Explore why organizations must prioritize operational discipline and recovery strategies alongside AI innovation, ensuring customer experiences remain resilient when technology inevitably changes.
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Success for me now looks like not just rising from the fire, but loving the woman who had to walk through it and not asking for permission to become her.
Kendra Parker · In Her Own Words
Her Story
About Kendra
Kendra Parker is a Client Partner, speaker, and board advisor with more than 22 years of experience helping organizations harness technology to solve complex business challenges. Currently serving as Client Partner at Waterfield Tech, she is recognized for her strategic leadership, collaborative approach, and ability to build lasting client partnerships. Throughout her career, Kendra has consistently ranked among the top performers in her organizations, including spending two decades with C1 (formerly ConvergeOne), where she successfully navigated company acquisitions while maintaining an unwavering focus on customer success. A graduate of Iowa State University with a Bachelor of Science in Hotel, Restaurant, and Institution Management, Kendra's journey into information technology began with an opportunity in campus telecommunications, launching a career defined by resilience, innovation, and continuous growth. Relationship-building has been the cornerstone of Kendra's success. She believes the strongest partnerships are built through trust, curiosity, and authentic communication, encouraging others to "listen with the intent to understand, not simply to respond." As a respected sales leader, she helps organizations navigate digital transformation while fostering collaboration across teams and empowering those around her to reach their full potential. Kendra is also a passionate advocate for women pursuing careers in technology and leadership, encouraging them to embrace discomfort as a catalyst for growth, confidence, and opportunity. Beyond her work with clients, she regularly shares insights on artificial intelligence, customer experience, and leadership, helping organizations thoughtfully balance innovation with operational excellence. Outside of her professional life, Kendra is fueled by adventure, lifelong learning, and meaningful connections. She is an avid motorcyclist, an enthusiastic traveler, and a self-proclaimed foodie who enjoys discovering new cuisines, cooking, and pairing great meals with fine wines. As a devoted mother, she proudly supports her daughter as a competitive dancer and embraces every moment of being a dedicated dance mom. Following a season of personal reinvention, Kendra has continued to pursue new experiences, joining an adventure club that curates immersive excursions designed to challenge perspectives and foster growth. Whether mentoring future leaders, exploring the world, or creating memorable moments with family and friends, she approaches every chapter of life with curiosity, courage, and an unwavering commitment to personal and professional excellence.
Her Interview
Ten minutes with Kendra
01What do you attribute your success to?
I attribute my success to building genuine, lasting relationships and maintaining a strong work ethic throughout my career. Over the past 20 years, I have consistently ranked among the top performers, placing in the top 5% of my organization. I believe that long-term success in sales comes from earning trust, creating meaningful connections, and always putting people first.
02What’s the best career advice you’ve ever received?
The best career advice I have ever received came from my father, who taught me the value of hard work and the importance of always keeping my word. His example inspired me to pursue every goal with integrity, accountability, and unwavering determination.
03What advice would you give to young women entering your industry?
I would offer two pieces of advice to young women entering a male-dominated industry. First, listen with the genuine intent to understand rather than simply to respond. Second, embrace discomfort as an opportunity for growth. Some of the greatest personal and professional development happens when you are willing to step outside your comfort zone.
04What are the biggest challenges or opportunities in your field right now?
Working in a male-dominated industry has presented challenges, but it has also created opportunities to lead, mentor, and help others succeed. Today, I have the privilege of sharing the sales strategies and relationship-building principles I have developed throughout my career with hundreds of leaders. Helping others strengthen their skills, build authentic relationships, and achieve lasting success is one of the most rewarding aspects of my work.
05What values are most important to you in your work and personal life?
The values that guide both my professional and personal life are integrity, meaningful relationships, community, and continuous growth. Becoming a mother inspired me to redefine success and place greater value on fostering genuine connections both inside and outside of the workplace. I believe that investing in people and building strong communities creates the greatest and most lasting impact.
Her Content Hub
Articles by Kendra
The Morning After AI Goes Dark: When Innovation Outruns Operational Discipline
Building resilient AI strategies requires operational discipline and recovery planning as much as innovation and speed.
In my last article, I wrote about what I called a culture of immediate—the pressure organizations face to move faster, adopt faster, and prove they are not standing still while AI reshapes the landscape around them. The question I asked then was simple: Are we getting AI right, or are we just getting it fast?
Damian Mathews and The Last Mile Team published a piece that brought me back to that question in a way I hadn’t expected. Their article wasn’t about a specific model. It was about what happens when a capability organizations depend on suddenly disappears.
And it highlighted something I don’t think we’re talking about enough.
Most organizations are building AI strategies before they’ve built recovery strategies. Security has become part of every AI conversation. Recovery too rarely is.
The Morning After
The events surrounding Claude Fable 5 and Mythos 5 created a scenario few organizations had seriously contemplated. One day, a frontier model was available. The next day, it wasn’t. Regardless of where anyone lands on the details, the event exposed something important: many organizations are not prepared for the morning after.
The morning after a model changes. The morning after a capability disappears. The morning after a feature your team built into a workflow behaves differently than it did the day before. Most importantly, they are not prepared for the moment someone asks a question that should have been answered long before deployment:
What do we roll back to?
Lessons We’ve Already Learned
For years in customer experience and contact center technology, we have operated in environments where change was expected. Platforms evolved. Releases happened. Features matured. Vendors adjusted roadmaps. Occasionally, a feature disappeared or a migration became more complicated than anyone anticipated (here’s looking at you, Intuity TUI). Yet through all of that, enterprise technology developed a discipline around managing change.
The technology industry spent decades learning how to operate in environments where change is inevitable. We built change-control processes. We built disaster recovery plans. We built redundancy. We built rollback procedures. We created governance frameworks and escalation paths—not because we expected failure, but because we respected the possibility of it.
The best technology organizations never assumed a platform would remain unchanged forever. They assumed it wouldn’t. What surprises me about many AI conversations today is how quickly we’ve forgotten those lessons.
The More Important Questions
Somewhere along the way, many organizations stopped asking operational questions and started asking only innovation questions. What model should we use? How quickly can we deploy it? What productivity gains can we achieve? How much cost can we remove? Those are important questions, but they are incomplete.
The more important questions are often the less exciting ones:
- What happens if the model changes?
- What happens if the vendor changes direction?
- What happens if regulations change?
- What happens if a capability becomes restricted?
- What happens if the experience you’ve designed today no longer exists tomorrow?
Those questions aren’t anti-innovation. They’re operational discipline. And operational discipline has never been more important.
What Feels Different Today
AI is not new to customer experience. Those of us who have spent our careers in this industry know that forms of artificial intelligence have existed in the contact center for decades. Speech technologies, predictive routing, workforce optimization, intelligent self-service, and automation have all played a role in shaping customer experiences long before generative AI became the topic of every board meeting.
Regulatory oversight isn’t new either. Organizations have spent years navigating requirements around privacy, accessibility, financial services, healthcare, security, and consumer protection. We understand compliance. We understand governance. We understand adapting technology to operate within evolving rules.
What feels different today is immediacy. Are we getting AI right, or are we just getting it fast?
Historically, organizations had time to respond to change. Today, regulatory decisions, vendor decisions, and model decisions can affect operations with unprecedented speed—not next quarter, not next year, but potentially tomorrow morning.
And if your customer experience is dependent on a single model or a single architectural approach, your customers won’t care why the capability changed. They will only experience the result.
Data Sovereignty and the Best Friend Experience
This brings me back to something I’ve spent years defining with my customers: the Best Friend Experience. Organizations are increasingly looking to AI to help deliver that experience, and rightly so. The technology is remarkably powerful when implemented thoughtfully.
But the Best Friend Experience requires something we don’t discuss nearly enough: data sovereignty. The model should not own the relationship. The platform should not own the relationship. The vendor should not own the relationship.
The organization should.
Because if customer understanding exists only inside a single model, then the relationship itself becomes dependent on that model’s continued availability. If the intelligence behind the experience changes, the customer relationship must survive the change. The context, history, and understanding that make the experience valuable should remain portable regardless of which technology is powering it at any given moment.
Automation Before AI
This connects to another conclusion I’ve reached after countless conversations with customers and industry peers.
If your organization has not yet mastered automation, it may not be time to jump directly into AI.
One of the most common patterns I see is organizations wanting to leapfrog foundational operational maturity in pursuit of AI transformation. Yet the most successful AI deployments I’ve observed are rarely the organizations that started with AI. They’re the organizations that first understood their operation well enough to automate it.
They knew their baselines. They understood where work flowed and where it stalled. They knew where customers became frustrated and where employees spent their time.
- They had reporting.
- They had governance.
- They had change control.
Most importantly, they understood what great looked like before asking AI to make it better. Because AI wasn’t their starting point. It was an accelerator.
That’s why I believe automation, measurement, governance, and change control (including your rollback plan) deserve more attention than they’re receiving in the current conversation. AI is not a substitute for operational maturity. It amplifies whatever maturity already exists.
Organizations with strong operational foundations can create extraordinary outcomes with AI. Organizations without those foundations often create complexity faster than they create value.
Who Owns the Intelligence?
Which brings me to what I increasingly believe is one of the most important architectural questions facing customer experience leaders:
Who owns the intelligence?
If the customer relationship, customer context, and operational knowledge exist primarily inside a single model, then the organization has outsourced more than technology. It has outsourced flexibility.
The Best Friend Experience depends on continuity. The customer should remain known even when the underlying technology changes. That requires data sovereignty. It requires portability. And increasingly, it requires modularity.
Bring Your Own AI
This is why I believe so strongly in a Bring Your Own AI approach.
- Not because every organization needs every model.
- Not because complexity is inherently good.
- But because resilience matters.
Employees already operate this way. Inside most organizations, people use different tools for different outcomes. They use Copilot, Claude, ChatGPT, Granola—they select the right tool for the right moment.
We’ve already accepted flexibility in the employee experience. Our customer-facing experiences should be designed with the same mindset.
The capability should belong to the business. The data should remain governed. The experience should remain portable. And the architecture should allow organizations to adapt when change inevitably arrives.
Innovation and Operational Discipline
Innovation and operational discipline should not be opposing forces.
The most successful technology organizations undertaking AI—those that will be written about in the future—will have understood both. They will move forward while maintaining the ability to recover. They will adopt new capabilities without becoming dependent on them. They will build flexibility into their architecture long before they need it.
AI doesn’t change those principles.
If anything, it makes them more important.
Because the question isn’t whether the technology will change.
It will.
The question is whether the customer experience you’ve built can survive the change when it does—including the morning after things have gone dark.
#ArtificialIntelligence #CustomerExperience #Leadership #AITransformation #ContactCenter
A thoughtful exploration of how organizations are implementing AI at breakneck speed, examining whether rapid deployment prioritizes quarterly gains over sustainable transformation and genuine customer trust.
A Culture of Immediate: Are We Getting AI Right, or Just Getting It Fast?
The tension between moving fast on AI and moving wisely: why speed alone won't deliver the transformation organizations actually need.
I spend my days in rooms where decisions are being made. Big decisions. Not the kind that simply move the numbers on a quarterly report, but the kind that shape the very experience that creates culture—and either grows it or changes the landscape of an organization and how it shows up in the world.
And when I say experience, I’m referring to a few points of view. There is the outward-in: the customer experience, the moments of trust, the brand promise customers feel, the entry point into an organization where human pressure testing takes place. And there is the inward-out: the internal experience within an organization—the people on the inside whose belief, energy, and ability to adjust determine whether the time, effort, and energy that went into making these big decisions actually lands.
Both of those experiences are being reshaped across industry verticals, and the question being asked isn’t whether or not to execute on AI—it’s about how fast it can be executed. And here is where I think a foundational challenge in AI exists, which I’ll come back to in a bit.
In the past few weeks, Coinbase announced a 14% reduction and described what comes next as “rebuilding Coinbase as an intelligence, with humans around the edge aligning it.” Now, when a company describes its future as “AI at the center, humans at the edge,” they mean it as a compliment to the technology. I understand that, truly. But customers don’t read the org charts. They feel the difference between a brand that backs self-service with humans and one that routes them to a model. Add Meta’s announcement of an 8,000-person reduction. The race toward IPOs by the major AI labs. The headlines are starting to look the same.
I have been thinking about a thread that runs through every report I have read on enterprise AI this month, and that brings me back to the foundational challenge I’m seeing in AI. Not the technology—the technology and what we are now doing with agentic AI is amazing. The pace has become the real challenge.
We are operating in a culture of immediate. We are the culture of immediate: immediate access to people, immediate access to information, immediate access to, well, everything. And this isn’t just our personal identity; it’s also our professional identity. Cost savings that show up in net earnings. Activist investors. AI hype cycles measured in weeks. Boards asking CEOs what they are doing about AI by next month. The pressure to act fast is not subtle.
And the most legible thing organizational leaders can do quickly is cut—cut headcount, cut layers, cut the cost of the people who do not directly touch the AI (for now). Implement the technology to account for the reduction in force and then publish the memo. Move the stock. Show the result on the bottom line. Let’s be clear: cutting headcount is not new in corporate America.
It is worth pausing and asking honestly: are we getting this right?
What’s Clearly Right
Some of what is happening is the right call. AI is real. Displacement is real. The companies that wait will fall behind. In life, we make bets. Be wrong about the AI bubble and save some money; be wrong about organizational displacement and lose permanently. The case for moving is strong.
And the prize is real on the other side of getting this AI thing right. I’ve long spoken with customers about creating the best friend experience. What I mean by this is: I can go weeks without talking to my best friend, but when we reconnect, I’m not reminding my person where I live, what my kids’ names are, or how many dogs I have (the answer: a lot). We just pick up where we left off. That is the experience customers want from the companies they do business with. They want connection. They want to be seen and heard, while also being easy to access, secure, and consistent across whatever device they happen to be on. AI is incredibly powerful at enabling experiences when it is designed and implemented correctly.
But moving is not the same as moving wisely.
What Gets Cracked First When Speed Is the Metric
The data on this is uncomfortable. KPMG’s Q1 2026 AI Pulse found that 67% of leaders cite employee job security concerns as a top barrier to AI adoption, 62% point to skills gaps as their top barrier to demonstrating AI ROI, and 87% say upskilling the existing workforce is their number one AI priority. You cannot widen the skills gap and close it at the same time. And reducing headcount is not just reducing people—it’s reducing people who hold institutional knowledge: the tribal understanding of what works in your specific operation, and the mid-career employees who connect departments and translate between functions. Those are the line items that are easiest to defend in a spreadsheet, but also the hardest to rebuild once they are gone.
Perhaps more concerning are the things you cannot see when moving too fast. I like to refer to this as the compounding cost of judgment debt: the moments when customer relationships built over time suffer, customer satisfaction drops, and employee morale is impacted—the signals leaders are sending into rooms they cannot see. I don’t believe AI is universally erasing tiers of labor. I am cautiously optimistic—and cautious is the operative word—because most of the data we are analyzing is still pre-agentic. Agentic AI only began seeing practical deployments last year.
What Customers Feel
Customer experience has been moving toward an AI-plus-human model for decades, and that path makes sense. I believe in it. I’ve built my career around helping customers do exactly this. Thinking back to the early contact center platforms I worked on, we were deploying a form of AI back in the ’90s. But until recently, those deployments all centered on the human agent. Deploying “humans around the edge” is more than an operating model—it is changing how business evolves, and doing so at a speed that makes the phrase “pace of change” feel outdated.
It is a model that changes what becomes acceptable as the norm. Cell phones changed what we considered acceptable customer service. AI is doing the same thing, faster. Point being: AI can solve problems quickly. Humans build the trust that determines whether your customer returns when the problem is harder than the model can handle.
A Different Way to Read the Same Evidence
This is not an argument against AI. The customers I work with who are leaning the hardest into AI are doing the most disciplined work I have seen on the human side. They are mapping layers where the human experience is irreplaceable—judgment, empathy, accountability, the moments that define the brand. They are redesigning the work, not just shrinking the org. They are treating AI as an operating model change, not a cost-out event. And they are smart to do so. The subsidized era of AI we were all introduced to is changing.
The question worth wrestling with is not whether to act. It is what we are optimizing for when we do—and how to get there.
If the answer is the quarterly number, we will keep cutting while telling ourselves we are transforming. If the answer is a better operation in three years—building a company our customers trust more, a business our competition cannot easily replicate—then deploying AI looks a little different: slower in some places, more deliberate, less press release, more design.
A culture of immediate rewards the visible move. Real transformation rewards the harder one.
The companies I’d bet on aren’t the ones who moved first (although, thank you for taking the shrapnel). They’re the ones who decided what they were optimizing for before they started downsizing whole organizations.
Are we getting it right? Or are we just getting it fast?
#ArtificialIntelligence #CustomerExperience #Leadership
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