The mass adoption of AI has created an unprecedented demand for computing infrastructure. Data centers are keeping pace, competing to offer the most AI-ready infrastructure: high-density power, liquid cooling, and the latest GPUs. But is this enough to make a data center AI-ready?
While technical proficiency remains a key advantage, many organizations underestimate the importance of the commercial layer that ties this ecosystem together.
A provider may offer the latest high-performance gear and yet struggle to convert demand into revenue. Because today building great infrastructure is only part of the deal. You must also enable your customers to discover and use these services without friction.
This is the challenge many large data center operators and telecom providers face today. Most of them have successfully cleared the technical excellence barrier. The next step is no less intricate – create an experience that makes complex services easy to find, understand, and consume.
In this article, we explore the key pain points behind this challenge and discuss practical ways to address them.
The Commercial Complexity Problem
For decades, the data center business was relatively straightforward. Customers leased space, power, and connectivity, while providers focused on maintaining reliable facilities. Today, that model has evolved into something far more complex.
As data centers adapt to growing demands, they become complex ecosystems that connect infrastructure providers, cloud platforms, network carriers, partners, and customers.
What begins as a request for rack space quickly turns into a combination of interconnected products and services.
Consider a company deploying a new AI workload. It may need colocation space in one region, a dedicated cloud connection to Azure or AWS, redundant network paths, backup infrastructure in a secondary location, and compliance controls that satisfy industry regulations.
As service portfolios expand, things become even more intricate. A single customer order may involve multiple internal teams, external providers, and billing systems. In such scenarios, customer experience quickly becomes fragmented, while manual handoffs increase the risk of delays and configuration errors.
Why Customer Portals Fall Short
Many multi-customer organizations rely on self-service portals to reduce operational costs and manage routine tasks.
By providing access to information and self-service functions, these portals enable customers to submit requests and manage services on their own, without having to contact a company representative. It is a fast and convenient solution – as long as it is backed by effective processes behind it.
While many portals successfully automate most tasks, others simply hide operational complexity behind a modern interface. Underneath a top-notch UI, a request is routed into the same spreadsheets and approval chains that have existed for years. |
This happens because many organizations treat the portal as the product, when in fact it’s just the presentation layer. To create real value, the portal must be supported by an operating model, and follow certain design principles such as simplicity, accessibility, workflows, and deep understanding of customer needs.
Commercial Infrastructure as a New Competitive Advantage
Another challenge customer portals face is helping users navigate an increasingly complex catalog of services. Today’s enterprise customers expect more than access to infrastructure. They expect access to entire ecosystems.
This challenge is particularly visible in carrier-neutral and interconnection-focused environments. Organizations operating these ecosystems interact with hundreds of network providers, cloud vendors, technology partners, and managed service providers. And all their offerings should be presented in a way that makes them easy to discover, configure, and consume.
The answer to this growing complexity are marketplace-style environments – intelligent platforms supported by an effective operating model. And a key role in their evolution from simple self-service portals into adaptable infrastructure ecosystems is assigned to AI.
The Value of AI in Modern Marketplaces
Commenting on today’s mass AI adoption, Clive Deakin, a Strategic Technology and Transformation Executive at Iron Mountain and PivotingCloud, raised concerns and challenges that it is currently uncontrolled and sporadic.
"A new technology appears, so people start using it simply because it's available, not because they've identified a business problem it solves."
Clive argues that AI should be held to the same standard as any other investment: does it create value, improve performance, or provide meaningful differentiation? If yes, it deserves investment. Otherwise, it becomes an expensive distraction disguised as progress.
We share this perspective. So rather than asking where AI can be added, let’s look at where it creates tangible value in marketplace-style platforms.
AI-powered Discovery
Imagine entering a marketplace with 500+ infrastructure products. Most customers get overwhelmed, and finding the right solution among hundreds of offerings becomes a challenge in itself.
Traditional marketplaces rely on category trees, filters, and keyword search.
A mature marketplace allows customers to think in terms of outcomes rather than products. Instead of forcing them to navigate a long list of services, it translates business needs into the underlying infrastructure components. It is when questions like “Which carrier do you need?” or “Which connectivity option are you looking for?” turn into “What are you trying to achieve?”
Everything else – the decomposition, orchestration, partner selection, provisioning, and fulfillment – happens behind the scenes.
Templates and Scenarios
Enterprise customers are not constantly buying brand-new solutions. Many orders involve expanding existing deployments or replicating proven architectures in new locations. Instead of rebuilding the same solution from scratch every time, the platform stores it as a reusable template, making it possible to deploy it again with just a few clicks.
This is also an effective way to capture organizational knowledge. Instead of every sales engineer having to remember, “This is how we usually build an AI development environment,” the platform does. It stores proven solution patterns and suggests the required components, helping teams deliver faster and with less effort.
Guided Configuration
A human solution architect naturally asks questions, makes recommendations, and points out potential issues. AI can play a very similar role. Based on the customer’s business goals, it can recommend services, identify missing dependencies, and suggest suitable configuration options.
In essence, AI says: “Most organizations with similar workloads choose this configuration.” It doesn’t leave customers on their own with complex infrastructure decisions.
Conversational Interfaces
Traditional search is efficient – but only if your customers know exactly what they are looking for. Increasingly, they do not. Using conversational interfaces, users can interact with the marketplace and explain their requests via natural language.
Imagine a customer asking: “We’re opening a second office in Frankfurt. We need secure connectivity to our London site, high availability, and enough capacity for about 500 employees.”
The AI extracts location, bandwidth, redundancy, security, expected workload, and preconfigures the solution. The customer only reviews it.
AI-Driven Insights
On the provider side, AI analytics enables an X-ray vision into marketplace activity and customer behavior. When done right, it can transform the marketplace from a transaction platform into a source of business intelligence.
Patterns in searches, purchases, and service demand can tell a lot about customer needs and emerging trends. They can highlight underserved market segments and indicate opportunities for new offerings, paving the way for improvement and better customer service.
Commercial Platform Checklist
- Simplified complexity. The customer catalog should reduce the complexity of the underlying infrastructure instead of exposing it to customers – not all of them are solution architects. Rather than forcing customers to assemble individual service components, offer them easily consumable service bundles. Instead of selecting AWS Direct Connect, Carrier X, and Firewall Vendor Y separately, the customer simply chooses a Secure Hybrid Cloud Package. The platform assembles the solution underneath.
- Workflow orchestration. Customers see a single request, but behind the scenes multiple systems, teams, and partners may be involved. The platform should coordinate these interactions automatically, ensuring that orders move through their life cycles without handoffs.
- Modular design. Think of Lego bricks. Instead of building one giant hardcoded process, build reusable capabilities such as cloud connectivity, managed security, compliance, and monitoring, and combine them as needed. Such an architecture allows partners to be swapped in and out without affecting customer-facing services. If tomorrow Carrier B offers a better deal, you simply replace Carrier A in the workflow, and that’s it.
- API integration. Partners, carriers, cloud providers, billing systems, CRM platforms, and provisioning tools all need a way to communicate with each other automatically. APIs act as a glue connecting these systems. They allow services to be discovered, ordered, provisioned, and managed without human interaction. What’s more, they make it possible to onboard new partners and technologies without redesigning the entire platform.
- Standardization. Comparing data center products can be challenging due to their complex nature and varied specifications. Customers often struggle to navigate technical terminology, pricing models, and service dependencies. Sound categorization and standardized product descriptions simplify discovery and make comparisons easier.
- Consolidated platform. Customers no longer need to navigate multiple vendor shops or negotiate separate contracts. Instead, they can discover, configure, purchase, and manage services from multiple providers through a single interface, with a unified checkout experience and consolidated billing.
- Default governance. For customers working in regulated industries, governance solves a problem of paramount importance: staying compliant with internal policies and external regulations without manual reviews and approval. The platform enforces predefined rules automatically, ensuring that configured solutions are technically feasible, commercially valid, and compliant by design.
Rethinking Commercial Infrastructure with Marketplace 2.0
Technology should never require customers to think like technologists.
The highest achievement of a commercial platform isn’t to impress customers with how sophisticated it is – it’s to make that sophistication disappear behind an experience that helps them achieve what they actually came for.
As technology becomes more accessible, competitive advantage shifts to the ability to translate business problems into technology decisions. The organizations that succeed are those that make complex ecosystems feel simple by absorbing the underlying complexity.
At REW Technology, this vision became the foundation of Marketplace 2.0 – a governed, AI-powered commercial platform. Designed for companies that sell complex connectivity products, Marketplace 2.0 enables complex service discovery, guided configuration, order orchestration, billing integration, and partner ecosystem management, ensuring cohesive experience throughout all stages of customer journey.
Conclusion
Building AI-ready infrastructure is necessary – but in today’s competitive environment, it is an enabler rather than a differentiator. Technical excellence creates potential. Making that infrastructure easy to discover, configure, and consume is what turns that potential into business value.
Perhaps the defining characteristic of an AI-ready data center isn’t simply that it has enough power or cooling, but that it can participate effectively in an intelligent, adaptable infrastructure ecosystem that delivers business outcomes.
