Tag Archives: AI

Is OpenAI About to Go Bankrupt?

LAST UPDATED: December 4, 2025 at 4:48 PM

Is OpenAI About to Go Bankrupt?

GUEST POST from Chateau G Pato

The innovation landscape is shifting, and the tremors are strongest in the artificial intelligence (AI) sector. For a moment, OpenAI felt like an impenetrable fortress, the company that cracked the code and opened the floodgates of generative AI to the world. But now, as a thought leader focused on Human-Centered Innovation, I see the classic signs of disruption: a growing competitive field, a relentless cash burn, and a core product advantage that is rapidly eroding. The question of whether OpenAI is on the brink of bankruptcy isn’t just about sensational headlines — it’s about the fundamental sustainability of a business model built on unprecedented scale and staggering cost.

The “Code Red” announcement from OpenAI, ostensibly about maintaining product quality, was a subtle but profound concession. It was an acknowledgment that the days of unchallenged superiority are over. This came as competitors like Google’s Gemini and Anthropic’s Claude are not just keeping pace, but in many key performance metrics, they are reportedly surpassing OpenAI’s flagship models. Performance parity, or even outperformance, is a killer in the technology adoption curve. When the superior tool is also dramatically cheaper, the choice for enterprises and developers — the folks who pay the real money — becomes obvious.

The Inevitable Crunch: Performance and Price

The competitive pressure is coming from two key vectors: performance and cost-efficiency. While the public often focuses on benchmark scores like MMLU or coding abilities — where models like Gemini and Claude are now trading blows or pulling ahead — the real differentiator for business users is price. New models, including the China-based Deepseek, are entering the market with reported capabilities approaching the frontier models but at a fraction of the development and inference cost. Deepseek’s reportedly low development cost highlights that the efficiency of model creation is also improving outside of OpenAI’s immediate sphere.

Crucially, the open-source movement, championed by models like Meta’s Llama family, introduces a zero-cost baseline that fundamentally caps the premium OpenAI can charge. Llama, and the rapidly improving ecosystem around it, means that a good-enough, customizable, and completely free model is always an option for businesses. This open-source competition bypasses the high-cost API revenue model entirely, forcing closed-source providers to offer a quantum leap in utility to justify the expenditure. This dynamic accelerates the commoditization of foundational model technology, turning OpenAI’s once-unique selling proposition into a mere feature.

OpenAI’s models, for all their power, have been famously expensive to run — a cost that gets passed on through their API. The rise of sophisticated, cheaper alternatives — many of which employ highly efficient architectures like Mixture-of-Experts (MoE) — means the competitive edge of sheer scale is being neutralized by engineering breakthroughs in efficiency. If the next step in AI on its way to artificial general intelligence (AGI) is a choice between a 10% performance increase and a 10x cost reduction for 90% of the performance, the market will inevitably choose the latter. This is a structural pricing challenge that erodes one of OpenAI’s core revenue streams: API usage.

The Financial Chasm: Burn Rate vs. Reserves

The financial situation is where the “bankruptcy” narrative gains traction. Developing and running frontier AI models is perhaps the most capital-intensive venture in corporate history. Reports — which are often conflicting and subject to interpretation — paint a picture of a company with an astronomical cash burn rate. Estimates for annual operational and development expenses are in the billions of dollars, resulting in a net loss measured in the billions.

This reality must be contrasted with the position of their main rivals. While OpenAI is heavily reliant on Microsoft’s monumental investment — a complex deal involving cash and Azure cloud compute credits — Microsoft’s exposure is structured as a strategic infrastructure play. The real financial behemoth is Alphabet (Google), which can afford to aggressively subsidize its Gemini division almost indefinitely. Alphabet’s near-monopoly on global search engine advertising generates profits in the tens of billions of dollars every quarter. This virtually limitless reservoir of cash allows Google to cross-subsidize Gemini’s massive research, development, and inference costs, effectively enabling them to engage in a high-stakes price war that smaller, loss-making entities like OpenAI cannot truly win on a level playing field. Alphabet’s strategy is to capture market share first, using the profit engine of search to buy time and scale, a luxury OpenAI simply does not have without a continuous cash injection from a partner.

The question is not whether OpenAI has money now, but whether their revenue growth can finally eclipse their accelerating costs before their massive reserve is depleted. Their long-term financial projections, which foresee profitability and revenues in the hundreds of billions by the end of the decade, require not just growth, but a sustained, near-monopolistic capture of the new AI-driven knowledge economy. That becomes increasingly difficult when competitors are faster, cheaper, and arguably better, and have access to deeper, more sustainable profit engines for cross-subsidization.

The Future Outlook: Change or Consequence

OpenAI’s future is not doomed, but the company must initiate a rapid, human-centered transformation. The current trajectory — relying on unprecedented capital expenditure to maintain a shrinking lead in model performance — is structurally unsustainable in the face of faster, cheaper, and increasingly open-source models like Meta’s Llama. The next frontier isn’t just AGI; it’s AGI at scale, delivered efficiently and affordably.

OpenAI must pivot from a model of monolithic, expensive black-box development to one that prioritizes efficiency, modularity, and a true ecosystem approach. This means a rapid shift to MoE architectures, aggressive cost-cutting in inference, and a clear, compelling value proposition beyond just “we were first.” Human-Centered Innovation principles dictate that a company must listen to the market — and the market is shouting for price, performance, and flexibility. If OpenAI fails to execute this transformation and remains an expensive, marginal performer, its incredible cash reserves will serve only as a countdown timer to a necessary and painful restructuring.

Frequently Asked Questions (FAQ)

Is OpenAI currently profitable?
OpenAI is currently operating at a significant net loss. Its annual cash burn rate, driven by high R&D and inference costs, reportedly exceeds its annual revenue, meaning it relies heavily on its massive cash reserves and the strategic investment from Microsoft to sustain operations.
How are Gemini and Claude competing against OpenAI on cost and performance?
Competitors like Google’s Gemini and Anthropic’s Claude are achieving performance parity or superiority on key benchmarks. Furthermore, they are often cheaper to use (lower inference cost) due to more efficient architectures (like MoE) and the ability of their parent companies (Alphabet and Google) to cross-subsidize their AI divisions with enormous profits from other revenue streams, such as search engine advertising.
What was the purpose of OpenAI’s “Code Red” announcement?
The “Code Red” was an internal or public acknowledgment by OpenAI that its models were facing performance and reliability degradation in the face of intense, high-quality competition from rivals. It signaled a necessary, urgent, company-wide focus on addressing these issues to restore and maintain a technological lead.

UPDATE: Just found on X that HSBC has said that OpenAI is going to have nearly a half trillion in operating losses until 2030, per Financial Times (FT). Here is the chart of their $100 Billion in projected losses in 2029. With the success of Gemini, Claude, Deep Seek, Llama and competitors yet to emerge, the revenue piece may be overstated:

OpenAI estimated 2029 financials

Image credits: Google Gemini, Financial Times

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Top 10 Human-Centered Change & Innovation Articles of November 2025

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Top 10 Human-Centered Change & Innovation Articles of November 2025 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 November’s ten most popular innovation posts:

Eight Types of Innovation Executives — by Stefan Lindegaard
Is There a Real Difference Between Leaders and Managers? — by David Burkus
1,000+ Free Innovation, Change and Design Quotes Slides — by Braden Kelley
The AI Agent Paradox — by Art Inteligencia
74% of Companies Will Die in 10 Years Without Business Transformation — by Robyn Bolton
The Unpredictability of Innovation is Predictable — by Mike Shipulski
How to Make Your Employees Thirsty — by Braden Kelley
Are We Suffering from AI Confirmation Bias? — by Geoffrey A. Moore
How to Survive the Next Decade — by Robyn Bolton
It’s the Customer Baby — by Braden Kelley

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

The Great American Contraction – Population, Scarcity, and the New Era of Human Value — by Art Inteligencia
Four Pillars of Innovation — by Mike Shipulski
The Voicebots are Coming — by Braden Kelley
The Future of Humanity in an Artificially Intelligent World — by Greg Satell
Measuring Human vs. AI Satisfaction — by Braden Kelley

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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The Reasons Customers May Refuse to Speak with AI

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The Reasons Customers May Refuse to Speak with AI

GUEST POST from Shep Hyken

If you want to anger your customers, make them do something they don’t want to do.

Up to 66% of U.S. customers say that when it comes to getting help, resolving an issue or making a complaint, they only want to speak to a live person. That’s according to the 2025 State of Customer Service and Customer Experience (CX) annual study. If you don’t provide the option to speak to a live person, you are at risk of losing many customers.

But not all customers feel that way. We asked another sample of more than 1,000 customers about using AI and self-service tools to get customer support, and 34% said they stopped doing business with a company or brand because self-service options were not provided.

These findings reveal the contrasting needs and expectations customers have when communicating with the companies they do business with. While the majority prefer human-to-human interaction, a substantial number (about one-third) not only prefer self-service options — AI-fueled solutions, robust frequently asked question pages on a website, video tutorials and more — but demand it or they will actually leave to find a competitor that can provide what they want.

This creates a big challenge for CX decision-makers that directly impacts customer retention and revenue.

Why Some Customers Resist AI

Our research finds that age makes a difference. For example, Baby Boomers show the strongest preference for human interaction, with 82% preferring the phone over digital solutions. Only half (52%) of Gen-Z feels the same way about the phone. Here’s why:

Lack of Trust: Trust is another concern, with almost half (49%) saying they are scared of technologies like AI and ChatGPT.
Privacy Concerns: Seventy percent of customers are concerned about data privacy and security when interacting with AI.
Success — Or Lack of Success: While I think it’s positive that 50% of customers surveyed have successfully resolved a customer service issue using AI without the need for a live agent, that also means that 50% have not.

Customers aren’t necessarily anti-technology. They’re anti-ineffective technology. When AI fails to understand requests and lacks empathy in sensitive situations, the negative experience can make certain customers want to only communicate with a human. Even half of Gen-Z (48%) says they are frustrated with AI technology (versus 17% of Baby Boomers).

Why Some Customers Embrace AI

The 34% of customers who prefer self-service options to the point of saying they are willing to stop doing business with a company if self-service isn’t available present a dilemma for CX leaders. This can paralyze the decision process for what solutions to buy and implement. Understanding some of the reasons certain customers embrace AI is important:

Speed, Convenience and Efficiency: The ability to get immediate support without having to call a company, wait on hold, be authenticated, etc., is enough to get customers using AI. If you had the choice between getting an answer immediately or having to wait 15 minutes, which would you prefer? (That’s a rhetorical question.)
24/7 Availability: Immediate support is important, but having immediate access to support outside of normal business hours is even better.
A Belief in the Future: There is optimism about the future of AI, as 63% of customers expect AI technologies to become the primary mode of customer service in the future — a significant increase from just 21% in 2021. That optimism has customers trying and outright adopting the use of AI.

CX leaders must recognize the generational differences — and any other impactful differences — as they make decisions. For companies that sell to customers across generations, this becomes increasingly important, especially as Gen-Z and Millennials gain purchasing power. Turning your back on a generation’s technology expectations puts you at risk of losing a large percentage of customers.

What’s a CX Leader To Do?

Some companies have experimented with forcing customers to use only AI and self-service solutions. This is risky, and for the most part, the experiments have failed. Yet, as AI improves — and it’s doing so at a very rapid pace — it’s okay to push customers to use self-service. Just support it with a seamless transfer to a human if needed. An AI-first approach works as long as there’s a backup.

Forcing customers to use a 100% solution, be it AI or human, puts your company at risk of losing customers. Today’s strategy should be a balanced choice between new and traditional customer support. It should be about giving customers the experience they want and expect — one that makes them say, “I’ll be back!”

Image credit: Pixabay

This article originally appeared on Forbes.com

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Re-engineering Trust and Retention in the AI Contact Center

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The Empathy Engine

LAST UPDATED: November 9, 2025 at 1:36PM
Re-engineering Trust and Retention in the AI Contact Center

by Braden Kelley

The contact center remains the single most critical point of human truth for a brand. It is where marketing promises meet operational reality. The challenge today, as highlighted by leaders like Bruce Gilbert of Young Energy at Customer Contact Week(CCW) in Nashville recently, is profound: Customers expect friction-less experiences with empathetic responses. The solution is not merely throwing technology at the problem; it’s about strategically weaving automation into the existing human fabric to create an Empathy Engine.

The strategic error most organizations make is starting with the technology’s capability rather than the human need. The conversation must start with empathy not the technology — focusing first on the customer and agent pain points. AI is not a replacement for human connection; it is an amplification tool designed to remove friction, build trust, and elevate the human agent’s role to that of a high-value relationship manager.

The Trust Imperative: The Cautious Adoption Framework

The first goal when introducing AI into the customer journey is simple: Building trust. The consumer public, after years of frustrating Interactive Voice Response (IVR) systems and rigid chatbots, remains deeply skeptical of automation. A grand, “all-in” AI deployment is often met with immediate resistance, which can manifest as call abandonment or increased churn.

To overcome this, innovation must adhere to a principle of cautious, human-centered rollout — a Cautious Adoption Framework: Starting small and starting with simple things can help to build this trust. Implement AI where the risk of failure is low and the utility is high — such as automating password resets, updating billing addresses, or providing initial diagnostics. These are the repetitive, low-value tasks that bore agents and frustrate customers. By successfully automating these simple, transactional elements, you build confidence in the system, preparing both customers and agents for more complex, AI-assisted interactions down the line. This approach honors the customer’s pace of change.

The Agent Retention Strategy: Alleviating Cognitive Load

The operational cost of the contact center is inextricably linked to agent retention. Finding and keeping high-quality agents remains a persistent challenge, primarily because the job is often highly stressful and repetitive. AI provides a powerful retention tool by directly addressing the root cause: cognitive load.

Reducing the cognitive load and stress level on agents is a non-negotiable step for long-term operational health. AI co-pilots must be designed to act as true partners, not simply data overlays. They should instantly surface relevant knowledge base articles, summarize the customer’s entire history before the agent picks up the call, or even handle real-time data entry. This frees the human agent to focus entirely on the empathetic response — active listening, problem-solving, and de-escalation. By transforming the agent’s role from a low-paid data processor into a high-value relationship manager, we elevate the profession, directly improving agent retention and turning contact center employment into an aspirational career path.

The Systemic Challenge: Orchestrating the AI Ecosystem

A major limiting factor in today’s contact center is the presence of fragmented AI deployments. Many organizations deploy AI in isolated pockets — a siloed chatbot here, a transcription service there. The future demands that we move far beyond siloed AI. The goal is complete AI orchestration across the enterprise, requiring us to get the AIs to talk to each other.

A friction-less customer experience requires intelligence continuity: a Voice AI must seamlessly hand off its collected context to a Predictive AI (which assesses the call risk), which then informs the Generative AI (that drafts the agent’s suggested response). This is the necessary chain of intelligence that supports friction-less service. Furthermore, complexity demands a blended AI approach, recognizing that the solution may involve more than one method (generative vs. directed).

For high-compliance tasks, a directed approach ensures precision: for instance, a flow can insert “read as is” instructions for regulatory disclosures, ensuring legal text is delivered exactly as designed. For complex, personalized problem-solving, a generative approach is vital. The best systems understand the regulatory and emotional context, knowing when to switch modes instantly and without customer intervention.

The Strategic Pivot: Investing in Predictive Empathy

The ultimate strategic advantage lies not in reacting to calls, but in preventing them. This requires a deeper investment in data science, moving from descriptive reporting on what happened to predictive analytics to understand why our customers are calling in before they dial the number.

This approach, which I call Predictive Empathy, uses machine learning to identify customers whose usage patterns, payment history, or recent service interactions suggest a high probability of confusion or frustration (e.g., first-time promotions expiring, unusual service interruptions). The organization then proactively initiates a personalized, AI-assisted outreach to address the problem or explain the confusion before the customer reaches the point of anxiety and makes the call. This shifts the interaction from reactive conflict to proactive support, immediately lowering call volume and transforming brand perception.

The Organizational Checkpoint: Post-Deployment Evolution

Once you’ve successfully implemented AI to address pain points, the work is not finished. A crucial strategic question must be addressed: What happens after AI deployment? What’s your plan?

As AI absorbs simple transactions, the nature of the calls that reach the human agent becomes disproportionately more complex, emotional, and high-value. This creates a skills gap in the remaining human workforce. The organization must plan for and fund the Up-skilling Initiative necessary to handle these elevated interactions, focusing on conflict resolution, complex sales, and deep relationship management. The entire organizational structure — training programs, compensation models, and career paths — must evolve to support this higher-skilled human workforce. By raising the value of the human role, the contact center transitions from a cost center into a profit-generating Relationship Hub.

Conclusion: Architecting the Human Layer

The goal of innovation in the contact center is not the elimination of the human, but the elevation of the human. By using AI to build trust, reduce cognitive load, enable predictive empathy, and connect disparate systems, we free the human agent to deliver on the fundamental customer expectation: a friction-less experience coupled with an empathetic response. This is how we re-engineer the contact center from a cost center into a powerful engine for talent retention and customer loyalty.

“AI handles the transaction. The human handles the trust. Design your systems to protect both.” — Braden Kelley

Your first step into the Empathy Engine: Map the single most stressful task for your top-performing agent and commit to automating 80% of its cognitive load using a simple AI co-pilot within the next 90 days.

What is that task for your organization?

Image credits: Google Gemini

Content Authenticity Statement: The topic area, key elements to focus on, insights captured from the Customer Contact Week session, panelists to mention, etc. were decisions made by Braden Kelley, with a little help from Google Gemini to clean up the article.

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Are We Suffering from AI Confirmation Bias?

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GUEST POST from Geoffrey A. Moore

When social media first appeared on the scene, many of us had high hopes it could play a positive role in community development and civic affairs, as indeed it has. What we did not anticipate was the long-term impact of the digital advertising model that supported it. That model is based on click-throughs, and one of the most effective ways to increase them was to present content that reinforces the recipient’s existing views.

Statisticians call the attraction to one’s existing point of view confirmation bias, and we all have it. As individuals, we believe we are in control of this, but it is obvious that at the level of populations, we are not. Confirmation bias, fed first by social media, and then by traditional media once it is converted to digital, has driven political and social polarization throughout the world. It has been further inflamed by conspiracy theories, malicious communications, fake news, and the like. And now we are faced with the advent of yet another amplifier—artificial intelligence. A significant portion of the fears about how AI could impact human welfare stem from how easily it can be put to malicious use through disinformation campaigns.

The impact of all this on our political life is chilling. Polarized media amplifies the impact of extremism and dampens the impact of moderation. This has most obviously been seen in primary elections, but it has now carried over into general elections to the point where highly unqualified individuals who have no interest in public service hold some of the most important roles in state and federal government. The resulting dysfunction is deeply disturbing, but it is not clear if and where a balance can be found.

Part of the problem is that confirmation bias is an essential part of healthy socialization. It reflects the impact that narratives have on our personal and community identities. What we might see as arrant folly another person sees as a necessary leap of faith. Our founding fathers were committed to protecting our nation from any authority imposing its narratives on unwilling recipients, hence our Constitutional commitment to both freedom of religion and freedom of speech.

In effect, this makes it virtually impossible to legislate our way out of this dilemma. Instead, we must embrace it as a Darwinian challenge, one that calls for us as individuals to adapt our strategies for living to a dangerous new circumstance. Here I think we can take a lesson from our recent pandemic experience. Faced with the threat of a highly contagious, ever-mutating Covid virus, most of the developed economies embraced rapid vaccination as their core response. China, however, did not. It embraced regulation instead. What they and we learned is that you cannot solve problems of contagion through regulation.

We can apply this learning to dealing with the universe of viral memes that have infected our digital infrastructure and driven social discord. Instead of regulation, we need to think of vaccination. The vaccine that protects people from fake news and its many variants is called critical thinking, and the healthcare provider that dispenses it is called public education.

We have spent the past several decades focusing on the STEM wing of our educational system, but at the risk of exercising my own confirmation bias, the immunity protection we need now comes from the liberal arts. Specifically, it emerges from supervised classroom discussions in which students are presented with a wide variety of challenging texts and experiences accompanied by a facilitated dialog that instructs them in the practices of listening, questioning, proposing, debating, and ultimately affirming or denying the validity of the argument under consideration. These discussions are not about promoting or endorsing any particular point of view. Rather, they teach one how to engage with any point of view in a respectful, powerful way. This is the intellectual discipline that underlies responsible citizenship. We have it in our labs. We just need to get it distributed more broadly.

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

Image Credit: Pixabay

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The AI Agent Paradox

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How E-commerce Must Proactively Manage Experiences Created Without Their Consent

LAST UPDATED: November 7, 2025 at 4:31 PM

The AI Agent Paradox

GUEST POST from Art Inteligencia

A fundamental shift is underway in the world of e-commerce, moving control of the customer journey out of the hands of the brand and into the hands of the AI Agent. The recent lawsuit by Amazon against Perplexity regarding unauthorized access to user accounts by its agentic browser is not an isolated legal skirmish; it is a red flag moment for every company that sells online. The core challenge is this: AI agents are building and controlling the shopping experience — the selection, the price comparison, the checkout path — often without the e-commerce site’s knowledge or consent.

This is the AI Agent Paradox: The most powerful tool for customer convenience (the agent) simultaneously poses the greatest threat to brand control, data integrity, and monetization models. The era of passively optimizing a webpage is over. The future belongs to brands that actively manage their relationship with the autonomous, agentic layer that sits between them and their human customers.

The Three Existential Threats of the Autonomous Agent

Unmanaged AI agents, operating as digital squatters on your site, create immediate systemic problems for e-commerce sites:

Data Integrity and Scraping Overload: Agents typically use resource-intensive web scraping techniques that overload servers and pollute internal analytics. The shopping experience they create is invisible to the brand’s A/B testing and personalization engines.
Brand Bypass and Commoditization: Agents prioritize utility over loyalty. If a customer asks for “best price on noise-cancelling headphones,” the agent may bypass your brand story, unique value propositions, and even your preferred checkout flow, reducing your products to mere SKU and price points. This is the Brand Bypass threat.
Security and Liability: Unauthorized access, especially to user accounts (as demonstrated by the Amazon-Perplexity case), creates massive security vulnerabilities and legal liability for the e-commerce platform, which is ultimately responsible for protecting user data.

The How-To: Moving from Resistance to Proactive Partnership

Instead of relying solely on defensive legal action (which is slow and expensive), e-commerce brands must embrace a proactive, human-centered API strategy. The goal is to provide a superior, authorized experience for the AI agents, turning them from adversaries into accelerated sales channels — and honoring the trust the human customer places in their proxy.

Step 1: Build the Agent-Optimized API Layer

Treat the AI agent as a legitimate, high-volume customer with unique needs (structured data, speed). Design a specific, clean Agent API separate from your public-facing web UI. This API should allow agents to retrieve product information, pricing, inventory status, and execute checkout with minimal friction and maximum data hygiene. This immediately prevents the resource-intensive web scraping that plagues servers.

Step 2: Define and Enforce the Rules of Engagement

Your Terms of Service (TOS) must clearly articulate the acceptable use of your data by autonomous agents. Furthermore, the Agent API must enforce these rules programmatically. You can reward compliant agents (faster access, richer data) and throttle or block non-compliant agents (those attempting unauthorized access or violating rate limits). This is where you insert your brand’s non-negotiables, such as attribution requirements or user privacy protocols, thereby regaining control.

Step 3: Offer Value-Added Agent Services and Data

This is the shift from defense to offense. Give agents a reason to partner with you and prefer your site. Offer exclusive agent-only endpoints that provide aggregated, structured data your competitors don’t, such as sustainable sourcing information, local inventory availability, or complex configurator data. This creates a competitive advantage where the agent actually prefers to send traffic to your optimized channel because it provides a superior outcome for the human user.

Case Study 1: The Furniture Retailer and the AI Interior Designer

Challenge: Complex, Multivariable E-commerce Decisions

A high-end furniture and décor retailer struggled with low conversion rates because buying furniture requires complex decisions (size, material, delivery time). Customers were leaving the site to use third-party AI interior design tools.

Proactive Partnership:

The retailer created a “Design Agent API.” This API didn’t just provide price and SKU; it offered rich, structured data on 3D model compatibility, real-time customization options, and material sustainability scores. They partnered with a leading AI interior design platform, providing the agent direct, authorized access to this structured data. The AI agent, in turn, could generate highly accurate virtual room mock-ups using the retailer’s products. This integration streamlined the complex path to purchase, turning the agent from a competitor into the retailer’s most effective pre-visualization sales tool.

Case Study 2: The Specialty Grocer and the AI Recipe Planner

Challenge: Fragmented Customer Journey from Inspiration to Purchase

An online specialty grocer, focused on rare and organic ingredients, saw their customers using third-party AI recipe planners and shopping list creators, which often failed to locate the grocer’s unique SKUs or sent traffic to competitors.

Proactive Partnership:

The grocer developed a “Recipe Fulfillment Endpoint.” They partnered with two popular AI recipe apps. When a user generated a recipe, the AI agent, using the grocer’s endpoint, could instantly check ingredient availability, price, and even offer substitute suggestions from the grocer’s unique inventory. The agent generated a “One-Click, Fully-Customized Cart” for the grocer. The grocer ensured the agent received a small attribution fee (a form of commission), turning the agent into a reliable, high-converting affiliate sales channel. This formalized partnership eliminated the friction between inspiration and purchase, driving massive, high-margin sales.

The Human-Centered Imperative

Ultimately, this is a human-centered change challenge. The human customer trusts their AI agent to act on their behalf. By providing a clean, transparent, and optimized path for the agent, the e-commerce brand is honoring that trust. The focus shifts from control over the interface to control over the data and the rules of interaction. This strategy not only improves server performance and data integrity but also secures the brand’s place in the customer’s preferred, agent-mediated future.

“The AI agent is your customer’s proxy. If you treat the proxy poorly, you treat the customer poorly. The future of e-commerce is not about fighting the agents; it’s about collaborating with them to deliver superior value.” — Braden Kelley

The time to move beyond the reactive defense and into proactive partnership is now. The e-commerce leaders of tomorrow will be the ones who design the best infrastructure for the machines that shop for humans. Your essential first step: Form a dedicated internal team to prototype your Agent API, defining the minimum viable, structured data you can share to incentivize collaboration over scraping.

Image credit: Google Gemini

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Why Going AI Only is Dumb

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I’m Sorry Dave, But I Can’t Do That

LAST UPDATED: November 3, 2025 at 4:50PM

Why Going AI Only is Dumb

by Braden Kelley

Last month I had the opportunity to attend Customer Contact Week (CCW) in Nashville, Tennessee and following up on my article The Voicebots Are Coming I’d like to dig into the idea that companies like Klarna explored of eliminating all humans from contact centers. After all, what could possibly go wrong?

When I first heard that Klarna was going to eliminate humans from their contact centers and go all in on artificial intelligence I thought to myself that they would likely live to regret it. Don’t get me wrong, artificial intelligence (AI) voicebots and chatbots can be incredibly useful, and that proves out in the real world according to conference speakers that almost half of Fanatics calls are automated on the phone without getting to an agent. A lot of people are experimenting with AI but AI is no longer experimental. What Klarna learned is that when you choose to use AI to reduce your number of human agents, then if the AI is down you don’t have the ability anymore to just call in off duty agents to serve your customers.

But, on the flip side we know that having AI customer service agents as part of your agent mix can have very positive impacts on the business. Small businesses like Brothers That Just Do Gutters have found that using AI agents increased their scheduling of estimate visits over humans alone. National Debt Relief automated their customer insufficient funds (CIF) calls and added an escalation path (AI then agent) that delivered a 20% revenue lift over their best agents. They found that when an agent gets a NO, there isn’t much of an escalation path left. And, the delicate reality is that some people feel self conscious calling a human to talk about debt problems, and there may be other sensitive issues where callers would actually feel more comfortable talking to a voicebot than a human. In addition, Fanatics is finding that AI agents are resolving some issues FASTER than human agents. Taken together these examples show that often a hybrid approach (humans plus AI) yields better results than humans only or AI only, so design your approach consciously.

Now let’s look at some important statistics from Customer Management Practice research:

2/3 of people prefer calling in and talking by phone, but most of that is 55+ and the preference percentage declines every ten years younger you go until 30% for 18-24
3/4 of executives say more people want self service now than three years ago
3/4 of people want to spend less time getting support – so they can get back to the fun stuff, or back to business

Taken together these statistics help make the case for increasing the use of AI agents in the contact center. If you happen to be looking to use AI agents in servicing your customers (or even if you already are) then it is important to think about how you can use them to remove friction from the system and to strategically allocate your humans towards things that only humans can do. And if you need to win support from someone to go big with AI voicebots then pick an important use case instead of one that nobody cares about OR even better, pick something that you couldn’t have done before (example: a ride sharing company had AI voicebots make 5 million calls to have drivers validate their tax information).

Finally, as I was listening to some of these sessions it reminded me of a time when I was tasked with finding a new approach for staffing peak season for one of the Blue Cross/Blue Shield companies in the United States. At that time AI voicebots weren’t a thing and so I was looking at how we could partner with a vendor to have a small number of their staff on hand throughout the year and then rely on them to staff and train seasonal staff using those seasoned vendor staff instead of taking the best employees off the phone to train temps.

Even now, all contact centers will still need a certain level of human staffing. But, AI voicebots, AI simulation training for agents, and other new AI powered tools represent a great opportunity for creating a better solution for peak staffing in a whole host of industries with very cyclical contact demand that is hard to staff for. One example of this from Customer Contact Week was a story about how Fanatics must 5x their number of agents during high seasons and in practice this often results in their worst agents (temps they hired only for the season) serving some of their best customers (high $$ value clients).

Conclusion

AI voicebots can be a great help during demand peaks and other AI powered tools (QA, simulations, coaching, etc.) can help accelerate and optimize both your on-boarding of full-time agents, but also of seasonal agents as well. But don’t pare back your human agent pool too far!

What has been your experience with balancing human and AI agents?

Image credits: Google Gemini

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Top 10 Human-Centered Change & Innovation Articles of October 2025

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Top 10 Human-Centered Change & Innovation Articles of October 2025 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 October’s ten most popular innovation posts:

AI, Cognitive Obesity and Arrested Development — by Pete Foley
Making Decisions in Uncertainty – This 25-Year-Old Tool Actually Works — by Robyn Bolton
The Marketing Guide for Humanity’s Next Chapter – How AI Changes Your Customers — by Braden Kelley
Don’t Make Customers Do These Seven Things They Hate — by Shep Hyken
Why Best Practices Fail – Five Questions with Ellen DiResta — by Robyn Bolton
The Need for Organizational Learning — by Mike Shipulski
You Must Accept That People Are Irrational — by Greg Satell
The AI Innovations We Really Need — by Art Inteligencia
Three Reasons You Are Not Happy at Work – And What to Do to Become as Happy as You Could Be — by Stefan Lindegaard
The Nuclear Fusion Accelerator – How AI is Commercializing Limitless Power — by Art Inteligencia

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

McKinsey is Wrong That 80% Companies Fail to Generate AI ROI — by Robyn Bolton
The Most Challenging Obstacles to Achieving Artificial General Intelligence — by Art Inteligencia
The Great American Contraction – Population, Scarcity, and the New Era of Human Value — by Art Inteligencia
Three Steps from Stuck to Success – Managing Uncertainty — by Robyn Bolton
Building a Learning Organization — by Stefan Lindegaard

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

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Cutting-Edge Ways to Decouple Data Growth from Power and Water Consumption

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The Sustainability Imperative

LAST UPDATED: November 1, 2025 at 8:59 AM

Cutting-Edge Ways to Decouple Data Growth from Power and Water Consumption

GUEST POST from Art Inteligencia

The global digital economy runs on data, and data runs on power and water. As AI and machine learning rapidly accelerate our reliance on high-density compute, the energy and environmental footprint of data centers has become an existential challenge. This isn’t just an engineering problem; it’s a Human-Centered Change imperative. We cannot build a sustainable future on an unsustainable infrastructure. Leaders must pivot from viewing green metrics as mere compliance to seeing them as the ultimate measure of true operational innovation — the critical fuel for your Innovation Bonfire.

The single greatest drain on resources in any data center is cooling, often accounting for 30% to 50% of total energy use, and requiring massive volumes of water for evaporative systems. The cutting edge of sustainable data center design is focused on two complementary strategies: moving the cooling load outside the traditional data center envelope and radically reducing the energy consumed at the chip level. This fusion of architectural and silicon-level innovation is what will decouple data growth from environmental impact.

The Radical Shift: Immersive and Locational Cooling

Traditional air conditioning is inefficient and water-intensive. The next generation of data centers is moving toward direct-contact cooling systems that use non-conductive liquids or leverage natural environments.

Immersion Cooling: Direct-to-Chip Efficiency

Immersion Cooling involves submerging servers directly into a tank of dielectric (non-conductive) fluid. This is up to 1,000 times more efficient at transferring heat than air. There are two primary approaches: single-phase (fluid remains liquid, circulating to a heat exchanger) and two-phase (fluid boils off the server, condenses, and drips back down).

This method drastically reduces cooling energy and virtually eliminates water consumption, leading to Power Usage Effectiveness (PUE) ratios approaching the ideal 1.05. Furthermore, the fluid maintains a more stable, higher operating temperature, making the waste heat easier to capture and reuse, which leads us to our first case study.

Case Study 1: China’s Undersea Data Center – Harnessing the Blue Economy

China’s deployment of a commercial Undersea Data Center (UDC) off the coast of Shanghai is perhaps the most audacious example of locational cooling. This project, developed by Highlander and supported by state entities, involves submerging sealed server modules onto the seabed, where the stable, low temperature of the ocean water is used as a natural, massive heat sink.

The energy benefits are staggering: developers claim UDCs can reduce electricity consumption for cooling by up to 90% compared to traditional land-based facilities. The accompanying Power Usage Effectiveness (PUE) target is below 1.15 — a world-class benchmark. Crucially, by operating in a closed system, it eliminates the need for freshwater entirely. The UDC also draws nearly all its remaining power from nearby offshore wind farms, making it a near-zero carbon, near-zero water compute center. This bold move leverages the natural environment as a strategic asset, turning a logistical challenge (cooling) into a competitive advantage.

Case Study 2: The Heat Reuse Revolution at a Major Cloud Provider

Another powerful innovation is the shift from waste heat rejection to heat reuse. This is where true circular economy thinking enters data center design. A major cloud provider (Microsoft, with its various projects) has pioneered systems that capture the heat expelled from liquid-cooled servers and redirect it to local grids.

In one of their Nordic facilities, the waste heat recovered from the servers is fed directly into a local district heating system. The data center effectively acts as a boiler for the surrounding community, warming homes, offices, and water. This dramatically changes the entire PUE calculation. By utilizing the heat rather than simply venting it, the effective PUE dips well below the reported operational figure, transforming the data center from an energy consumer into an energy contributor. This demonstrates that the true goal is not just to lower consumption, but to create a symbiotic relationship where the output of one system (waste heat) becomes the valuable input for another (community heating).

“The most sustainable data center is the one that gives back more value to the community than it takes resources from the planet. This requires a shift from efficiency thinking to regenerative design.”

Innovators Driving the Sustainability Stack

Innovation is happening at every layer, from infrastructure to silicon:

Leading companies and startups are rapidly advancing sustainable data centers. In the cooling space, companies like Submer Technologies specialize in immersion cooling solutions, making it commercially viable for enterprises. Meanwhile, the power consumption challenge is being tackled at the chip level. AI chip startups like Cerebras Systems and Groq are designing new architectures (wafer-scale and Tensor Streaming Processors, respectively) that aim to deliver performance with vastly improved energy efficiency for AI workloads compared to general-purpose GPUs. Furthermore, cloud infrastructure provider Crusoe focuses on powering AI data centers exclusively with renewable or otherwise stranded, environmentally aligned power sources, such as converting flared natural gas into electricity for compute, tackling the emissions challenge head-on.

The Future of Decoupling Growth

To lead effectively in the next decade, organizations must recognize that the convergence of these technologies — immersion cooling, locational strategy, chip efficiency, and renewable power integration — is non-negotiable. Data center sustainability is the new frontier for strategic change. It requires empowered agency at the engineering level, allowing teams to move fast on Minimum Viable Actions (MVAs) — small, rapid tests of new cooling fluids or localized heat reuse concepts — without waiting for monolithic, years-long CapEx approval. By embedding sustainability into the very definition of performance, we don’t just reduce a footprint; we create a platform for perpetual, human-driven innovation.

You can learn more about how the industry is adapting to these challenges in the face of rising heat from AI in the video:

This video discusses the limitations of traditional cooling methods and the necessity of liquid cooling solutions for next-generation AI data centers.

Disclaimer: This article speculates on the potential future applications of cutting-edge scientific research. While based on current scientific understanding, the practical realization of these concepts may vary in timeline and feasibility and are subject to ongoing research and development.

UPDATE: Apparently, Microsoft has been experimenting with underwater data centers for years and you can learn more about them and progress in this area in this video here:

Image credit: Google Gemini

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The Indispensable Role of CX

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Insights from CCW’s 25-Year Journey

LAST UPDATED: October 28, 2025 at 12:00PM

by Braden Kelley

I recently had the privilege of sitting down with Mario Matulich, President of Customer Management Practice, at Customer Contact Week (CCW) in Nashville. As an organization celebrating its 25th anniversary, CCW has been a critical barometer for the entire customer experience and contact center industry. Our conversation wasn’t just a look back, but a powerful exploration of the strategic mandate facing CX leaders today, particularly how we manage innovation and human-centered change in an era dominated by AI and tightening budgets.

CCW at 25: The Hub for Benchmarking and Breakthroughs

Mario underscored that CCW is far more than just a conference; it’s a living repository of industry knowledge. Professionals attend for actionable takeaways, which primarily fall into three categories: benchmarking performance against industry leaders, learning about new trends (like Generative AI’s impact), and, critically, sourcing the right vendors and capabilities needed to execute their strategies. It’s where leaders come to calibrate their investment strategies and learn how to do more with their finite resources.

This pursuit of excellence is driven by a single, powerful market force: The Amazon Effect. As Mario put it, customers no longer judge your experience solely against your industry peers. They expect every single touchpoint with your company to be as seamless, intuitive, and effective as the best experience they’ve had anywhere. This constantly escalating bar for Customer Effort Score (CES) and Customer Satisfaction (CSAT) makes a complacent CX investment a near-fatal strategic mistake. The customer experience must always be top-tier, or you simply lose the right to compete.

The Strategic Disconnect: CX vs. The Contact Center

One of the most valuable parts of our discussion centered on the subtle, yet crucial, distinction between a Customer Experience (CX) professional and a Contact Center (CC) professional. While both are dedicated to the customer journey, their scope and focus often differ:

The CX Professional: Often owns the entire end-to-end customer journey, from marketing to product use to support. Their responsibilities and definition of success are deeply influenced by where CX sits organizationally — is it under Marketing, Operations, or the CEO?
The CC Professional: Focused on the operational efficiency, quality, and effectiveness of the voice and digital support channels. Their reality is one of doing a lot with a little, constantly asked to manage complex interactions while being, ironically, often looked to as a prime source of cuts in a downturn.

Social media, for instance, is still a relevant customer service channel, not just a marketing one. However, the operational reality is that many companies, looking for cost-effective solutions, outsource social media support to Business Process Outsourcing (BPO) providers, highlighting the ongoing tension between strategic experience design and operational efficiency.

“Being a CX leader in your industry is not a temporary investment you can cut and reinstate later. Those who cut, discover quickly that regaining customer trust and market position is exponentially harder than maintaining it.” — Braden Kelley

AI in the Contact Center: From Hypothesis to Hyper-Efficiency

The conversation inevitably turned to the single biggest factor transforming the industry today: Artificial Intelligence. Mario and I agreed that while the promise of AI is vast, the quickest, most immediate win for nearly every organization lies in agent assist.

This is where Generative AI tools empower the human agent in real-time — providing instant knowledge base look-ups, auto-summarizing previous interactions, and drafting responses. It’s a human-centric approach that immediately boosts productivity and confidence, improving Agent Experience (AX) and reducing training time.

However, implementing AI successfully isn’t a “flip-the-switch” deployment. The greatest danger is the wholesale adoption of complex technology without rigor. True AI success, Mario noted, must be implemented via the classic innovation loop: hypothesis, prototyping, and testing. AI isn’t a solution; it’s a tool that must be carefully tuned and validated against human-centered metrics before scaling.

The Mandate for Enduring Investment

A recurring theme was the strategic folly of viewing CX as a cost center. In a downturn, the contact center is often the first place management looks for budgetary reductions. Yet, the evidence is overwhelming: CX leadership is not a temporary investment. When you are leading in your industry in customer experience, that position must be maintained. Cut your investment at your peril, and you risk a long, painful road to recovery when the market turns. The CX team, despite being resource-constrained, often represents the last line of defense for the brand, embodying the human-centered change we preach.

As CCW moves into its next 25 years, the lesson is clear: customer expectations are only rising. The best leaders will leverage AI not just to cut costs, but to augment their people and apply the innovation principles of rigorous testing to truly master the new era of customer orchestration. The commitment to a great customer experience is the single, enduring investment that will future-proof your business.

HALLOWEEN BONUS: Save 30% on the eBook, hardcover or softcover of my latest book Charting Change (now in its second edition) — FREE SHIPPING WORLDWIDE — using code HAL30 until midnight October 31, 2025

Image credits: Customer Management Practice

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Google Gemini to clean up the article.

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