Marketing Tech Monitor 2026: 33 percent utilization, 8 percent CRM adoption, 47 percent project failure

On July 7, the Marketing Tech Monitor 2026 from the Hamburg-based Marketing Tech Lab published new figures. It surveyed 1,562 marketing decision-makers in the DACH region, 414 of whom gave complete answers and took part in supplementary qualitative interviews. Study director Ralf Strauß sums up the result in one sentence: there is heavy investment in AI and marketing technologies, but the value created too often falls short of what is possible.

We analyzed the study together with the Bitkom figures on the ICT market, published at the same time, and the IW-Consult data on AI use in the German economy. This post summarizes what SME owners can concretely take from the data for the second half of 2026. No sales pitch, no fantasy promises, just an honest assessment with recommendations for action.

The five figures from the Marketing Tech Monitor 2026

1. 33 percent utilization

On average, large marketing and sales organizations use only 33 percent of the operational capacity of their existing MarTech and SalesTech applications. Up to 70 percent of the applications introduced are used inadequately. Licenses are paid for, the shelf is full, the impact is close to zero.

For SMEs this means: before buying a new tool, we first check what the existing portfolio actually delivers. In our client projects, we start with a tool inventory. An Excel table, three columns: name, monthly license cost, active usage per week. That takes 90 minutes. And it regularly uncovers duplicate subscriptions and dormant licenses running into four-digit euro amounts per year.

2. 8 percent full CRM adoption

68 percent of the companies surveyed use a CRM system. Only 8 percent fully exploit its capabilities. For marketing automation solutions the figure is even lower, at 4 percent.

This is not a technical question. The systems can do more than their operators know. The Marketing Tech Monitor names the cause clearly: data quality, processes, and skills hold back value creation more than missing software does. Anyone who does not update the CRM database regularly gets expensive random results out of an AI analysis.

A practical approach for SMEs: twice a year, a 4-hour session with the sales team. Clean up duplicates, delete outdated contacts, fill in required fields. This is the foundation, and without it no marketing automation and no AI analysis works sensibly.

3. 70 to 80 percent project failure

47 percent of respondents say that between 70 and just under 80 percent of the projects in their organizations fail or do not achieve the intended success. The main causes cited are:

• 56 percent: insufficient technical know-how
• 44 percent: too many parallel projects
• 42 percent: unclear objectives

For us, point 2 is the most interesting. Busywork instead of focus. We see it regularly in conversations with management teams. Five projects running at once, none of them truly finished. In practice, an SME with clear priorities and three actively pursued projects beats any company that works half-heartedly on twelve topics.

4. Perception gap: leadership versus employees

53 percent of managers consider their teams poorly equipped to use data and tools effectively. At the same time, 49 percent of employees rate their own willingness to change as high. Managers see a competency problem. Teams see a communication problem.

Both are partly right. And no one is talking to anyone. This is a structural finding that cannot be fixed by buying a tool. In our projects we have had good results with a quarterly 90-minute retrospective. Leadership and the operational team at one table. What works, what blocks, which two changes for the next quarter.

5. Leadership as sponsor is missing

Ralf Strauß, the study director, puts the central point like this: buying AI is a matter for the boss. If it stays an IT project, it burns budget. In the study this shows up in the role in which AI topics are introduced. Where management holds responsibility and reviews regularly, success rates are significantly higher. Where AI is delegated to the next available IT team, the projects run into the typical patterns.

What the parallel studies say about this

Two further data points from the first week of July complete the picture.

Bitkom ICT market forecast of July 7, 2026: the German market for IT and telecommunications grows by 4.1 percent to 246.4 billion euros in 2026. Revenue from AI platforms rises by 75.8 percent to 3.1 billion euros. That is a massive investment surge. It meets an application ecosystem that, according to the Marketing Tech Monitor, uses only a third of the tools it already has.

IW-Consult study of July 8, 2026: 40 percent of German companies now use AI. An increase of 118 percent since 2024. Among businesses with fewer than 50 employees, the adoption rate is likewise just under 40 percent. 60 percent do not use AI, mainly because they see no relevance to their own business model (61 percent), lack capacity (34 percent), or have data protection concerns (29 percent).

And a contrast to that: on July 10, the Monopolies Commission submitted a report to the Minister of Economic Affairs. Its core message: German companies are hesitant on AI and are losing time in international competition.

The three data points do not contradict each other. They describe the same reality from different angles. The market is growing fast. Adoption is rising. But the actual value created per euro invested stays behind what is possible.

Five action steps for SMEs in the second half of the year

For the third quarter of 2026, we recommend our clients this concrete path. Every step can be implemented with in-house resources. External consulting is optional.

Step 1: tool inventory and utilization check

Effort: 2 to 3 working hours.

Collect all paid marketing and sales tools in one table. Per tool: license cost per month, active users, last actual use. Flag everything below 30 percent active usage. Two possible consequences: activate or drop. Half usage is not an option.

Step 2: CRM data quality as the foundation

Effort: 4 to 8 hours depending on size.

Before any AI tool is let loose on the customer data, the data base gets cleaned up. Remove duplicates, archive dead contacts, fill in required fields. Anyone who skips this step produces expensive random results. The Marketing Tech Monitor names data quality as the central bottleneck. In DACH SMEs, this point is almost everywhere the number one blocker.

Step 3: the three-project rule

Effort: one 90-minute meeting with the leadership team.

List all currently running projects. Prioritize by revenue leverage in the current fiscal year. A maximum of three get active resources. All others pause until one of the three is finished. This is uncomfortable. But it is the only way out of the 70 percent failure rate.

Step 4: AI policy and data processing agreement

Effort: 6 to 10 hours for the first draft.

Anyone who uses ChatGPT, Claude, Gemini, or similar tools productively needs, since February 2, 2025 under the EU AI Regulation, documentation of the competency obligation. In practice: a three-page internal AI policy plus a data processing agreement with the respective provider. A team version or enterprise license is assumed. The free variant is not data protection compliant in a corporate context.

Step 5: AI purchasing decisions are a matter for the boss

Effort: one monthly 30-minute meeting.

The Marketing Tech Monitor is clear on this point. Management owns the AI roadmap, or the project fails. In practice this means: a monthly meeting with a fixed agenda. What was done with AI this month, which time savings were concretely measured, which changes for next month. Not a status report with bullet points, but an honest assessment.

What we do ourselves

We have worked to this pattern since early 2026. Our own tool inventory listed 14 active marketing and sales tools in January. After the utilization check we ended up with 9. Savings: 3,780 euros in annual licenses. The freed-up time and budget went into structured AI use with team licenses and a documented policy.

An honest addendum: the utilization check is not complex. It fails on other points. First: no one voluntarily reads through license agreements. Second: it is uncomfortable to drop a tool a colleague introduced. Both points can be solved with a clear appointment and shared responsibility. But they need the backing of management.

Conclusion

The figures from the Marketing Tech Monitor 2026 are no cause for panic. They are a reason for honest work. 33 percent utilization means: in most SMEs, a second third of performance is sitting in the tools already paid for. 8 percent CRM adoption means: data quality is the real bottleneck, not the software. 47 percent project failure means: the three-project rule is the most important management lever this year.

Anyone who consistently implements these five steps in the third quarter of 2026 gets more out of the existing budget in six to nine months than out of any new license. The fastest way to discuss this with us is via waterproof.agency/kontakt/.

• horizont.net: AI report on the Marketing Tech Monitor 2026, July 8, 2026
• bitkom.org: Digital industry remains stable, forecast, July 7, 2026
• ZEIT Online on the IW-Consult study: AI applications are booming, July 8, 2026
• n-tv Wirtschaft on the Monopolies Commission report: German economy is falling behind on AI transformation, July 10, 2026