The MarTech landscape is experiencing its biggest shift yet: the rise of agentic AI. But beneath the hype lies a more fundamental challenge. Most marketing organizations are stuck optimizing costs when they should be engineering value. The real transformation isn’t about adding more technology to the stack; it’s about fundamentally rethinking how marketing creates revenue.
In this episode of Data Driven Voices, host Emma Storbacka sits down with Frans Riemersma, founder of MartechTribe and co-author of the “MarTech for 2026” report to talk about the biggest shifts reshaping marketing organizations. Frans introduces Value Engineering as the framework for shifting marketing’s focus from cost-cutting to revenue growth, and shares his Hack, Pack, Stack methodology for turning experiments into scalable customer journeys.
In this blog, we summarize some of the key takeaways from the episode.
AI agents in marketing: Separating hype from reality
The 2026 MarTech report reveals a striking gap: 90% of organizations report using AI agents, but only 20-30% have them in production. Frans’s perspective is clear:
“AI is not taking over SaaS. It’s SaaS using AI that’s replacing SaaS.”
The research identifies three types of AI agents: agents for marketers (internal efficiency), agents for customers (guided choice), and most disruptively, agents of customers where people use their own AI to browse and purchase without visiting your website. But this implementation rate may never reach traditional software levels. Unlike deterministic SaaS with clear if-then logic, AI models are probabilistic. They require constant experimentation. Most organizations aren’t structured for this, and boardrooms demanding clear yes/no decisions struggle with AI’s inherent uncertainty.
Frans offers a simple heuristic to detect hype: listen for absolute language. “Always,” “never,” “yes,” “no” signal hype-cycle thinking. Real expertise shows up as contextual thinking: “when this happens, then that makes sense.”
The maturity order that most organizations get backwards
Through ten years of analyzing digital transformations, Frans discovered a pattern that explains why so many MarTech implementations fail. The required sequence, as he describes it, cannot be skipped:
“Your strategy maturity has to be slightly higher than your organizational maturity and your organizational maturity slightly higher than your technology maturity. You cannot automate what you haven’t standardized and you cannot standardize what you haven’t defined.”
Yet most organizations operate in reverse. They select technology first, then figure out processes, and only belatedly realize they haven’t defined success or identified their ideal customer. Ask 100 marketers to define their goal or ideal customer profile with specificity. Most struggle. Yet they’re expected to know what technology they need, what roles to hire, what skills to develop.
Frans’s background in marketing shaped his perspective. He kept seeing global brands invest heavily in platforms without clear business cases. The technology is just a hammer. It doesn’t build your house. When organizations lack strategic clarity, they cling to details: license fees, button features, pull-down menus. This explains why enterprise software evaluations devolve into comparing email editors rather than discussing business outcomes.
Why marketing lost the boardroom (and how Value Engineering wins it back)
Marketing has fallen off the balance sheet. Only 10% of CEOs have affinity with marketing. The IT-Marketing power struggle is universal immaturity, not unique to the Nordics. The financial dynamics explain why IT wins. License fees and operational costs appear clearly on the balance sheet. IT’s role in maintaining current revenue at scale is visible. But marketing’s focus on future revenue remains invisible.
“License fees, total cost of ownership campaigns, they show up on the cost side of the balance sheet. That’s very clear. But missed opportunities never show up on the balance sheet.”
Frans points to this structural problem as where Value Engineering changes the game. His breakthrough came 12-13 years ago at a global bank. They purchased five software modules but implemented only two. Within those, two specific buttons created 1.2 million euros in annual revenue. Initially, Frans thought this was an anomaly. But when students built business cases using their company data, the pattern repeated. Every single time.
Value Engineering shifts focus from efficiency (doing things less badly) to revenue upside. Frans discovered that 30% of boardrooms give marketing wrong targets, not because executives are incompetent, but because they lack data visibility. When teams bring quantified value cases showing concrete revenue opportunities, boardrooms embrace them without exception. The accountability sits with the boardroom and CFO, but the responsibility belongs to marketing. Growth hacking and demonstrating revenue impact have been lost as the function focused narrowly on leads and conversions.
Hack, Pack, Stack: The methodology for turning experiments into revenue engines
Frans developed this methodology through practical experience. Trying to force growth hackers to write clean code was torture for everyone. The three-phase framework respects different strengths:
Hack (Growth Hacking): The laboratory phase with dirty code and brilliant ideas. Focus is problem-market fit, not scale. 99% of experiments fail, but that 1% saves the company for the next decade.
Pack (Marketing Operations): The packaging phase. These people clean up, removing unnecessary integrations, fields, and code to prepare experiments for production.
Stack (IT): The factory phase for mature customer journeys. Once something has traction, IT implements it cleanly at scale.
In Frans’s experience, the natural tension between these groups isn’t conflict at all. It’s different roles serving different needs. Marketing always asks for more MarTech (experimentation tools), IT wants less (stability), and marketing operations sits in the middle optimizing what actually drives value. The methodology accommodates all three by defining clear transitions.
When should something move from Hack to Stack? When it has traction. You’re testing whether you’ve solved a problem customers care about enough to pay for. This can take two weeks or two years. A critical element: someone must own the entire customer journey end to end. That person should wake up sweating about friction points because they own the value it drives.
How AI is silently breaking down organizational silos
Something unexpected is happening in organizations implementing AI, something years of restructuring couldn’t accomplish. AI doesn’t work with fragmented information. It needs context that lives across departments: customer history from sales, support tickets, product usage data, marketing interactions, financial transactions. This forces teams that never collaborated to suddenly work together.
Frans hears the shift in how people talk: “I’m doing this with the other teams, such and so, these use cases, those customer journeys.” The language has shifted from channel focus to customer focus organically, driven by AI’s technical requirements rather than management mandates. Customers don’t care about internal silos. They want your product and want to engage with your brand. AI’s need for context forces organizations to mirror this customer perspective.
Bringing joy back to marketing
Value Engineering delivers more than revenue. It reconnects marketing professionals with why they chose the field. Not to optimize license fees or argue about button placement, but to understand customers, drive growth, and create value.
“When I do work with people on value engineering, the most often heard feedback is, thank you for giving my joy back in marketing. This is why I did marketing, studied marketing.”
Frans shares this as the most consistent response to his work. The framework provides clarity: start with strategy (who are we serving and what value are we creating?), build organizational capabilities, then select technology. Use Hack-Pack-Stack to move from experimentation to reliable revenue. Assign clear journey ownership. Let AI’s need for context break down silos naturally. When these pieces align, marketing regains its strategic seat at the boardroom table by demonstrating quantifiable revenue impact and owning the future growth engine of the business.
Key takeaways for marketing transformation
Frans’s framework offers a practical path forward for marketing organizations struggling with AI hype, technology bloat, and boardroom irrelevance:
- Move past the hype: Listen for absolute language (always/never, this or that). Real expertise shows up in contextual thinking: “when this, then that.”
- Respect the maturity order: Strategy before Organization before Technology. You cannot automate what you haven’t standardized, and you cannot standardize what you haven’t defined.
- Engineer value, not just efficiency: Missed opportunities never appear on balance sheets. Quantify revenue upside, not just cost savings.
- Build for different personalities: Hack (growth hacking), Pack (marketing operations), Stack (IT). Allow each phase to thrive with different people and goals.
- Assign journey ownership: Make individuals accountable for entire customer journeys end to end, measured by the value they drive.
- Use AI to break silos: AI’s need for cross-functional context creates organic collaboration. Embrace this rather than fighting it.
For organizations ready to transform: pick your highest-value customer segment and biggest revenue opportunity. Build the value engineering case. Then work backwards to define the strategy, organization, and technology needed to capture that value.
Inspiration for marketing, sales, and data professionals
Data Driven Voices is a podcast where Avaus together with industry experts, thought leaders, and partners discuss how to harness data, technology, and strategy to drive meaningful change and business results in primarily marketing and sales. The podcast shares actionable insights, success stories, and thought-provoking challenges to help professionals with new perspectives.