Tag Archives: AI

The AI Agent Paradox

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

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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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.

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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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The Voicebots are Coming

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Your Next Customer Support Agent May Not Be a Human

LAST UPDATED: October 27, 2025 at 1:00PM
The Voicebots are Coming

by Braden Kelley

Last week I had the opportunity to attend Customer Contact Week (CCW) in Nashville, Tennessee and learn that the familiar, frustrating tyranny of the touch-tone IVR (Interactive Voice Response) system is finally ending. For too long, the gateway to customer service has felt like a maze designed to prevent contact, not facilitate it. But thanks to the rapid evolution of Conversational AI — fueled by Generative Large Language Models (LLMs) — the entire voice interaction landscape is undergoing a revolutionary, and necessary, change. As a thought leader focused on human-centered change, innovation and experience design, I can tell you the future of the call center isn’t purely automated; it’s intelligently orchestrated.

The voicebot — the modern AI-powered voice agent — is moving past its days as a simple chatbot with a synthesized voice. Today’s AI agents use Natural Language Processing (NLP) to understand intent, context, and even tone, allowing them to handle complex, multi-step issues with startling accuracy. More importantly, they are ushering in the era of the bionic contact center, where the human agent is augmented, not replaced. This hybrid model — where AI handles the heavy lifting and humans provide empathy, complex reasoning, and necessary approvals — is the key to achieving both massive scale and superior Customer Experience (CX).

Overcoming the Voice Friction: The Tech Foundation

The shift to true voice AI required overcoming significant friction points that plagued older systems:

Barge-In and Latency: Modern voicebots offer near-instantaneous response times and can handle barge-in (when a customer interrupts the bot) naturally, mimicking human conversation flow.
Acoustic Noise: Advanced speech recognition models are highly resilient to background noise and varied accents, ensuring high accuracy even in noisy home or car environments.
Intent Nuance: LLMs provide the deep contextual understanding needed to identify customer intent, even when the customer uses vague or emotional language, turning frustrated calls into productive ones.

The Dual Pillars of Voice AI in CX

Conversational AI is transforming voice service through two primary deployment models, both of which reduce Customer Effort Score (CES) and boost Customer Satisfaction (CSAT):

1. Full Call Automation (The AI Front Line)

This model is deployed for high-volume, routine, yet critical interactions. The voicebot connects directly to the company’s backend systems (CRM, ERP, knowledge base) to pull personalized information and take action in real-time. Crucially, these new AI agents move beyond rigid scripts, using Generative AI to create dynamic, human-like dialogue that resolves the issue instantly. This 24/7 self-service capability slashes queue times and dramatically lowers the cost-to-serve.

2. Human-AI Collaboration (The Bionic Agent)

This is where the real human-centered innovation lies. The AI agent handles the bulk of the call — identifying the customer, verifying identity, diagnosing the problem, and gathering data. When the request hits a complexity threshold — such as requiring a policy override, handling an escalated complaint, or needing a final human authorization — the AI performs a contextual handoff. The human agent receives the call along with a complete, structured summary of the conversation, the customer’s intent, and often a recommended next step, turning a frustrating transfer into a seamless, empowered human interaction.

OR, even better can be the solution where a single human agent provides approvals or other guidance to multiple AI voice agents that continue owning their calls while waiting for the human to respond (possibly simultaneously helping the customer with additional queries) before continuing with the conversation through to resolution.

Customer Contact Week Nashville

“The most powerful application of voice AI isn’t automation, it’s augmentation. By freeing human agents from transactional drudgery, we elevate them to be empathic problem solvers, enhancing both their job satisfaction and the customer’s outcome.” — Braden Kelley

Measuring the Success of the Handoff

The quality of the transitions between AI and human is the true measure of success. Leaders must track metrics that assess the efficacy of the handoff itself:

Repeat Story Rate: The percentage of customers who have to repeat information to the human agent after an AI handoff. This must be near zero.
Agent Ramp-up Time (Post-Transfer): The time it takes for the human agent to absorb the AI-generated context and take meaningful action. Lower is better.
Post-Handoff CSAT: The customer satisfaction score specifically captured after a complex AI-to-human transfer, measuring the seamlessness of the experience.

The Agentic Future

The voicebots are indeed coming, and they are bringing with them the most significant shift in customer service since the telephone itself. The next evolution will see agentic AI — bots that can dynamically choose between multiple tools and knowledge sources to resolve novel problems without being strictly pre-scripted. The challenge for leaders is to ensure that as this technology scales, our focus remains firmly on the human experience, leveraging the best of AI’s speed and the best of human empathy to create a truly effortless and satisfying customer journey.

🤖 Companies to Watch in AI Voicebots

The voicebot space is rapidly evolving, driven by generative AI, and the recent Customer Contact Week (CCW) in Nashville highlighted several key players. Companies to watch in this generative AI voicebot and contact center space include market-leading platforms like NICE, Genesys, Zoom and Five9, all of whom are heavily integrating generative and agentic AI features—such as real-time coaching and automated post-call summaries — into their core Contact Center as a Service (CCaaS) offerings.

Beyond the traditional CCaaS providers, specialist AI firms like Replicant, Voice.AI and ASAPP (who had a significant presence at the event) continue to stand out by focusing on either full end-to-end voice automation for complex transactions or providing advanced Human-in-the-Loop AI features to augment live agents, particularly in regulated industries like financial services.

Additionally, major cloud vendors like Google Cloud and AWS (Amazon Connect) are increasingly aggressive, leveraging their foundational AI models to provide scalable, next-generation AI agents and contact center platforms, ensuring they remain transformative forces in customer experience (CX) automation.

Image credits: Customer Management Practice, Google Gemini

Content Authenticity Statement: The topic area, key elements to focus on, vendors 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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How Cobots are Humanizing the Factory Floor

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The Collaborative Revolution

LAST UPDATED: October 25, 2025 at 4:33PM
How Cobots are Humanizing the Factory Floor - The Collaborative Revolution

GUEST POST from Art Inteligencia

For decades, industrial automation has been defined by isolation. Traditional robots were caged behind steel barriers, massive, fast, and inherently dangerous to humans. They operated on the principle of replacement, seeking to swap out human labor entirely for speed and precision. But as a thought leader focused on human-centered change and innovation, I see this model as fundamentally outdated. The future of manufacturing, and indeed, all operational environments, is not about replacement — it’s about augmentation.

Enter the Collaborative Robot, or Cobot. These smaller, flexible, and safety-certified machines are the definitive technology driving the next phase of the Industrial Revolution. Unlike their predecessors, Cobots are designed to work alongside human employees without protective caging. They are characterized by their force-sensing capabilities, allowing them to stop instantly upon contact, and their ease of programming, often achieved through simple hand-guiding (or “teaching”). The most profound impact of Cobots is not on the balance sheet, but on the humanization of work, transforming dull, dirty, and dangerous tasks into collaborative, high-value roles. This shift requires leaders to address the initial psychological barrier of automation, re-framing the technology as a partner in productivity and safety.

The Three Pillars of Cobot-Driven Human-Centered Innovation

The true value of Cobots lies in how they enable the three core tenets of modern innovation:

1. Flexibility and Agility: Cobots are highly portable and quick to redeploy. A human worker can repurpose a Cobot for a new task — from picking parts to applying glue — in a matter of hours. This means production lines can adapt to short runs and product customization far faster than large, fixed automation systems, giving businesses the agility required in today’s volatile market.
2. Ergonomic and Safety Improvement: Cobots take on the ergonomically challenging or repetitive tasks that lead to human injury (like repeated lifting, twisting, or precise insertion). By handling the “Four Ds” (Dull, Dirty, Dangerous, and Difficult-to-Ergonomically-Design), they dramatically improve worker health, morale, and long-term retention.
3. Skill Elevation and Mastery: Instead of being relegated to simple assembly, human workers are freed to focus on high-judgment tasks: quality control, complex troubleshooting, system management, and, crucially, Cobot programming and supervision. This elevates the entire workforce, shifting roles from manual labor to process management and robot literacy.

“Cobots are the innovation that tells human workers: ‘We value your brain and your judgment, not just your back.’ The factory floor is becoming a collaborative workspace, not a cage, but leaders must proactively communicate the upskilling opportunity.”

Case Study 1: Transforming Aerospace Assembly with Human-Robot Teams

The Challenge:

A major aerospace manufacturer faced significant challenges in the final assembly stage of large aircraft components. Tasks involved repetitive drilling and fastener application in tight, ergonomically challenging spaces. The precision required meant workers were often in awkward positions for extended periods, leading to fatigue, potential errors, and high rates of Musculoskeletal Disorders (MSDs).

The Cobot Solution:

The company deployed a fleet of UR-style Cobots equipped with vision systems. The human worker now performs the initial high-judgment setup — identifying the part and initiating the sequence. The Cobot then precisely handles the heavy, repetitive drilling and fastener insertion. The human worker remains directly alongside the Cobot, performing simultaneous quality checks and handling tasks that require tactile feedback or complex dexterity (like cable routing).

The Innovation Impact:

The process yielded a 30% reduction in assembly time and, critically, a near-zero rate of MSDs related to the process. The human role shifted entirely from physical exertion to supervision and quality assurance, turning an exhausting, injury-prone role into a highly skilled, collaborative function. This demonstrates Cobots’ power to improve both efficiency and human well-being, increasing overall job satisfaction.

Case Study 2: Flexible Automation in Small-to-Medium Enterprises (SMEs)

The Challenge:

A small, family-owned metal fabrication business needed to increase production to meet demand for specialized parts. Traditional industrial robotics were too expensive, too large, and required complex, fixed programming — an impossible investment given their frequent product changeovers and limited engineering staff.

The Cobot Solution:

They invested in a single, affordable, lightweight Cobot (e.g., a FANUC CR series) and installed it on a mobile cart. The Cobot was tasked with machine tending — loading and unloading parts from a CNC machine, a task that previously required a dedicated, monotonous human shift. Because the Cobot could be programmed by simple hand-guiding and a user-friendly interface, existing line workers were trained to set up and manage the robot in under a day, focusing on Human-Robot Interaction (HRI) best practices.

The Innovation Impact:

The Cobot enabled lights-out operation for the single CNC machine, freeing up human workers to focus on higher-value tasks like complex welding, custom finishing, and customer consultation. This single unit increased the company’s throughput by 40% without increasing floor space or headcount. More importantly, it democratized automation, proving that Cobots are the essential innovation that makes high-level automation accessible and profitable for small businesses, securing their future competitiveness.

Companies and Startups to Watch in the Cobot Space

The market is defined by both established players leveraging their industrial expertise and nimble startups pushing the envelope on human-AI collaboration. Universal Robots (UR) remains the dominant market leader, largely credited with pioneering the field and setting the standard for user-friendliness and safety. They are focused on expanding their software ecosystem to make deployment even simpler. FANUC and ABB are the industrial giants who have quickly integrated Cobots into their massive automation portfolios, offering hybrid solutions for high-mix, low-volume production. Among the startups, keep an eye on companies specializing in advanced tactile sensing and vision — the critical technologies that will allow Cobots to handle true dexterity. Companies focusing on AI-driven programming (where the Cobot learns tasks from human demonstration) and mobile manipulation (Cobots mounted on Autonomous Mobile Robots, or AMRs) are defining the next generation of truly collaborative, fully mobile smart workspaces.

The shift to Cobots signals a move toward agile manufacturing and a renewed respect for the human worker. The future factory floor will be a hybrid environment where human judgment, creativity, and problem-solving are amplified, not replaced, by safe, intelligent robotic partners. Leaders who fail to see the Cobot as a tool for human-centered upskilling and empowerment will be left behind in the race for true productivity and innovation. The investment must be as much in robot literacy as it is in the robots themselves.

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Are You Getting Your Fair Share of $860 Billion?

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Are You Getting Your Fair Share of $860 Billion?

GUEST POST from Shep Hyken

According to Qualtrics, there is an estimated $860 billion worth of revenue and cost savings available for companies that figure out how to create an improved Customer Experience (CX) using AI to better understand and serve their customers. (That includes $420 billion for B2B and $440 billion for B2C.) Qualtrics recently released these figures in a report/eBook titled Unlock the Potential through AI-Enabled CX.

I had a chance to interview Isabelle Zdatny, head of thought leadership at Qualtrics Experience Management Institute, for Amazing Business Radio. She shared insights from the report, including ways in which AI is reshaping how organizations measure, understand and improve their relationships with customers. These ideas are what will help you get more customers, keep existing customers and improve your processes, giving you a share of the $860 billion that is up for grabs. Here are some of the top takeaways from our interview.

AI-Enabled CX Represents a Financial Opportunity

The way AI is used in customer experience is much more than just a way to deflect customers’ questions and complaints to an AI-fueled chatbot or other self-service solution. Qualtrics’ report findings show that the value comes through increased employee productivity, process improvement and revenue growth. Zdatny notes a gap between leadership’s recognition of AI’s potential and their readiness to lead and make a change. Early adopters will likely capture “compounding advantages,” as every customer interaction makes their systems smarter and their advantage more difficult for competitors to overcome. My response to this is that if you aren’t on board with AI for the many opportunities it creates, you’re not only going to be playing catch-up with your competitors, but also having to catch up with the market share you’re losing.

Customers Want Convenience

While overall CX quality is improving, thanks to innovation, today’s customers have less tolerance for friction and mistakes. A single bad experience can cause customers to defect. My customer experience research says an average customer will give you two chances. Zdatny says, “Customers are less tolerant of friction these days. … Deliver one bad experience, and that sends the relationship down a bad path more quickly than it used to.”

AI Takes Us Beyond Surveys

Customer satisfaction surveys can frustrate customers. AI collects the data from interactions between customers and the company and analyzes it using natural language processing and sentiment. It can predict churn and tension. It analyzes customer behavior, and while it doesn’t look at a specific customer (although it can), it is able to spot trends in problems, opportunities and more. The company that uses this information the right way can reap huge financial rewards by creating a better customer experience.

Agentic AI

Agentic AI takes customer interactions to a new level. As a customer interacts with AI-fueled self-service support, the system can do more than give customers information and analyze the interaction. It can also take appropriate action. This is a huge opportunity to make it easier on the workforce as AI processes action items that employees might otherwise handle manually. Think about the dollars saved (part of the $860 billion) by having AI support part of the process so people don’t have to.

Customer Loyalty is at Risk

To wrap this up, Zdatny and I talked about the concept of customer loyalty and how vulnerable companies are to losing their most loyal customers. According to Zdatny, a key reason is the number of options available to consumers. (While there may be fewer options in the B2B world, the concern should still be the same.) Switching brands is easy, and customers are more finicky than ever. Our CX research finds that typical customers give you a second chance before they switch. A loyal customer will give you a third chance — but to put it in baseball terms, “Three strikes and you’re out!” Manage the experience right the first time, and keep in mind that whatever interaction you’re having at that moment is the reason customers will come back—or not—to buy whatever you sell.

Image Credits: Pexels

This article was originally published on Forbes.com

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How Tangible AI Artifacts Accelerate Learning and Alignment

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Seeing the Invisible

By Douglas Ferguson, Founder & CEO of Voltage Control
Originally inspired by “A Lantern in the Fog” on Voltage Control, where teams learn to elevate their ways of working through facilitation mastery and AI-enabled collaboration.

Innovation isn’t just about generating ideas — it’s about testing assumptions before they quietly derail your progress. The faster a team can get something tangible in front of real eyes and minds, the faster they can learn what works, what doesn’t, and why.

Yet many teams stay stuck in abstraction for too long. They debate concepts before they draft them, reason about hypotheses before they visualize them, and lose energy to endless interpretation loops. That’s where AI, when applied strategically, becomes a powerful ally in human-centered innovation — not as a shortcut, but as a clarifier.

How Tangible AI Artifacts Accelerate Learning and Alignment

At Voltage Control, we’ve been experimenting with a practice we call AI Teaming — bringing AI into the collaborative process as a visible, participatory teammate. Using new features in Miro, like AI Flows and Sidekicks, we’re able to layer prompts in sequence so that teams move from research to prototypes in minutes. We call this approach Instant Prototyping — because the prototype isn’t the end goal. It’s the beginning of the real conversation.

Tangibility Fuels Alignment

In human-centered design, the first artifact is often the first alignment. When a team sees a draft — even one that’s flawed — it changes how they think and talk. Suddenly, discussions move from “what if” to “what now.” That’s the tangible magic: the moment ambiguity becomes visible enough to react to.

AI can now accelerate that moment. With one-click flows in Miro, facilitators can generate structured artifacts — such as user flows, screen requirements, or product briefs — based on real research inputs. The output isn’t meant to be perfect; it’s meant to be provocative. A flawed draft surfaces hidden assumptions faster than another round of theorizing ever could.

Each iteration reveals new learning: the missing user story, the poorly defined need, the contradiction in the strategy. These insights aren’t AI’s achievement — they’re the team’s. The AI simply provides a lantern, lighting up the fog so humans can decide where to go next.

Layering Prompts for Better Hypothesis Testing

One of the most powerful aspects of Miro’s new AI Flows is the ability to layer prompts in connected sequences. Instead of a single one-off query, you create a chain of generative steps that build on each other. For example:

Synthesize research into user insights.
Translate insights into “How Might We” statements.
Generate user flows based on selected opportunities.
Draft prototype screens or feature lists.

Each layer of the flow uses the prior outputs as inputs — so when you adjust one, the rest evolves. Change a research insight or tweak your “How Might We” framing, and within seconds, your entire prototype ecosystem updates. It’s an elegant way to make hypothesis testing iterative, dynamic, and evidence-driven.

Seeing the Invisible

In traditional innovation cycles, these transitions can take weeks of hand-offs. With AI flows, they happen in minutes — creating immediate feedback loops that invite teams to think in public and react in real time.

(You can see this process in action in the video embedded below — where we walk through how small prompt adjustments yield dramatically different outputs.)

The Human Element: Facilitating Sensemaking

The irony of AI-assisted innovation is that the faster machines generate, the more valuable human facilitation becomes. Instant prototypes don’t replace discussion — they accelerate it. They make reflection, critique, and sensemaking more productive because there’s something concrete to reference.

Facilitators play a critical role here. Their job is to:

Name the decision up front: “By the end of this session, we’ll have a directionally correct concept we’re ready to test.”
Guide feedback: Ask, “What’s useful? What’s missing? What will we try next?”
Anchor evidence: Trace changes to specific research insights so teams stay grounded.
Enable iteration: Encourage re-running the flow after prompt updates to test the effect of new assumptions.

Through this rhythm of generation, reflection, and adjustment, AI becomes a conversation catalyst — not a black box. And the process stays deeply human-centered because it focuses on learning through doing.

Case in Point: Building “Breakout Buddy”

We recently used this exact approach to prototype a new tool called Breakout Buddy — a Zoom app designed to make virtual breakout rooms easier for facilitators. The problem was well-known in our community: facilitators love the connection of small-group moments but dread the logistics. No drag-and-drop, no dynamic reassignment, no simple timers.

Using our Instant Prototyping flow, we gathered real facilitator pain points, synthesized insights, and created an initial app concept in under two hours. The first draft had errors — it misunderstood terms like “preformatted” and missed saving room configurations — but that’s precisely what made it valuable. Those gaps surfaced the assumptions we hadn’t yet defined.

After two quick iterations, we had a working prototype detailed enough for a designer to polish. Within days, we had a testable artifact, a story grounded in user evidence, and a clear set of next steps. The magic wasn’t in the speed — it was in how visible our thinking became.

Designing for Evidence, Not Perfection

If innovation is about learning, then prototypes are your hypotheses made tangible. AI just helps you create more of them — faster — so you can test, compare, and evolve. But the real discipline lies in how you use them.

Don’t rush past the drafts. Study what’s wrong and why.
Don’t hide your versions. Keep early artifacts visible to trace the evolution.
Don’t over-polish. Each iteration should teach, not impress.

When teams treat AI outputs as living evidence rather than final answers, they stay in the human-centered loop — grounded in empathy, focused on context, and oriented toward shared understanding.

A Lantern in the Fog

At Voltage Control, we see AI not as a replacement for creative process, but as a lantern in the fog — illuminating just enough of the path for teams to take their next confident step. Whether you’re redesigning a product, reimagining a service, or exploring cultural transformation, the goal isn’t to hand creativity over to AI. It’s to use AI to make your learning visible faster.

Because once the team can see it, they can improve it. And that’s where innovation truly begins.

🎥 Watch the Demo: How layered AI prompts accelerate hypothesis testing in Miro

Join the waitlist to get your hands on the Instant Prototyping template

Image Credit: Douglas Ferguson, Unsplash

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The Nuclear Fusion Accelerator

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How AI is Commercializing Limitless Power

The Nuclear Fusion Accelerator - How AI is Commercializing Limitless Power

GUEST POST from Art Inteligencia

For decades, nuclear fusion — the process that powers the sun and promises clean, virtually limitless energy from basic elements like hydrogen — has been the “holy grail” of power generation. The famous joke has always been that fusion is “30 years away.” However, as a human-centered change and innovation thought leader, I can tell you that we are no longer waiting for a scientific miracle; we are waiting for an engineering and commercial breakthrough. And the key catalyst accelerating us across the finish line isn’t a new coil design or a stronger laser. It is Artificial Intelligence.

The journey to commercial fusion involves taming plasma — a superheated, unstable state of matter hotter than the sun’s core — for sustained periods. This process is characterized by extraordinary complexity, high costs, and a constant, data-intensive search for optimal control parameters. AI is fundamentally changing the innovation equation by replacing the slow, iterative process of trial-and-error experimentation with rapid, predictive optimization. Fusion experiments generate petabytes of diagnostic data; AI serves as the missing cognitive layer, enabling physicists and engineers to solve problems in days that once took months or even years of physical testing. AI isn’t just a tool; it is the accelerator that is finally making fusion a question of when, not if, and critically, at a commercially viable price point.

AI’s Core Impact: From Simulation to Scalability

AI accelerates commercialization by directly addressing fusion’s three biggest engineering hurdles, all of which directly affect capital expenditure and time-to-market:

1. Real-Time Plasma Control & Digital Twins: Fusion plasma is highly turbulent and prone to disruptive instabilities. Reinforcement Learning (RL) models and Digital Twins — virtual, real-time replicas of the reactor — learn optimal control strategies. This allows fusion machines to maintain plasma confinement and temperature far more stably, which is essential for continuous, reliable power production.
2. Accelerating Materials Discovery: The extreme environment within a fusion reactor destroys conventional materials. AI, particularly Machine Learning (ML), is used to screen vast material databases and even design novel, radiation-resistant alloys faster than traditional metallurgy, shrinking the time-to-discovery from years to weeks. This cuts R&D costs and delays significantly.
3. Design and Manufacturing Optimization: Designing the physical components is immensely complex. AI uses surrogate models — fast-running, ML-trained replicas of expensive high-fidelity physics codes — to quickly test thousands of design iterations. Furthermore, AI is being used to optimize manufacturing processes like the winding of complex high-temperature superconducting magnets, ensuring precision and reducing production costs.

“AI is the quantum leap in speed, turning the decades-long process of fusion R&D into a multi-year sprint towards commercial viability.” — Dr. Michl Binderbauer, the CEO of TAE Technologies

Case Study 1: The Predict-First Approach to Plasma Turbulence

The Challenge:

A major barrier to net-positive energy is plasma turbulence, the chaotic, swirling structures inside the reactor that cause heat to leak out, dramatically reducing efficiency. Traditionally, understanding this turbulence required running extremely time-intensive, high-fidelity computer codes for weeks on supercomputers to simulate one set of conditions.

The AI Solution:

Researchers at institutions like MIT and others have successfully utilized machine learning to build surrogate models. These models are trained on the output of the complex, weeks-long simulations. Once trained, the surrogate can predict the performance and turbulence levels of a given plasma configuration in milliseconds. This “predict-first” approach allows engineers to explore thousands of potential operating scenarios and refine the reactor’s control parameters efficiently, a process that would have been physically impossible just a few years ago.

The Commercial Impact:

This application of AI dramatically reduces the design cycle time. By rapidly optimizing plasma behavior through simulation, engineers can confirm promising configurations before they ever build a new physical machine, translating directly into lower capital costs, reduced reliance on expensive physical prototypes, and a faster path to commercial-scale deployment.

Case Study 2: Real-Time Stabilization in Commercial Reactor Prototypes

The Challenge:

Modern magnetic confinement fusion devices require precise, continuous adjustment of complex magnetic fields to hold the volatile plasma in place. Slight shifts can lead to a plasma disruption — a sudden, catastrophic event that can damage reactor walls and halt operations. Traditional feedback loops are often too slow and rely on simple, linear control rules.

The AI Solution:

Private companies and large public projects (like ITER) are deploying Reinforcement Learning controllers. These AI systems are given a reward function (e.g., maintaining maximum plasma temperature and density) and train themselves across millions of virtual experiments to operate the magnetic ‘knobs’ (actuators) in the most optimal, non-intuitive way. The result is an AI controller that can detect an instability milliseconds before a human or conventional system can, and execute complex corrective maneuvers in real-time to mitigate or avoid disruptions entirely.

The Commercial Impact:

This shift from reactive to proactive control is critical for commercial viability. A commercial fusion plant needs to operate continuously and reliably to make its levelized cost of electricity competitive. By using AI to prevent costly equipment damage and extend plasma burn duration, the technology becomes more reliable, safer, and ultimately more financially attractive as a baseload power source.

The New Fusion Landscape: Companies to Watch

The private sector, recognizing the accelerating potential of AI, is now dominating the race, backed by billions in private capital. Companies like Commonwealth Fusion Systems (CFS), a spin-out from MIT, are leveraging AI-optimized high-temperature superconducting magnets to shrink the tokamak design to a commercially viable size. Helion Energy, which famously signed the first power purchase agreement with Microsoft, uses machine learning to control their pulsed Magneto-Inertial Fusion systems with unprecedented precision to achieve high plasma temperatures. TAE Technologies applies advanced computing to its field-reversed configuration approach, optimizing its non-radioactive fuel cycle. Other startups like Zap Energy and Tokamak Energy are also deeply integrating AI into their core control and design strategies. The partnership between these agile startups and large compute providers (like AWS and Google) highlights that fusion is now an information problem as much as a physics one.

The Human-Centered Future of Energy

AI is not just optimizing the physics; it is optimizing the human innovation cycle. By automating the data-heavy, iterative work, AI frees up the world’s best physicists and engineers to focus on the truly novel, high-risk breakthroughs that only human intuition can provide. When fusion is commercialized — a time frame that has shrunk from decades to perhaps the next five to ten years — it will not just be a clean energy source; it will be a human-centered energy source. It promises energy independence, grid resiliency, and the ability to meet the soaring demands of a globally connected, AI-driven digital economy without contributing to climate change. The fusion story is rapidly becoming the ultimate story of human innovation, powered by intelligence, both artificial and natural.

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