazing customer experiences. As Passi and I wrapped up our interview, he mentioned that amazing is what Zingly is about. He shared that his mission, like mine, is to help his customers create amaZINGLY great experiences for their customers. With an increasing demand for customers to have more control over how, when and on which devices they communicate with businesses, the combination of AI and human expertise, paired with transparency and collaboration with customers, can create a more personalized, effective and amaZINGLY great customer experience.
Image Credit: Pixabay
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Anyone who has used Chat GPT or any of its cousins will testify to its astonishing ability to provide valuable responses to virtually any query. This is hardly a threat—indeed, it is a boon. So, what are we worrying about?
Well, there is the issue of veracity, of course, and it is true, GPT-enabled assistants can indeed make mistakes. But, come on—humans don’t? We are not looking for gospel truth here. We want highly probable, highly informed answers to questions where we need guidance, and it is clear that GPT-enabled applications are outstanding at meeting this need, for at least three reasons. They are remarkably well-informed. They are available 24/7 on demand with no hold time. And they have infinite patience. So, let’s not kid ourselves. We are massively better off for their emergence on the scene.
What we should be worrying about, on the other hand, is their impact on jobs to be done, employment, and career development. A simple way to think about this is that for any of us to earn money, we have to release some form of trapped value. A bank clerk helps a customer get access to the trapped value in their savings account. A bus driver helps a passenger cope with their trapped value by transporting them to the location where they need to be. A lawyer helps a client get access to trapped value by constructing a contract that meets their needs while protecting against risk. A teacher helps a student access trapped value by helping her solve problems she couldn’t handle before. The principle applies to every job. All systems have points of trapped value, and all jobs are organized around releasing and capturing that value.
Now, let’s introduce generative AI. All of a sudden, a whole lot of trapped value that funded a whole lot of jobs can now be released for free (or virtually for free). Those jobs can be protected in the short term but not forever. In other words, the environment really has changed, and we must assess our new circumstances or fall behind. This is Darwinism at work. Evolution never stops. It can’t. As long as there is change, there will be dislocation, which in turn will stimulate innovation. That’s life.
But here’s the good news. The universe can never eliminate trapped value, it can only move it from place to place. That is, there are always emergent problems to solve, always new opportunities to capitalize on, because every system always traps value somewhere. What Darwinism requires is that we detect the new value traps and redirect our activity to engage with them.
Publicly funded agencies sometimes interpret this as a mandate for training programs, but we have to be careful here. Training works well for disseminating established skills that address known problems. It does not work well, however, where the problems are still being determined and the skills are as yet undeveloped. Novelty, in other words, demands creativity. It is simply not negotiable.
Getting back to the impact of generative AI, we should understand that it is an advisory technology. It is not automation. That is, it is not eliminating the need for human beings to make judgment calls. Rather, it is accelerating the preparation for so doing and framing the options in ways that make decision-making more straightforward. By solving for the old value traps, it is giving us the opportunity to up our game. It’s our job to step up to add net new value to the equation.
The best way to do this is to ferret out the emerging new value traps. Who is the customer now? What is the bottleneck that is holding them back? How could that bottleneck be broken open? What is the reward for so doing? These are the fundamental questions that drive any business model. We know how to do this. It’s just that we have been riding on the inertia of the past set of solutions for so long we may have atrophied in some of the muscles we need now. One thing we need not worry about is the universe running out of trapped value. If you are ever in doubt, just read the day’s headlines and be reassured. The world needs our help. Any tool that helps us do our part better is a blessing.
That’s what I think. What do you think?
Image Credit: Pexels
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I’ve been writing a lot about artificial intelligence (AI) and the digital customer experience (CX). Many of the executives I interview and the articles I refer to are all about how AI is revolutionizing, changing, helping and sometimes hurting CX. So we’ve heard from experts. How about if we heard from the customers?
That’s exactly what we did in our annual customer service and CX research sponsored by RingCentral. We asked more than 1,000 U.S. consumers about their experiences with AI and digital customer support, and here are the basic findings for 2024:
The Good
Sixty-two percent of U.S. consumers expect that AI (and related technologies) will be the primary mode of customer service in the future. But how about today? As you will see in some of the findings below, not everyone feels AI is ready for primetime customer service and CX.
Thirty-eight percent believe AI and related technologies will lead to more personalized customer experiences. Personalization has been a hot topic for marketing and CX leaders. AI is giving companies and brands far greater capabilities to use customer data to create a personalized experience. Customers enjoy doing business with companies that recognize them and use the information they have to create a better experience.
Forty-nine percent think AI technologies have the potential to improve the overall customer experience. This is good news, however, the next group of findings shows that companies still have an uphill battle to get customers to adopt and embrace a CX fueled by AI.
The Bad
Only 32% of customers have successfully resolved a customer service issue using AI or ChatGPT technologies. That number is low. One theory is that customers often don’t realize AI is what’s behind what they are doing. Some think AI is chatbots and automated voice response systems that interact with them like a human would or should.
Fifty-six percent of customers admit to being scared of technologies like AI and ChatGPT. Some of these customers may have watched movies where computers take over the world or robots go rogue, none of which are grounded in reality. However, some customers simply don’t trust the technology because of past bad experiences.
Sixty-three percent of customers are frustrated with self-service options using AI, ChatGPT and similar technologies. Frustration is different than being scared, but it has the same impact: customers would rather avoid technology and talk to a live human for support and service.
As I studied the significance of these findings as a whole, the overarching theme of why AI has not caught on as a viable and reliable customer support option is inconsistency. Included in the annual study is a finding that 70% of customers choose talking to a live customer service agent on the phone as their primary channel for customer service.
Why? It’s easier, and customers know what to expect when they talk and interact with live agents. What they don’t want to experience is a self-service solution powered by AI that takes them through a series of prompts that eventually lead to a dead end, where they end up having to call the company anyway.
There’s good reason for the fear and frustration. As more customers are exposed to AI and start to understand it, their inconsistent experiences from one company to the next are creating a confidence problem. The latest technology, which is very cost-effective for even small businesses, has not been purchased and implemented by a majority of businesses.
As of the beginning of this year, just 27% of customers think self-service or automated customer support using AI-powered technology can deliver as good of a customer experience as a live agent. That number will eventually go up, although not as quickly as it needs to. Once companies recognize that bad service equates to lost business, they will make the investment to do it right. It’s not an option if they want their customers to say, “I’ll be back!”
This article was originally published on Forbes.com
Image Credits: Unsplash
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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 January’s ten most popular innovation posts:
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!
SPECIAL BONUS: While supplies last, you can get the hardcover version of my first bestselling book Stoking Your Innovation Bonfire for 44% OFF until Amazon runs out of stock or changes the price. This deal won’t last long, so grab your copy while it lasts!
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:
Welcome to another installment of the “Innovation or Not” series, where we dissect intriguing products and services to determine whether they truly represent ingenuity, or if they’re just another notch on the belt of incremental progress. Today, we’re venturing into the realm of birdwatching — a niche hobby that surprisingly intersects with cutting-edge artificial intelligence and automated insights into our avian neighbors.
Introducing FeatherSnap
The product up for review is the FeatherSnap bird feeder camera. At its core, FeatherSnap is a bird feeder equipped with a camera that not only captures images of our feathery friends but also uses AI to identify species and offer insights to the user. The idea itself blends the tranquility of birdwatching with the technological advancements of AI and machine learning. It also has a smart design to integrate the food storage into the structure itself to save space, and has solar panels to power the onboard technology. But the question remains: is this a pleasant convenience or a groundbreaking innovation?
The Tech Behind FeatherSnap
FeatherSnap integrates a high-quality camera with AI capabilities to recognize and catalog bird species visiting your garden or backyard. It allows the user to receive real-time alerts on their smartphone, providing information about the birds that stop by for a snack. This records data such as the species, time of day, and frequency of visits, creating a rich, personalized avian database over time.
“AI birdwatching may be niche, but it bridges a gap between nature enthusiasts and technology, making the act of observation more engaging and informed.”
Innovation Analysis
When assessing FeatherSnap through the lens of innovation, we explore several key criteria:
Originality: AI-augmented birdwatching is a fresh take on a traditional hobby, significantly enhancing the user experience.
Technology Application: The application of AI in identifying bird species represents an advancement in both hobbyist technology and AI’s practical capabilities.
Value Creation: FeatherSnap adds substantial value to the birdwatching experience by providing educational insights and personalized interaction with nature.
Market Impact: While its potential market may seem limited to bird enthusiasts, the push towards automated, intelligent environmental engagement could have broader applications.
Final Verdict: Innovation or Not?
So, is FeatherSnap an innovation or not? Taking all factors into consideration, I would argue that FeatherSnap qualifies as an innovation. Despite its niche market, it presents a clever integration of AI with everyday life that could inspire further applications across different domains. The product encourages a deeper interaction with nature and presents a template for utilizing technology to enrich leisure activities.
In the broader context of our tech-driven world, FeatherSnap’s introduction to the market both exemplifies ingenuity in leisure tech and challenges developers to think creatively about AI’s scope and potential in nature-based contexts.
As we reflect on this product, it reminds us that innovation isn’t always about life-changing inventions but also about elevating the simple joys of life with smart adaptations.
I encourage you to share your thoughts and opinions on FeatherSnap and whether you consider it groundbreaking or just another incremental product in the tech landscape. Until next time, keep questioning and exploring the ever-changing facets of innovation around you.
Image credit: FeatherSnap
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Recently several people have asked me whether or not artificial intelligence (AI) has a role to play innovation. One of the ways I’ve answered this question is by speaking about how artificial intelligence can be used to help test/disprove assumptions. Innovation always makes assumptions and often the success or failure of any innovation effort is determined by how well the team identifies the critical assumptions to test, those that if incorrectly assumed to be true could later derail the pursuit of innovation or waste limited innovation investment dollars.
But I thought it could be interesting to use AI to answer this question in more detail, leveraging my Eight I’s of Infinite Innovation framework to highlight how artificial intelligence could be used at each step of the continuous innovation journey.
Below you will find a detailed explanation of the Eight I’s of Infinite Innovation framework along with clearly called out contextual responses generated by Microsoft CoPilot detailing how AI could be used productively during that specific phase of the continuous innovation journey from prompts generated by me after uploading a PDF version of the original Eight I’s of Infinite Innovation article (see the link at the bottom).
Creating a Continuous Innovation Capability
To achieve sustainable success at innovation, you must work to embed a repeatable process and way of thinking within your organization, and this is why it is important to have a simple common language and guiding framework of infinite innovation that all employees can easily grasp. If innovation becomes too complex, or seems too difficult then people will stop pursuing it, or supporting it.
Some organizations try to achieve this simplicity, or to make the pursuit of innovation seem more attainable, by viewing innovation as a project-driven activity. But, a project approach to innovation will prevent it from ever becoming a way of life in your organization. Instead you must work to position innovation as something infinite, a pillar of the organization, something with its own quest for excellence – a professional practice to be committed to.
So, if we take a lot of the best practices of innovation excellence and mix them together with a few new ingredients, the result is a simple framework organizations can use to guide their pursuit of continuous innovation – the Eight I’s of Infinite Innovation. This framework anchors what is a very collaborative process. Here is the framework and some of the many points organizations must consider during each stage of the continuous process:
1. Inspiration
Employees are constantly navigating an ever changing world both in their home context, and as they travel the world for business or pleasure, or even across various web pages in the browser of their PC, tablet, or smartphone.
What do they see as they move through the world that inspires them and possibly the innovation efforts of the company?
What do they see technology making possible soon that wasn’t possible before?
The first time through we are looking for inspiration around what to do, the second time through we are looking to be inspired around how to do it.
What inspiration do we find in the ideas that are selected for their implementation, illumination and/or installation?
How to leverage artificial innovation during the Inspiration phase (according to AI):
AI can help employees find inspiration by analyzing vast amounts of data from various sources, such as social media, news articles, and industry reports. By identifying emerging trends and patterns, AI can provide insights into what is possible and inspire new ideas for innovation. Additionally, AI-powered tools can help employees visualize potential solutions and explore creative possibilities.
2. Investigation
What can we learn from the various pieces of inspiration that employees come across?
How do the isolated elements of inspiration collect and connect? Or do they?
What customer insights are hidden in these pieces of inspiration?
What jobs-to-be-done are most underserved and are worth digging deeper on?
Which unmet customer needs that we see are worth trying to address?
Which are the most promising opportunities, and which might be the most profitable?
How to leverage artificial innovation during the Inspiration phase (according to AI):
AI can assist in the investigation phase by processing and analyzing large datasets to uncover hidden insights and customer needs. Machine learning algorithms can identify patterns and correlations that may not be immediately apparent to humans, helping organizations understand which opportunities are most promising and worth pursuing. AI can also automate the process of gathering and organizing information, making it easier for employees to focus on deeper analysis.
3. Ideation
We don’t want to just get lots of ideas, we want to get lots of good ideas
Insights and inspiration from first two stages increase relevance and depth of the ideas
We must give people a way of sharing their ideas in a way that feels safe for them
How can we best integrate online and offline ideation methods?
How well have we communicated the kinds of innovation we seek?
Have we trained our employees in a variety of creativity methods?
How to leverage artificial innovation during the Inspiration phase (according to AI):
AI can enhance the ideation process by generating a wide range of ideas based on input from employees and external sources. Natural language processing (NLP) algorithms can analyze and categorize ideas, making it easier to identify the most relevant and promising ones. AI-powered collaboration tools can also facilitate brainstorming sessions, allowing employees to share and build on each other’s ideas in real-time, regardless of their physical location.
4. Iteration
No idea emerges fully formed, so we must give people a tool that allows them to contribute ideas in a way that others can build on them and help uncover the potential fatal flaws of ideas so that they can be overcome
We must prototype ideas and conduct experiments to validate assumptions and test potential stumbling blocks or unknowns to get learnings that we can use to make the idea and its prototype stronger
Are we instrumenting for learning as we conduct each experiment?
How to leverage artificial innovation during the Inspiration phase (according to AI):
AI can support the iteration phase by providing tools for rapid prototyping and experimentation. Machine learning models can simulate different scenarios and predict potential outcomes, helping teams identify and address potential flaws in their ideas. AI can also automate the process of collecting and analyzing feedback from experiments, enabling continuous improvement and refinement of prototypes.
5. Identification
In what ways do we make it difficult for customers to unlock the potential value from this potentially innovative solution?
What are the biggest potential barriers to adoption?
What changes do we need to make from a financing, marketing, design, or sales perspective to make it easier for customers to access the value of this new solution?
Which ideas are we best positioned to develop and bring to market?
What resources do we lack to realize the promise of each idea?
Based on all of the experiments, data, and markets, which ideas should we select?
How to leverage artificial innovation during the Inspiration phase (according to AI):
AI can help organizations identify the most viable ideas by analyzing data from experiments, market research, and customer feedback. Predictive analytics can assess the potential success of different ideas and prioritize those with the highest likelihood of success. AI can also identify potential barriers to adoption and suggest strategies to overcome them, ensuring that innovative solutions are accessible and valuable to customers.
You’ll see in the framework that things loop back through inspiration again before proceeding to implementation. There are two main reasons why. First, if employees aren’t inspired by the ideas that you’ve selected to commercialize and some of the potential implementation issues you’ve identified, then you either have selected the wrong ideas or you’ve got the wrong employees. Second, at this intersection you might want to loop back through the first five stages though an implementation lens before actually starting to implement your ideas OR you may unlock a lot of inspiration and input from a wider internal audience to bring into the implementation stage.
6. Implementation
What are the most effective and efficient ways to make, market, and sell this new solution?
How long will it take us to develop the solution?
Do we have access to the resources we will need to produce the solution?
Are we strong in the channels of distribution that are most suitable for delivering this solution?
How to leverage artificial innovation during the Inspiration phase (according to AI):
AI can streamline the implementation process by optimizing production, marketing, and sales strategies. AI-powered project management tools can help teams plan and execute tasks more efficiently, while machine learning algorithms can optimize supply chain and distribution processes. AI can also personalize marketing campaigns and sales approaches, ensuring that new solutions reach the right customers at the right time.
7. Illumination
Is the need for the solution obvious to potential customers?
Are we launching a new solution into an existing product or service category or are we creating a new category?
Does this new solution fit under our existing brand umbrella and represent something that potential customers will trust us to sell to them?
How much value translation do we need to do for potential customers to help them understand how this new solution fits into their lives and is a must-have?
Do we need to merely explain this potential innovation to customers because it anchors to something that they already understand, or do we need to educate them on the value that it will add to their lives?
How to leverage artificial innovation during the Inspiration phase (according to AI):
AI can enhance the illumination phase by helping organizations communicate the value of their innovations to potential customers. NLP algorithms can generate compelling marketing content and product descriptions, while sentiment analysis can gauge customer reactions and adjust messaging accordingly. AI can also identify key influencers and target them with personalized messages to amplify the reach of new solutions.
8. Installation
How do we best make this new solution an accepted part of everyday life for a large number of people?
How do we remove access barriers to make it easy as possible for people to adopt this new solution, and even tell their friends about it?
How do we instrument for learning during the installation process to feedback new customer learnings back into the process for potential updates to the solution?
How to leverage artificial innovation during the Inspiration phase (according to AI):
AI can facilitate the installation of new solutions by removing barriers to adoption and ensuring a seamless customer experience. AI-powered customer support tools can provide instant assistance and troubleshooting, while machine learning algorithms can personalize onboarding processes to meet individual customer needs. AI can also monitor usage patterns and gather feedback, enabling continuous improvement and updates to the solution.
Conclusion
The Eight I’s of Infinite Innovation framework is designed to be a continuous learning process, one without end as the outputs of one round become inputs for the next round. It’s also a relatively new guiding framework for organizations to use, so if you have thoughts on how to make it even better, please let me know in the comments. The framework is also ideally suited to power a wave of new organizational transformations that are coming as an increasing number of organizations (including Hallmark) begin to move from a product-centered organizational structure to a customer needs-centered organizational structure. The power of this new approach is that it focuses the organization on delivering the solutions that customers need as their needs continue to change, instead of focusing only on how to make a particular product (or set of products) better.
By leveraging AI at each stage of the innovation process, organizations can enhance their ability to generate, develop, and implement successful innovations.
So, as you move from the project approach that is preventing innovation from ever becoming a way of life in your organization, consider using the Eight I’s of Infinite Innovation to influence your organization’s mindset and to anchor your common language of innovation. The framework is great for guiding conversations, making your innovation outputs that much stronger, and will contribute to your quest for innovation excellence – it is even more powerful when you combine it with my Value Innovation Framework (found here). The two are like chocolate and peanut butter. They’re powerful tools when used separately, but even more powerful when used together.
People who upgrade to the Bronze Version of the Change Planning Toolkit™ will get access to my Innovation Planning Canvas™ which combines the Value Innovation Framework together with the Eight I’s of Infinite Innovation, allowing you to track the progress of each potential innovation on the three value innovation measures as you evolve any individual idea through this eight step process.
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Oh, what a difference a year makes. A few months ago I traveled to Las Vegas to attend the Customer Contact Week (CCW), the largest conference and trade show in the contact center industry. For the past several years, the big discussion has centered on artificial intelligence (AI), and that continues, but Customer Experience (CX) is also moving into the spotlight. AI and natural language models can give customers an almost human-like experience when they have a question or complaint. However, no surprise, some companies do it better than others.
First, all the hype around AI is not new. AI has been in our lives for decades, just at a much simpler level. How do you think Outlook and other email companies recognize that an email is spam and belongs in the junk/spam folder? Of course, it’s not 100% perfect, and neither are today’s best AI programs.
Many of us use Siri and Alexa. That’s AI. And as simple as that is, it’s obviously more sophisticated when you apply it to customer support and CX.
Let’s go back 10 years ago when I attended the IBM Watson conference in Las Vegas. The big hype then was around AI. There were some incredible cases of AI changing customer service, sales and marketing, not to mention automated processes. One of the demonstrations during the general session showcased AI’s stunning capability. Here’s what I saw:
A customer called the contact center. While the customer service agent listened to the customer, the computer (fueled by AI) listened to the conversation and fed the agent answers without the agent typing the questions. In addition, the computer informed the agent how long the customer had been doing business with the company, how often they made purchases, what products they had bought and more. The computer also compared this customer to others who had the same questions and suggested the agent answer those questions. Even though the customer didn’t yet know to ask them, at some point in the future, they would surely be calling back to do so.
That demonstration was a preview of what we have today. One big difference is that implementing that type of solution back then could have cost hundreds of thousands of dollars, if not more than a million. Today, that technology is affordable to almost any company, costing a fraction of what it cost back then (as in just a few thousand dollars).
Voice Technology Gets Better
Less than two years ago, ChatGPT was introduced to the world. Similar technologies have been developed. The capability continues to improve at an incredibly rapid pace. The response from an AI-fueled chatbot is lightning fast. Now, the technology is moving to voice. Rather than type a question for the chatbot, you talk, and it responds in a human-like voice. While voice technology has existed for years, it’s never been this good. Google introduced voice technology that seemed almost human-like. The operative word here is almost. As good as it was, people could still sense they weren’t talking to a human. Today, the best systems are human-like, not almost human-like. Think Alexa and Siri on steroids.
Foreign Accents Are Disappearing
We’ve all experienced calling customer support, and an offshore customer service agent with a heavy accent answers the call. Sometimes, it’s nearly impossible to understand the agent. New technologies are neutralizing accents. A year ago, the software sounded a little “digital.” Today, it sounds almost perfect.
Why Customers Struggle with AI and Other Self-Service Solutions
As far as these technologies have come, customers still struggle to accept them. Our customer service research (sponsored by RingCentral) found that 63% of customers are frustrated by self-service options, such as ChatGPT and similar technologies. Furthermore, 56% of customers admit to being scared of these technologies. Even though 32% of the customers surveyed said they had successfully resolved a customer service issue using AI or ChatGPT-type technologies, it’s not their top preference as 70% still choose the phone as their first level of support. Inconsistency is part of the problem. Some companies still use old technology. The result is that the customer experience varies from company to company. In other words, customers don’t know whether the next time they experience an AI solution if it will be good or not. Inconsistency destroys trust and confidence.
Companies Are Investing in Creating a Better CX
I’ve never been more excited about customer service, CX and the contact center. The main reason is that almost everything about this conference was focused on creating a better experience for the customer. The above examples are just the tip of the iceberg. Companies and brands know what customers want and expect. They know the only way to keep customers is to give them a product that works with an experience they can count on. Price is no longer a barrier as the cost of some of these technologies has dropped to a level that even small companies can afford.
Customer Service Goes Beyond Technology: We Still Need People!
This article focused on the digital experience rather than the traditional human experience. But to nail it for customers, a company can’t invest in just tech. It must also invest in its employees. Even the best technology doesn’t always get the customer what they need, which means the customer will be transferred to a live agent. That agent must be properly trained to deliver the experience that gets customers to say, “I’ll be back.”
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 November’s ten most popular innovation posts:
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!
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Looking back at the beginning of this decade now that we’re closing in on the halfway point, it’s clearly been a wild ride!
We’ve had a global pandemic, groundbreaking technological breakthroughs, geopolitical shocks, supply chain disruptions, and so much more.
These challenges have revealed a critical truth: organizations need to adapt and innovate faster than ever before.
Add to this the tough economic climate, shrinking capital availability, the disillusionment many business leaders feel toward their innovation teams (sometimes justified, sometimes less so), and we’re looking at a highly turbulent environment for corporate innovation.
The mandate has never been so clear: deliver more results, faster, and with fewer resources. For seasoned innovators, that’s just business as usual. However, structural shifts are poised to reshape the innovation management landscape.
With that background, here’s our take on the top trends to watch in 2025.
1. Innovation as a Distributed Core Capability
With tighter budgets, the rise of AI and other transformative technologies, the pressing need for organizations to reinvent themselves, and you can see why innovation is increasingly owned by individual business units.
This shift can arise from necessity—businesses needing to transform—or simply from a desire for better strategic alignment and more measurable outcomes.
Don’t get me wrong, there’s still a need for innovation expertise, but the role of corporate innovators is undoubtedly evolving. Instead of driving innovation directly, they are now enablers and educators, equipping the broader organization to innovate effectively. Embodying this phenomenon is TD Bank, for example:
“The program is truly driven by each line of business—we’re here as a tool to empower their innovation, not to direct it.”
– Josh Death, VP of Intellectual Property and Ideation at TD Bank.
To pull that off, every organization needs to have 3 key elements in place:
Innovation is now at a similar transition point as IT was during the digital transformation era a couple of decades ago: the exact method and approach can be debated, but one thing is clear: every organization must embed innovation as a core capability. Just as some organizations are “digital natives,” the situation is the same for “innovation natives.”
Frameworks, toolkits, and best practices: Innovation isn’t (always) rocket science, but you still need to know what you’re doing. To pull this off, the organization needs to provide its employees with practical tools, frameworks and practices, preferably in the format of a well-designed Innovation System or Program. The recently published ISO 56000 series of standards is now a great starting point, but they need to be complemented with tools that innovators across the organization can use.
Education, coaching, and enablement: A good framework serves as an efficient and effective launching pad, but without proper education, most employees won’t benefit from it. This is where corporate innovation leaders play a key role. They need to organize education and enablement for innovators across the organization, and coach people on how to get past common obstacles. However, doing that at the scale of a large organization is complex—that’s where programs such as The Innovation System, which is included for all HYPE software customers, can be highly effective.
Scalable and adaptive system support: To get measurable outcomes from innovation, you need to operationalize your program. Even the best designed programs with highly effective leaders and coaches can struggle to scale their work and get the outcomes they want without proper system support. That’s where a holistic innovation platform, such as the HYPE Suite, can play a key supporting role.
Generative AI has been the focus of most of the hype around AI lately, and for good reason, but there’s more to AI than that. When you combine the latest generative AI models with proven innovation best practices, more traditional machine learning algorithms, and data from your innovation ecosystem, you have a powerful toolkit that enables a variety of different use cases.
AI can:
Analyze and structure large datasets.
Provide actionable recommendations.
Help users locate relevant information more efficiently.
Detect market signals earlier.
Generate novel ideas.
Coach innovators to enhance their work.
The common denominator for all of them is that AI can help streamline, automate, and accelerate work, and provide easier access to information and skills that used to be the domain of only a few experts within the organization.
However, scaling AI’s benefits isn’t without challenges. Most employees aren’t going to be expert prompters or data analysts that know all the right innovation best practices. So, to unlock the real benefits of using AI, you’re going to need a capable system that is specifically designed for corporate innovation and deeply integrated with AI across the board. When deployed right, AI can help democratize, scale and accelerate innovation like never before.
3. Democratization of Innovation
The third trend builds on the first two. As innovation becomes a core capability better supported by tools, processes, and technology, it will also become more democratized.
Here are the three key shifts are driving this transformation:
Innovation tools, frameworks, and best practices are becoming more widely available, understood, and easier to use: This makes it easier for anyone that wants to be an innovator to get started on the right path and avoid many of the common beginner mistakes.
Technology reduces barriers to entry: Thanks to technologies such as 3D printing, low or no-code software, and Gen AI, it’s never been easier, faster, and cheaper to prototype innovations, whether focused on digital solutions, physical products, or process improvements.
Organizations are looking for more bottom up, employee and team-led innovation and intrapreneurship: Corporate innovation is no longer solely driven by top management. While management needs to set the strategy and targets, more and more organizations are looking towards empowering their employees to help them get where they want to go. It all starts from ideas, but self-organized teams, business units, and intrapreneurship programs are all on the rise. Companies increasingly want to encourage employees to think and act more like entrepreneurs.
When you put all three together, they create a powerful combination that can propel organizations to new heights of innovation and growth.
4. Partner Innovation and the Venture Client Model
No organization, no matter how large or powerful, can house all the best talent on every topic. That’s why the “Not Invented Here” syndrome can be particularly dangerous.
When you need to move fast, and do so with a lower budget, your best bet is to leverage talent from outside your organization.
The trick? Partnering with leaders and early movers in your area of interest to accelerate time to market and gain valuable insights. These partners can include research institutes, universities, or, increasingly, startups.
Historically, large organizations have relied on accelerators or Corporate Venture Capital (CVC) investments to engage with startups. However, both approaches have limitations:
Learning is indirect and secondhand.
They often fail to directly contribute to strategic business goals.
CVC investments require significant capital that could be allocated elsewhere.
The better approach? The Venture Client Model. This approach allows organizations to act as customers and development partners to startups that align with their strategic goals, resulting in:
Lower costs and faster time to market.
Accelerated learning through direct engagement.
Quick ROI by leveraging the organization’s existing scale.
To succeed with this model, you need a systematic approach, the right tools—like HYPE Partnering—and a clear focus on addressing real business problems, not just nice to haves.
The Venture Client Model, featured in Gartner’s latest Hype Cycle for Innovation Practices, brings all these elements together, making it a proven and effective strategy for driving innovation.
5. Cross-industry Collaboration
Building on the trend of partnering, companies are increasingly looking beyond their industries to find innovation opportunities.
Experienced innovators know that there’s no such thing as a new idea. Every idea is simply a combination of previous concepts and ideas applied to solve a specific problem. By partnering with organizations in different industries, companies can leverage highly advanced, specialized capabilities to uncover surprising opportunities and tackle the often-difficult execution phase of innovation.
As such, we’re seeing more and more strategic partnerships between companies from different industries, such as automotive or life science firms partnering with tech companies, to not just learn from one another, but to cocreate hybrid solutionsand products that unlock new value for customers and enable breakthroughs that neither industry could achieve alone.
6. Sustainability and ESG-driven Innovation
Last decade, ESG (Environmental, Social, and Governance) was all the rage. In the last couple of years, many of these initiatives took a backseat due to economic pressures and growing disillusionment with some of the failures associated with many of these programs.
The problem was that many organizations implemented ESG at a superficial level—promises and policies with little real-world impact—leading to skepticism about the value behind the topic at large.
However, the fundamental need for transformation remains critical. From addressing government deficits to combating climate change, the urgency for sustainable innovation is greater than ever.
What’s different now? The drivers and enablers are firmly in place:
Regulatory Pressure: Many governments across the globe are introducing stricter mandates for sustainable practices.
Technological Advancements: Breakthroughs in renewable energy, electrification, AI, and circular solutions provide tools for real change.
Consumer Preferences: Shifts toward sustainability are influencing demand and shaping circular economic models.
For innovators, this is a perfect storm—a unique opportunity to create breakthroughs that move the needle for both their organizations and the planet. Sustainability has been through the Hype Cycle, and is now nearing the plateau of productivity. For many, it’s no longer a “nice-to-have” but a strategic imperative, making ESG-driven innovation one of the most significant trends shaping the future of corporate innovation and strategy.
Conclusion
These trends highlight a clear shift toward more agile, sustainable, and externally focused innovation practices. For many organizations, they’re not just a nice addition, but a must to stay competitive in increasingly complex and fast-moving global markets. What hasn’t changed, is that those organizations that master innovation, unlock new opportunities to create value, drive impact. They will be able to future-proof themselves and leave the competition in the dust.
This article was originally published in HYPE’s blog. Images from Unsplash and Pixabay.
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