Interview
The Gist
- MACH and AI are mutually reinforcing. Organizations embracing MACH principles are significantly more likely to implement AI successfully — 77% vs. 36%.
- Composable tech drives real AI outcomes. Modular architecture gives CX teams agility to test, scale and swap AI capabilities without vendor lock-in.
- Ethical AI starts with MACH foundations. MACH’s openness and flexibility empower better governance, transparency and control over AI use.
CHICAGO — Let’s get real: we’ve all heard that MACH (Microservices, API-first, Cloud-native, Headless) is the blueprint for digital modernization.
We’ve also heard that AI is the next big leap in customer experience.
But here’s the question nobody seems to answer clearly — do these two actually work together, or are we just layering buzzwords and hoping for magic?
So we went straight to the source. We're here in the Midwest this week to get the scoop at The Composable Conference put on by the MACH Alliance, the nonprofit that provides education around the MACH and composable philosophies for managing digital technology stacks.
In this one-on-one, I sat down with Casper Rasmussen, president of the MACH Alliance, to dig into the relationship between MACH maturity and AI readiness, examining some of the results from the 2025 MACH Alliance Global Annual Research report.
Spoiler: it’s not just about tech stacks — it’s about mindsets. And according to Rasmussen, organizations that embrace MACH aren’t just playing catch-up with AI — they’re leading the charge. We talk AI brain-building, “vendor AI” vs. the real thing, and how composable systems give CX leaders more control than ever before — if they’re willing to think modular.
Table of Contents
- How MACH Maturity Lays the Groundwork for AI Adoption
- Beware of Vendor AI: Why Bolt-On Features Fall Short
- Modernization Without a Full Rip-And-Replace
- Putting CX Leaders in the AI Driver’s Seat
- The Right Foundation to Operationalize CX Teams at Scale
- Driving Next-Gen Engagement With an AI Portfolio Mindset
- Composable Control: Governing AI Responsibly at Scale
- Why MACH Maturity Changes How Teams Deliver Customer Value
How MACH Maturity Lays the Groundwork for AI Adoption
Nicastro: Is MACH maturity really driving AI — or are both just symptoms of broader digital modernization?
Rasmussen: MACH and AI is a mutually reinforcing relationship rather than a simple cause-effect dynamic. Organizations that embrace MACH principles develop the technical foundation and organizational mindset that naturally enables AI and agentic adoption. Our research shows that 77% of organizations well along in their MACH journey are leveraging AI, compared to only 36% of those new to MACH.
What's happening is that forward-thinking companies recognize that both MACH and AI require similar organizational capabilities: acceleration through innovation, focus on real-time data and connectivity, and the ability to compose applications to achieve fit-for-purpose solutions. Companies investing in these capabilities position themselves to succeed with AI and agentic solutions through modern architecture. MACH is aligned with the technical requirements and mindset that make AI initiatives successful.
Beware of Vendor AI: Why Bolt-On Features Fall Short
Nicastro: Are organizations mistaking vendor AI for real AI integration?
Rasmussen: Unfortunately, many organizations are settling for AI features bolted onto legacy systems — what you might call "vendor AI" — rather than pursuing true AI integration that can re-invent their business. The difference is significant.
Vendor AI typically offers pre-packaged capabilities within a closed ecosystem, which limits expandability, utility and creates new forms of lock-in. True AI integration allows organizations to:
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Leverage best-of-need AI and GenAI capabilities across their entire business
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Unify data streams across functions and systems to create a comprehensive "knowledge foundation"
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Orchestrate data streams and AI capabilities to achieve agentic solutions, with sufficient autonomy to solve complex, multi-step problems
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Swap AI components as technology evolves without major disruption
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Scale AI workloads dynamically according to business needs
As Amanda Cole points out in her analysis, organizations need to build a comprehensive "AI brain" before focusing on execution tools. This approach requires open, modular architectures that give businesses control over their AI destiny rather than delegating it to any single vendor's roadmap.
Related Article: Digital Experience 2024: Insights From the Frontlines of AI, CMS and DXPs
Modernization Without a Full Rip-And-Replace
Nicastro: Do older organizations risk falling irreversibly behind in AI readiness?
Rasmussen: The risk of organizations falling behind in AI readiness isn't about age — it's about architectural approach. Organizations clinging to monolithic architectures face increasing disadvantages as AI advances. As our research shows, companies with traditional systems report spending nearly 60% of their IT time and budget on upgrades alone, severely limiting resources available for innovation.
However, the path forward doesn't require "ripping and replacing" everything. As Malte Ubl emphasizes, "Digital transformation isn't about starting over, it's about creating a technology foundation that can evolve with your business." Successful organizations take an incremental approach, focusing on high-impact areas first while gradually modernizing core systems.
What's critical is starting the journey now. Each year of delay multiplies the eventual cost of modernization while competitors gain increased agility. The good news is that with MACH principles, organizations can modernize gradually, focusing on outcomes rather than wholesale transformation.
Putting CX Leaders in the AI Driver’s Seat
Nicastro: How can MACH give CX leaders more control over AI outcomes?
Rasmussen: MACH architecture empowers CX leaders with unprecedented control over AI outcomes in three critical ways:
- First, it provides data openness and connectedness. AI models are only as good as the data they access. MACH's API-first approach unifies streams of data across PIMs, CDPs, DAMs and other sources, ensuring AI always operates with complete, real-time context.
- Second, it enables experimentation without commitment. CX leaders can quickly test and orchestrate different AI approaches — whether for personalization, content generation or customer service — without being locked into a single vendor's solution. When a better AI capability emerges, it can be integrated without disrupting the entire flow.
- Third, it lets brands tap into the agentic era of commerce. Rather than simply relying on traditional direct channels for business, CX leaders can connect their own digital capabilities to industry-wide AI and agentic interfaces — whether that's to drive product discovery, customer support or customer loyalty in agentic environments.
As the MACH Alliance research demonstrates, this level of control translates directly to better customer experiences, with MACH-mature organizations reporting they're better able to meet and exceed customer expectations.
Related Article: Smarter CX, Better Outcomes: How AI Maximizes Customer Value
The Right Foundation to Operationalize CX Teams at Scale
Nicastro: Organizations with higher MACH maturity are more likely to be actively implementing AI, not just experimenting. What does this mean for CX teams trying to shift from AI potential to performance?
Rasmussen: The correlation that organizations with higher MACH maturity are more likely to be actively implementing AI, and not just experimenting, reveals something critical for CX teams: the shift from AI potential to performance isn't primarily about AI technology itself — it's about having the right foundation to operationalize AI at scale.
MACH-mature organizations succeed with AI implementation because they've already solved many of the underlying challenges that typically derail AI initiatives, including:
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Real-time data access across systems and business functions
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Open approach to data streaming and integration
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Cloud-native infrastructure that distributes and scales dynamically
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Organizational comfort with innovation and modular, composable approaches
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For CX teams seeking to move beyond AI experiments, this means focusing on the architectural foundation first. Rather than pursuing isolated AI use cases, they should advocate for MACH principles that will enable sustainable AI deployment across the entire customer journey.
The most successful approach, as outlined in our research, is to build a comprehensive "AI brain" about your business — your products, customers, processes and history — before focusing on specific automation tools. This foundation, which might take six to eight months to establish, ultimately powers capabilities across the entire customer experience.
Driving Next-Gen Engagement With an AI Portfolio Mindset
Nicastro: More MACH equals more advanced AI — but the practical leap comes when modular infrastructure empowers rapid testing, data orchestration and deployment. How can CX and marketing teams use this to drive next-gen engagement?
Rasmussen: This is precisely where the competitive advantage emerges. CX and marketing teams in MACH-enabled organizations can drive next-generation engagement by taking an "AI portfolio" approach rather than betting on single-use cases.
Practically, this means:
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Layering AI capabilities. Deploy different specialized AI applications across the customer journey rather than seeking a one-size-fits-all solution. Use specialized AI for content creation, another for personalization, another for conversation.
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Implementing continuous learning loops. Set up your systems so customer interactions automatically improve AI models, creating a virtuous cycle of improvement.
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Creating adaptive experiences. Design customer journeys that dynamically reconfigure based on real-time context and AI insights rather than predetermined paths.
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Testing with minimal risk. Conduct A/B tests of new AI capabilities with specific customer segments before broader deployment.
The modular nature of MACH allows teams to experiment constantly without technical complexity. For example, a retailer could implement AI-powered next best actions, test them with a specific customer segment, measure results and either scale up or try a different approach — all without disrupting the core shopping experience.
Related Article: Real-Time Customer Journey Analytics Starts With Smarter Data Infrastructure
Composable Control: Governing AI Responsibly at Scale
Nicastro: How does MACH architecture change the calculus for responsible AI governance? Composable systems promise more control — but do they also fragment accountability? As MACH platforms scale AI deployment, how can marketing and experience teams ensure ethical use and data compliance?
Rasmussen: MACH architecture fundamentally changes the governance equation by decoupling control from centralization. Rather than forcing a choice between rigid control and flexible innovation, it enables both through thoughtful governance.
The key is establishing thoughtful governance – architectural guidelines that balance flexibility with control, standards for APIs and data models and designs where components can be replaced without disrupting the whole system.
For marketing and experience teams concerned about ethical AI use, MACH architecture provides several advantages:
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Transparency: The modular nature makes it easier to audit how each AI component processes data and makes decisions
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Consistency: Standardized APIs and data models ensure AI tools work with properly governed data
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Control: Teams can replace AI components that don't meet ethical standards without disrupting the entire stack
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Adaptability: Governance frameworks can evolve as regulations change
To ensure ethical use and compliance, organizations should:
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Establish a center of excellence that develops governance standards for all AI components
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Implement monitoring at the API level to ensure data use compliance
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Create clear documentation of how customer data flows through various AI systems
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Design flexible consent management that can evolve with changing regulations
This approach turns what could be fragmentation into a strength — a system of checks and balances that's more resilient than monolithic approaches to governance.
Actionable Insights for Digital CX Technologists
Editor's note: Here are Casper Rasmussen's recommendations for applying MACH principles to AI and digital experience success:
| Advice Area | Actionable Recommendation |
|---|---|
| AI Integration Strategy | Avoid vendor AI bolted onto legacy systems; prioritize open, modular infrastructure that supports composable AI integration. |
| Composable Architecture | Adopt MACH principles (Microservices, API-first, Cloud-native, Headless) to enable scalable, fit-for-purpose digital solutions. |
| Data Governance | Use MACH’s API-first architecture to unify real-time data streams and improve AI context and transparency. |
| Experimentation and Innovation | Run rapid A/B tests on AI features without long-term commitment thanks to MACH’s modular flexibility. |
| Modular Infrastructure | Take an “AI portfolio” approach: layer AI tools across the CX journey rather than rely on monolithic AI platforms. |
| CX Leadership Empowerment | Enable CX leaders to orchestrate AI solutions, swap tools easily, and align outcomes with customer needs. |
| Incremental Modernization | Don’t wait for full re-platforming. Start with high-impact areas and modernize incrementally with MACH. |
| Ethical AI Deployment | Use MACH's modular transparency to build ethical AI governance — monitor APIs, document data flows, and maintain audit trails. |
Why MACH Maturity Changes How Teams Deliver Customer Value
Nicastro: Many MACH-mature orgs say they're better able to meet — and even exceed — customer expectations. Is that because MACH enables faster AI deployment, or because it forces teams to rethink how they deliver value?
Rasmussen: It's both, but the deeper transformation is in how teams rethink value delivery. MACH architecture requires organizations to break down traditional silos, focus on outcomes rather than features, and build systems around customer needs rather than internal processes. Companies that have already done so are ahead of the rest when implementing AI with the same focus.
This mindset shift changes how companies work. When teams design with composability in mind, they focus on the essential customer value each component delivers. This clarity — combined with the ability to act on insights quickly — creates a foundation for better experiences.
The ability to deploy AI faster matters, but the change in how teams work drives the real results. As Malte Ubl notes, "Architecture should never be pursued for its own sake. It's a means to an end, the end being better outcomes, improved customer experiences, and greater organizational agility."
The most successful MACH organizations don't just implement new technology — they use it to transform how they approach customer experience, building systems that can evolve as quickly as customer expectations change.
