Expert Advice for Marketing Leaders on MarTech, AI, & CX
Expert mode marketing technology, AI, and customer experience articles for marketers and other professionals from The Agile Brand Guide®.
This article was written by Greg Kihlström for CMSWire. Experiences are not just a feature of the business. They are the business,” said Qualtrics CEO
Jason Maynard as he kicked off Qualtrics X4, which took place March 17 to 19 at the Seattle Convention Center.
This article was based on the interview with Emma Robinson , Head of B2B Marketing at Canva by Greg Kihlström, AI and MarTech keynote speaker for The Agile Brand with Greg Kihlström podcast. Listen to the original episode here:
In the world of enterprise B2B marketing, we have become masters of the measurable. We live in a landscape defined by lead scoring, MQLs, SQLs, and the intricate dance of account-based marketing. Our dashboards are pristine, our funnels are optimized, and our data is, for the most part, clean. Yet, in this relentless pursuit of quantifiable results, it’s worth asking a critical question: have we forgotten that on the other side of every click, form-fill, and contract is a human being? A person whose brain is fundamentally wired to respond more powerfully to a compelling image than to another row in a spreadsheet.
This isn’t a call to abandon the science of demand generation, but rather a challenge to infuse it with the art of communication. The most forward-thinking marketing leaders understand that creativity is not the antithesis of performance; it is a critical accelerator. The challenge, of course, has always been one of scale and governance. How do you empower a global, distributed team to be both creative and on-brand? How do you close the agonizingly long feedback loop between a creative asset’s launch and the performance data that tells you if it actually worked? The answer lies in a strategic shift—from seeing design as a final, decorative step to embracing it as a core, integrated function, powered by platforms that enable speed, consistency, and intelligent iteration.
For any marketing leader still facing internal skepticism about prioritizing design, the most compelling argument isn’t found in a portfolio, but in neuroscience. The intuitive feeling we have that a strong visual “just works” is now backed by hard data on how our brains process information. It’s not a matter of taste; it’s a matter of cognitive efficiency. In a world of infinite content and finite attention, winning the first few milliseconds of engagement is everything.
Canva’s recent research delved into this, using brain activity tracking to quantify the impact of visual content. The findings are a stark reminder for those of us who live and breathe text-based communication like emails and whitepapers. As Emma Robinson explains, the proof is in how we are wired.
“We process visuals far, far faster than we do text, and we remember them longer… we tracked brain activity using a technique called steady-state topography, which shows that, you know, visual content triggers memory encoding 74% faster than dull alternatives… The research shows that it’s clear that visual communication is not just optional anymore. You know, design-led companies are achieving major boosts in clarity. So, 66% of these companies achieve more efficient communications compared to just 52% that rely on text alone.”
This isn’t just an academic exercise. A 74% faster memory encoding translates directly to brand recall in a crowded market. More efficient communication means your message cuts through the noise and lands with impact, reducing friction in the buyer’s journey. The data highlights a significant opportunity gap: while the benefits are clear, Robinson notes that “only 22% of companies actually consider themselves design-led.” For enterprise leaders, this gap represents a competitive advantage waiting to be seized. It’s an opportunity to build a brand that is not just seen, but remembered.
The perennial tension in B2B marketing is the perceived conflict between the “art” of brand building and the “science” of demand generation. One is often seen as a long-term, unmeasurable investment, while the other is a short-term, data-driven necessity. This is a false dichotomy. The two are not in opposition; they are symbiotic. Strong creative makes performance channels work harder, and performance data should make creative smarter.
The mistake many organizations make is to subordinate creativity entirely to short-term metrics, forcing every brand effort to “justify clicks.” This approach starves the top of the funnel and weakens the very brand equity that makes demand generation effective in the first place. Robinson offers a more integrated and productive analogy for how these two functions should coexist.
“Creativity is really the engine, and then data’s almost like the steering wheel. And so, one without the other goes either nowhere or crashes… you should treat brand like research and development. You know, they become those brand efforts become really the oxygen that sustains demand over time and also accelerates channel performance when you get it right… creativity isn’t really just sort of the enemy, I guess, of precision, but it’s what makes the message actually land ultimately with your buyers and and sort of doesn’t end up disappearing in that kind of inbox noise.”
Thinking of brand as R&D is a powerful mental model. It reframes brand investment not as a cost center, but as a long-term investment in market relevance and trust. When your brand resonates, your cost-per-click goes down. When your message is clear and compelling, your conversion rates go up. The data from your performance campaigns—the steering wheel—provides the crucial feedback to guide the creative engine, ensuring you’re not just moving, but moving in the right direction. This integrated approach ensures that your precision targeting efforts in channels like ABM are delivering a message that actually connects on a human level, rather than just another piece of inbox noise.
The theoretical value of visual communication is easy to grasp. The practical challenge, especially in an enterprise with thousands of employees, is execution. How do you maintain brand consistency when content creation is democratized? The traditional model of a centralized creative team acting as a bottleneck is no longer viable in an agile world. Yet, the alternative—a free-for-all where everyone is a designer—risks brand dilution and chaos.
The solution lies in a platform-based approach that shifts the paradigm from control to enablement. It’s about providing teams with the tools and frameworks they need to create freely and quickly, but within a system that ensures brand cohesion. This means moving brand guidelines out of a forgotten PDF and embedding them directly into the creative workflow.
“The way we think about at Canva is we help companies to really set those templates up as the guardrails, not handcuffs. And they help people to move faster without going off the road… The shift from centralized control to centralized enablement is where creativity is democratized and then the brand still stays incredibly cohesive.”
This is more than just a workflow improvement; it addresses a fundamental source of friction and risk in modern marketing organizations. Robinson points out that teams are juggling, on average, 8.7 different tools each week. This fragmentation not only slows execution but also makes maintaining brand consistency nearly impossible. By consolidating on a single platform where brand assets, fonts, colors, and templates are built-in, you remove that friction. The marketing team in Japan and the sales team in Germany can both create localized, relevant content that still feels like it comes from one unified brand. This is the key to achieving both speed and scale without sacrificing the integrity of the brand you’ve worked so hard to build.
As we look to the future, the integration of artificial intelligence into the creative process is inevitable. However, not all AI is created equal. The initial wave of generative AI has been impressive, but for an enterprise brand, a generic tool that has no context of your visual identity, tone of voice, or performance history is more of a liability than an asset. The real transformation will come from AI that is deeply integrated with your brand and your data.
This means closing the final loop between creation, distribution, and analytics, and using AI to learn from that loop at scale. Instead of a human manually analyzing a spreadsheet to decide which ad creative to iterate on, a brand-aware AI can surface those insights and even suggest improvements in real-time.
“You can consider it the generic AI is a bit like having an intern with no context. Whereas with brand aware AI is more like a coach who knows your playbook and then constantly learns from it… It’s not just asking, you know, what can I create? It is actually asking what should I improve based on what’s already working, which is a very different nuance.”
This shift moves AI from a simple content generator to a strategic partner in optimization. It collapses the feedback loop, allowing marketers to move from looking at performance in the “rearview mirror” to looking through the “windshield,” as Robinson puts it. This frees up marketers to focus on what they do best: understanding the customer, crafting compelling narratives, and making strategic decisions. The AI handles the tireless work of at-scale optimization, ensuring that every campaign is smarter than the last. This is the future of the agile marketing team—one that is not competing with AI, but directing it.
The path forward for B2B marketing is not a choice between data and design, but a synthesis of the two. The data from neuroscience is unequivocal: our buyers are visual creatures, and brands that communicate visually will be seen, processed, and remembered more effectively. This is no longer a “nice-to-have” but a fundamental requirement for cutting through the noise of an increasingly crowded digital marketplace. Leaders who successfully operationalize this understanding will build more resilient brands and more effective marketing engines.
Ultimately, the technology and platforms now exist to make this vision a reality at the enterprise level. By moving from a mindset of centralized control to one of centralized enablement, and by embracing brand-aware AI as a coach rather than a mere tool, we can empower our teams to work faster and smarter. The future of B2B marketing belongs to those who can master this blend of art and science, telling powerful, human stories that are guided by data and scaled by technology. It’s a future that is not only more effective but, dare I say, a lot more interesting.
This article was written by Greg Kihlström for MarTech. As AI promises to automate 90% of your administrative tasks, are you ready to stake your brand’s future on the remaining 10% — the high-value human judgment machines can’t replicate?
Cloud-led innovation in the era of AI, a global report by NTT DATA, based on a survey of over 2,300 senior decision-makers across various industries, highlights a significant gap between cloud ambition and reality. The research indicates that organizations failing to evolve their cloud foundations risk constraining the growth and value of their AI investments.
Framer’s State of Sites ‘26 report, a survey of over 1,900 professionals reveals a significant disconnect between strategic imperatives and operational realities. While the speed of launching new digital experiences has increased, the ability to continuously optimize and adapt websites for performance often lags, leading to missed opportunities and stalled growth.
Intuit Mailchimp’s recent report, The Ecommerce Playbook: A Practical Field Guide for Modern DTC Brands consolidates insights from 22 industry experts, offering a practical guide for senior marketing and CX leaders to build, scale, and sustain profitable e-commerce operations. This blueprint emphasizes a holistic approach, integrating brand strategy, financial discipline, optimized customer experiences, and diversified growth channels to foster resilient and measurable business outcomes.
This is the story of how CIBC, one of Canada’s largest financial institutions, undertook such a transformation. Over the course of a decade, they moved from a fragmented organization that was lagging its peers in client experience to a modern, relationship-oriented bank driving billions in revenue growth. They did it by moving customer experience (CX) from a siloed function to the core of their enterprise operating model, led from the very top.
Artificial intelligence (AI) has progressed from experimental pilots to an integral component of enterprise customer experience (CX) strategies. While many organizations now leverage AI agents for customer service, a significant gap persists between AI’s technological capabilities and its current operational deployment. This disparity often leads to consumer dissatisfaction and a missed opportunity for businesses to fully realize AI’s strategic value. Agentic CX in 2026: What consumers expect and most enterprises miss a recent global research study conducted by Ada and NewtonX, surveying 2,000 consumers and 500 CX decision-makers, reveals critical insights into consumer expectations, enterprise measurement shortcomings, and the organizational complexities hindering AI’s potential .
The research indicates that consumers are not inherently skeptical of AI; rather, they are critical of AI that fails to resolve their issues. Currently, only 24% of consumers report achieving full resolution from an AI agent without human intervention. Concurrently, 55% of businesses lack clear visibility into their AI agents’ performance, making sustained improvement structurally challenging. The path to advanced agentic CX requires a fundamental shift in how enterprises approach AI implementation, measurement, and governance.
Consumers demand efficient and effective AI, prioritizing concrete problem resolution over perceived “human-like” empathy. The data indicates a clear mandate for AI that works seamlessly.
Despite widespread AI adoption, many consumers encounter unresolved issues when interacting with AI agents. Only 24% of consumers report their issues being fully resolved by AI . The remaining interactions frequently result in negative outcomes: 33% are escalated to a human, 22% achieve only partial resolution, and 11% lead to complete abandonment . These failures are rooted in AI’s operational limitations, not a preference for human interaction. Key failure points include:
These issues often lead to greater frustration than if the consumer had reached a human agent immediately. As one Service Operations & Technology Department Head in Travel & Hospitality noted, “AI gets them 70-80% there, then fails. That makes the experience more frustrating than if they’d reached a human immediately” .
When asked to allocate points across various customer service features, consumers overwhelmingly prioritized core capabilities over empathetic communication. Top priorities included accuracy (18 points), problem-solving ability (18 points), and the option for human escalation (16 points), followed by data privacy/security and speed of resolution. Empathy ranked last (6 points) . This underscores that consumers seek effective solutions, not AI that mimics human sentiment. For instance, in a billing error scenario, consumers chose a straightforward, solution-focused AI that resolved their issue in 10 seconds over a conversational, empathetic AI that took two minutes . The core insight is straightforward: “Consumers aren’t anti-AI. They’re anti-bad customer service” .
Consumer trust in AI is directly correlated with the complexity and potential consequences of the interaction. While AI is preferred for simple tasks like basic FAQs, preference shifts towards human agents for moderate tasks (e.g., account updates, status checks) and becomes pronounced for complex or urgent issues (e.g., financial advice, lost credit cards) . This highlights that complexity itself is not a barrier; rather, it’s a visibility problem concerning AI’s reliability in high-stakes scenarios.
What to do:
What to avoid:
Summary: Consumer trust in agentic CX is built on the AI’s demonstrated capability to resolve issues accurately and efficiently, supported by transparent disclosure and accessible human escalation options.
While enterprises are investing heavily in AI, many are measuring outcomes based on traditional efficiency metrics, rather than the resolution-focused metrics consumers value. This misalignment impedes the ability to prove AI’s strategic impact.
Most CX leaders prioritize operational efficiency when evaluating AI deployments. The top benefits cited by businesses include reduced wait/handle times (51%), lower cost per interaction (47%), 24/7 availability (43%), and ticket deflection (41%) . In contrast, critical customer-centric metrics such as resolution rate (22%) and CSAT improvement (20%) rank significantly lower . This indicates a fundamental gap between what businesses measure and what customers value.
Containment and deflection metrics, while useful for managing contact center volume, fundamentally measure avoidance of human interaction, not the successful fulfillment of customer needs. True “automated resolution” must signify end-to-end completion of an issue, accurately and in alignment with policy, without human intervention . Without this precise definition, “success” becomes ambiguous, corrupting business intelligence and hindering meaningful improvement.
Over half of businesses (55%) lack the robust infrastructure required to accurately monitor AI agent performance. Many measure AI and human agent interactions together (blended metrics), or only by channel, making it impossible to isolate AI’s specific impact . Only 28% of businesses achieve the “gold standard” of tracking AI-only, hybrid, and human-only interactions separately . This lack of granular visibility prevents organizations from:
The commercial case for AI scaling extends beyond initial efficiency gains. It requires demonstrating measurable impact on downstream business outcomes such as customer lifetime value (CLTV), retention, and incremental revenue. Currently, 42% of businesses cannot link AI interactions to downstream outcomes, and 32% cannot isolate AI’s impact from other CX improvements . The mandate has evolved from simply deploying AI quickly to providing tangible proof of its value.
Advancing AI in CX necessitates a staged approach to measurement maturity:
What to do:
Summary: To move beyond efficiency metrics and establish AI as a strategic growth driver, enterprises must build a robust, multi-layered measurement infrastructure that provides clear visibility into AI performance and its direct impact on commercial outcomes.
Scaling AI beyond basic use cases requires more than just technology; it demands a robust governance framework, seamless system integration, and a skilled workforce capable of managing and optimizing complex AI deployments.
While 92% of businesses intend to increase their AI investment in CX over the next year, only 9% are primarily AI-led today, with 21% expecting to be within 12 months (a 2.4x increase) . This ambition contrasts with the reality that most current AI deployments handle low-complexity tasks such as basic FAQs or simple how-to guidance (66-67% of businesses) .
To progress, AI agent scope must expand through maturity stages:
This progression highlights the need for AI to perform actions and automate back-end systems, moving beyond simple information retrieval.
Several critical barriers prevent businesses from advancing their AI maturity:
What advanced businesses do :
Effective AI deployment hinges on robust governance and a skilled workforce. The study reveals that 80% of businesses lack a fully adopted, aligned governance framework for AI in CX . Governance, defined as the rules, controls, and data practices dictating AI behavior and its boundaries, is crucial for safely deploying AI in high-stakes environments. Among businesses with formal frameworks, 44% co-developed them with their AI vendors, highlighting the value of expert collaboration in anticipating potential gaps.
What to do:
What to avoid:
Immediate priorities (first 90 days):
Summary: Achieving advanced agentic CX requires a strategic organizational commitment to robust governance, proactive system integration planning, and the cultivation of specialized AI talent, rather than solely focusing on technological deployment.
The future of agentic CX is not defined by the sheer volume of AI investments, but by the strategic quality of those investments. Consumers consistently seek AI that delivers accurate, efficient resolutions, coupled with transparency and accessible human support. Yet, many enterprises remain hampered by misaligned measurement strategies, underdeveloped governance, and critical skills gaps, preventing them from realizing AI’s full potential.
To transform customer experience from a cost center into a growth driver, CX leaders must pivot from merely deploying AI to building a robust foundation that ensures AI effectiveness and measurable business impact. This entails implementing sophisticated measurement infrastructures to track AI performance against commercial outcomes, establishing comprehensive governance frameworks to manage risk and ensure ethical deployment, and cultivating the specialized talent required to operate and optimize advanced AI agents. Organizations that prioritize these foundational elements today will build lasting competitive advantages in an increasingly AI-driven market.
Source: Ada & NewtonX. (2026). Agentic CX in 2026: What consumers expect and most enterprises miss. Ada.
The Incisiv 2026 Benchmark Index: Unified Commerce for Specialty Retail, conducted in partnership with Manhattan and Google Cloud, provides a comprehensive assessment of unified commerce capabilities across over 400 specialty retailers globally. This benchmark reveals that true competitive advantage now stems from delivering seamless, connected customer experiences, extending from initial discovery through final fulfillment.
For years, the Net Promoter Score (NPS) has been the North Star for customer experience programs. We’ve built dashboards, tied bonuses to it, and presented its steady upward climb in boardrooms as definitive proof of our success. And for good reason—it’s a simple, powerful metric. But as any seasoned leader knows, the comfort of a high, stable score can be deceptive. It can become a vanity metric, a signal so consistent that it lulls us into a state of operational complacency, masking the subtle friction points and emerging issues that lie just beneath the surface.
