Expert Advice for Marketing Leaders on MarTech, AI, & CX
Insights, ideas, and best practices for marketers that utilize artificial intelligence in their marketing.
There’s a narrative in tech that construction, manufacturing, and energy companies are resistant to change. I’ve spent 26 years in this space, and I think that narrative is backwards.
The real problem? Technology companies are slow to understand physical industries. That realization shaped everything about how we built Briq. Here’s what we learned.
As we enter 2026, the omnipresence of artificial intelligence will continue to transform it from a cutting-edge advantage into a mere operational baseline. As every competitor integrates the same autonomous agents, generative tools, and optimization engines, the race for the “fastest algorithm” will become irrelevant. Industry leaders agree: the brands poised to win in this saturated environment will bypass the technology arms race entirely.
The integration of artificial intelligence into business operations is no longer a futuristic vision but a present reality. However, the true potential of AI lies not just in its adoption, but in its democratization within an organization. Empowering every employee, regardless of their technical expertise, to leverage AI can unlock unprecedented levels of innovation and efficiency.
This article was written by Greg Kihlström for CMSWire. As customer
expectations accelerate, CX leaders are turning to synthetic research and
agentic AI to test experiences before customers ever feel the friction.
For years, enterprise marketing leaders have been engaged in a monumental construction project. With blueprints drawn up by the major marketing clouds and materials sourced from an ever-expanding MarTech landscape, you’ve meticulously built intricate technology stacks. The goal has always been the same: a unified, intelligent system capable of delivering personalized customer experiences at scale. Millions of dollars and countless hours have been invested in this pursuit. We’ve all seen the diagrams, marvelled at their complexity, and worked tirelessly to integrate the disparate pieces.
The core prediction is this: the “new brand currency” will be nothing less than unvarnished authenticity. This shift mandates a re-evaluation of human capital, where employees must evolve from task executors into sophisticated managers of digital agents, and where emotional intelligence is set to transition from a soft skill to a hard, quantifiable metric for enterprise success and competitive advantage.
We recently had a conversation with Arto Minasyan, Founder and President of Krisp.ai, a company that has navigated this complex landscape to become a leader in voice AI. His journey offers a masterclass not in hype, but in the pragmatic, strategic choices that underpin sustainable, world-class growth.
The real challenge, and the greater opportunity, lies not in replacing human effort but in amplifying it. This requires a more nuanced approach, an intentional choreography between human creativity and machine capability. It demands that we move beyond the rigid, outdated frameworks of customer segmentation and learn to read the dynamic, real-time language of customer signals.
In enterprise marketing, we live with a peculiar paradox. We are tasked with driving innovation, creating seamless customer journeys, and responding to market shifts with a nimbleness that borders on clairvoyance. Yet, we are often tethered to technology stacks that resemble a geological cross-section, with layers of legacy systems, undocumented processes, and business logic locked away in digital vaults no one has the key for. The brightest marketing strategies can, and often do, grind to a halt against the unyielding wall of technical debt.
The old playbooks, the reliable dashboards, and the comfortable strategies of yesterday are proving to be liabilities today. In this environment, agility isn’t just a buzzword; it’s the fundamental requirement for survival and, more importantly, for dominance. The question is no longer *if* we need to adapt, but *how* we build an operational model that can thrive amidst constant instability.
