Expert Advice for Marketing Leaders on MarTech, AI, & CX
Insights, ideas, and best practices for marketers that utilize artificial intelligence in their marketing.
While organizations are quickly deploying AI features to capitalize on efficiency gains, a widening “quality gap” is emerging between the pace of AI development and the capacity to rigorously test it. This gap directly leads to costly rollbacks and diminished user value, as highlighted in the The State of Digital Quality in Testing AI 2026 Report by Applause.
According to the Cloudera Data Readiness Index 2026, a survey of over 1,200 IT leaders, 79% of data-backed initiatives are hindered because organizations cannot access 100% of the data needed across environments (Cloudera, 2026). This article examines the critical components of data readiness, identifies common roadblocks, and outlines strategies for senior marketing and CX leaders to build a robust foundation for successful AI adoption.
Have you ever shipped an AI capability that looked great on launch day, then quietly started making your customer experience worse, one “personalized” suggestion at a time?
This is the part of the AI story that rarely shows up in vendor demos. A
model can be well-designed, thoroughly tested, and responsibly approved, and still become wrong. Not because the team did anything reckless, but because the world it learned from moved on. Catalogs change. Inventory fluctuates. Promotions end. Policies update. Customer behavior shifts with
seasonality and economic pressure. Your model keeps optimizing for last
quarter’s reality, while your customers live in today’s.
A recent study, the State of AI in Marketing Report 2026 by Callan Consulting, reveals that AI is now deeply embedded within marketing organizations, driving significant productivity gains and reshaping strategic priorities. This shift necessitates that senior marketing and CX leaders refine their governance frameworks, measurement strategies, and talent development initiatives to harness AI’s full potential while mitigating emerging risks.
True marketing agility isn’t just about making faster decisions; it’s about having a foundational trust in the information that fuels those decisions. It’s about building an infrastructure that can reliably deliver clean, governed, and accessible data to pivot not just a campaign, but an entire go-to-market strategy.
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.
What if your AI system is working exactly as designed… and still making your business worse? If you can’t clearly measure AI performance, you can’t
confidently say it’s working. Many organizations deploy AI with strong
pilots and impressive dashboards but lack a consistent evaluation framework once the system is in production.
Across the enterprise, marketing leaders are under immense pressure to deploy AI, automate processes, and unlock the efficiencies promised by an agentic workforce. We are moving with unprecedented speed, transitioning from AI as a clever assistant to AI as an autonomous actor—an agent empowered to negotiate, make offers, and resolve customer issues on behalf of our brands. The potential upside is enormous, promising a new frontier of personalized, scalable customer engagement. Yet, in our haste to innovate, we are collectively sidestepping a foundational question, one that keeps the most forward-thinking leaders up at night: when an autonomous agent makes a decision that costs the company millions, damages its reputation, or violates a customer’s trust, who is accountable?
Agentic AI promises autonomous lead research, scoring, and outreach, but experts caution that human oversight and measurable KPIs are critical to avoid compliance risks and brand damage.
