Over the past 18 months, organizations across the Nordics have been urgently rethinking their MarTech stacks. Not because vendors told them to, but because AI has created a new wave comparable to the cloud transformation of a decade ago. In this episode of Data Driven Voices, Emma Storbacka, Claes Kaarni and Gustav Malmqvist discuss why MarTech modernization is happening everywhere at once, why it cannot succeed without organizational change, and why the real problem is often data, not technology.
In this blog, we summarize some of the key takeaways from the episode.
Why MarTech modernization is happening now
The drivers fall into two categories: business imperatives (grow with fewer resources, justify tech investments, reduce manual work) and a new technology wave. Gustav frames it simply:
“10, 15 years ago, everyone was doing the cloud upgrades. You went from on-prem, you had your client console on your computers. There was only six people who could be in the system at the same time. And now there’s a new wave with technology and it’s quite simple: AI.”
The cloud transformation enabled omnichannel and personalization at scale. Now AI promises to scale even further. But many organizations face a gap between what competitors supposedly do and their own reality of manual work and AI experiments stuck in sandboxes. Some also face technical forcing functions where legacy systems reach end of life, creating a natural moment to question everything.
Technology renewal as catalyst for organizational change
When organizations struggle with MarTech, is it the technology or how people work? Both, but fixing technology alone solves nothing. Claes observes:
“I’ve seen one bigger client working on this way, they’re using this renewal for the organizational change. So this is the excuse for the operational change to start the technology change. Not that there’s some features and some capabilities always that a new technology could provide, but usually the change is in the people more than the tech.”
The data-driven world requires a different operating model. Organizations used to dump data into MarTech and let marketers sort it out. If you want real automation, you need to fix the data, trust it, and let automations run. Gustav emphasizes that modernization isn’t swapping one tool for another. It’s rethinking the full process, and that process lives in how people work.
The bike shedding problem
Emma introduces bike shedding: the tendency to focus on trivial details everyone can understand while ignoring complex fundamentals. The term comes from people planning a nuclear power plant who spent most time discussing the bike shed outside because everyone understood bike sheds, while nuclear engineering was too complex to contribute to meaningfully.
In MarTech, senior executives spend time evaluating email editors and journey canvases while ignoring data warehouse architecture that determines success. Anyone can have an opinion about sending emails. Data models and technical architecture? Not so much. Gustav relates immediately, noting people comment on chart sizes in mockup dashboards rather than the structure. The solution requires maturity: understanding you need to start from the source with data and build models that scale.
Data first, or nothing works
If you don’t fix your data foundation, nothing else matters. Gustav is direct:
“If you don’t have your data warehouse in order, you will never be able to excel further down in value chain. So make sure whatever you do in your modernization, that you go all the way to the bottom and correct it at the source.”
There’s huge risk that organizations renew MarTech but never touch the data that was the actual problem. Claes lists this as the top mistake: underestimating data work and change resistance. The MarTech stack becomes the execution layer while the data platform becomes the system of truth. But that shift requires rethinking roles, skills, and team structures.
You cannot modernize without reorganizing
Emma asks directly: Can enterprises do MarTech modernization without reorganizing? Both answer: No. Claes clarifies it doesn’t mean massive headcount reductions, but tasks and themes must change. Gustav frames it simply:
“It’s always people, processes, the ways of working, right? So everyone can change. We just need to understand why and have a good reason. And that is for the leaders to present.”
Who should own this? Not the IT product owner. For real impact, you need C-level ownership: CMO, CFO, Chief Sales Officer. You need someone who cares about the bottom line and understands how technology provides value. Emma pushes back: if ownership needs to be at this level, isn’t “MarTech modernization” the wrong framing? Shouldn’t we talk about data-driven go-to-market or commercial transformation? Claes agrees. The commercial landscape is moving toward a new paradigm where technology plays a bigger role.
The top mistakes to avoid
The top mistakes: underestimating data work, underestimating organizational change resistance, and failing to include new use cases. Claes is clear:
“You don’t create extra value by just doing the same thing with the new technology. You need to have new use cases. You need to have an ambition to do new stuff, engage the client in a new way. So if you just plan to send emails to the new platform, then you won’t rock the boat enough.”
The business case cannot be “same work, different platform.” You need ambition to expand capabilities and drive measurable impact. Gustav adds a fourth mistake: big bang implementations. Trying to change everything simultaneously while running daily business is nearly impossible. Take an incremental approach. The path forward: start with data platform architecture built to scale, know your use cases, and build an organization that constantly tests and questions. Maintain an explorative mindset and reserve capacity for experimentation.
Key takeaways for MarTech modernization
Claes and Gustav’s discussion reveals what actually works and what causes expensive failures:
- AI is the new cloud wave: Just as cloud drove the first MarTech wave 10-15 years ago, AI is creating a new wave happening across industries simultaneously.
- Use tech renewal as catalyst: Smart organizations use technology implementation to restructure how people work and break down silos.
- Beware bike shedding: Executives spend time evaluating email editors while ignoring data architecture that determines success.
- Data first, or nothing works: Without your data foundation in order, you cannot excel. Fix data at the source before worrying about tools.
- You cannot modernize without reorganizing: Tasks, teams, and ways of working must evolve alongside technology.
- C-level ownership required: MarTech modernization needs CMO, CFO, and Chief Sales Officer involvement, not IT product managers.
- New use cases required: The business case cannot be “same work, different platform.” You need new capabilities and measurable impact.
- Avoid big bang implementations: Take an incremental approach that delivers value in phases rather than changing everything at once.
For organizations evaluating MarTech modernization, the conversation needs to move beyond software selection. Are you ready to fix your data foundation? Are you willing to reorganize? Does your C-level understand this is commercial transformation, not IT projects?
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.