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Striped Giraffe Innovation & Strategy GmbH, Lenbachplatz 3, Munich (2026)

01/09/2026

Most B2B commerce transformations still begin with a familiar question: “Which platform should we choose?” However, we advise our customers to design commerce around their workflows before selecting a platform.

Consider a typical B2B order.

A customer requests a quote, pricing needs approval, product data comes from PIM, availability from ERP, and contract terms from CRM. What looks like one transaction to the customer is a chain of connected decisions across the organization.

This is the idea behind workflow-first architecture: designing the technology landscape around how work actually moves through the business.

The shift can have tangible consequences.

Better-connected workflows can shorten quote cycles, accelerate fulfillment, improve service coordination, and create a more consistent customer experience.

For B2B commerce, this changes the architectural question. Instead of asking which platform should run the transaction, companies increasingly need to ask how the entire workflow should run across systems.

That may be where the next phase of commerce modernization begins.

What do you think? Is workflow-first architecture here to stay, or will it join the long list of commerce buzzwords that briefly captured the industry’s attention?

27/08/2026

🏗️ Construction projects depend on critical information scattered across specifications, drawings, contracts, permits, RFIs, and project correspondence.

AI-powered document processing helps teams uncover, connect, and validate that information before it contributes to delays, disputes, rework, cost overruns, or safety risks.

This capability is transforming several document-intensive construction processes, including:

👉 Design and specification review

AI compares drawing revisions, technical specifications, and project documentation to identify inconsistencies before they affect ex*****on.

👉 Contract and tender analysis

AI reviews tender packages and contracts to highlight obligations, deadlines, commercial risks, and contractual requirements.

👉 Project correspondence and RFIs

AI analyzes emails, meeting minutes, RFIs, and change requests to surface relevant project knowledge and support faster responses.

👉 Supplier and subcontractor documentation

AI validates certificates, qualifications, approvals, and contractual documents to accelerate onboarding and reduce project risk.

AI-powered document processing is only one part of a much broader opportunity for the construction industry.

As AI adoption accelerates, companies can use this technology to reduce costs, improve processes, and manage project risks more effectively.

Discover more practical AI applications in our free booklet “AI in Construction”:
https://striped-giraffe.com/en/ai-construction/

25/08/2026

𝗘𝘃𝗲𝗿𝘆 𝗘𝗾𝘂𝗶𝗽𝗺𝗲𝗻𝘁 𝗮𝘀 𝗮 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 (𝗘𝗮𝗮𝗦) 𝗶𝗻𝘃𝗼𝗶𝗰𝗲 𝗯𝗲𝗴𝗶𝗻𝘀 𝘄𝗶𝘁𝗵 𝗮 𝗱𝗮𝘁𝗮 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻. Before a manufacturer can issue an invoice, it must first determine what actually happened.

Depending on the commercial model, that means answering questions such as:

🔸 How many parts were produced?
🔸 How many operating hours were delivered?
🔸 What level of availability was achieved?
🔸 Was the agreed performance target met?

The answers determine what the customer pays.

That makes operational data part of the revenue process itself.

In traditional manufacturing, operational data primarily supports service, maintenance, and performance improvement.

In EaaS, the same data also supports pricing, billing, revenue recognition, contract compliance, and customer transparency.

The challenge is that operational events rarely arrive in a form finance systems can use directly.

They must be translated into commercial events:

🔸 Usage becomes a billing record.
🔸 Performance becomes a contractual outcome.
🔸 Service delivery becomes a billable event.

This is why data governance, quality, traceability, and integration become strategic capabilities for manufacturers pursuing EaaS.

When customers, finance teams, and service organizations rely on the same data, trust increases and commercial processes become easier to scale.

The equipment may create the value.

The invoice depends on proving that value was delivered.

If your organization launched an EaaS offering tomorrow, would your data stand up to customer scrutiny?

20/08/2026

What happens when your website, salesperson, and call center give the same customer different answers?
For over half of B2B buyers surveyed by McKinsey, that inconsistency could be enough to switch suppliers.

Last year’s Global B2B Pulse Survey by McKinsey & Company showed that poor digital experiences and gaps in cross-channel tracking were the top two reasons for switching. This year, inconsistent information across teams has taken the lead.

The reason may be how B2B commerce has evolved:

Omnichannel has become the baseline, with buyers using an average of 10 channels throughout their purchasing journey.

For B2B leaders, this raises a more fundamental question:

Can my customer start a journey in one channel and continue in another without losing context or receiving conflicting information?

This requires an integrated architecture that keeps ERP, CRM, e-commerce, product and customer data, and other commercial systems aligned.

Our experience shows that such system synchronization is often the hardest part of an omnichannel initiative.

Consider a simple reorder.

A customer places an order through a sales representative. If that order never reaches the customer’s online account, they cannot track it there or easily reorder it later.

Meanwhile, McKinsey found that 39% of B2B buyers prefer digital self-service for reordering. That is even higher than the 36% of buyers who use digital self-service for placing orders.

This creates a clear requirement: the customer’s digital account needs to reflect the full order history, including orders placed offline, and make them easy to reorder.

This example shows what omnichannel looks like now when the customer is in control of the journey.

Are you sure that every channel in your organization gives your customer the same answers?

18/08/2026

𝗕𝟮𝗕 𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲 𝗶𝘀 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗶𝗻𝗴𝗹𝘆 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗼𝗻-𝗹𝗶𝗸𝗲. And as models based on usage, outcomes, time, or access gain traction, companies must redesign how they charge for value.

The real complexity here lies in connecting customer activity with revenue.

Companies need to determine what should be charged, when charges should occur, and how usage, outcomes, or service delivery translate into billable value.

This requires establishing a continuous connection between commercial agreements, customer activity, pricing logic, and payment processes.

The shift is particularly visible in industrial equipment.

A manufacturer can still sell a machine as a one-time transaction.

Instead, it can also build a commercial model (Equipment as a Service) where revenue depends on machine usage, performance levels, maintenance services, production output, or other metrics defined in the commercial agreement.

The pattern extends well beyond industrial machinery.

As more revenue depends on measurable customer activity, billing becomes as much a data challenge as a payment challenge.

This requires visibility into:

🔸 what the customer used
🔸 when it was used
🔸 which pricing rules apply
🔸 how usage translates into charges

When those elements are unclear, invoices become difficult to explain, disputes increase, customer trust declines, and revenue models become harder to scale.

This is why successful companies treat billing as part of their commercial architecture.

They connect usage data, pricing logic, customer information, and payment infrastructure into one consistent process.

For B2B leaders, an increasingly important question emerges:
Can your organization measure value precisely enough to monetize it continuously?

Because the future of B2B payments will increasingly depend on the ability to transform customer activity into transparent, accurate, and scalable revenue streams.

Is your payment infrastructure ready for revenue models built around usage, outcomes, or recurring value delivery?
Let us know in the comments.

13/08/2026

MDM is moving beyond the idea of a single trusted record, and the meaning of “master” is expanding with it.

The latest report from Stibo Systems describes the next chapter of Master Data Management as creating a shared business understanding that applications, workflows and AI systems can rely on.

That understanding goes beyond attributes and records. It includes entities, relationships, intent, rules and context. A product, for example, only becomes truly meaningful when it remains connected to the customers, suppliers, locations, inventory, pricing and other business context around it.

This becomes increasingly important as decision-making spreads across people, applications, workflows and intelligent systems. The more distributed those decisions become, the greater the need for a shared authority on meaning, context and business rules.

Stibo sees MDM evolving into an authoritative layer for shared business context — continuously validating, enriching and harmonizing critical data, relationships and meaning across the enterprise.

Autonomous agents make this requirement even more visible. When machines act at scale, trusted context, provenance and validation become essential to keeping decisions governed.

Perhaps the most concise way to capture the vision is Stibo’s own:

“Intelligence is distributed, but understanding is centralized.”

Could this broader role make MDM one of the critical architectural layers of the AI-enabled enterprise?

10/08/2026

A product record can be 95% complete and still fail when the business needs it most.
The reason is simple:
Not every product attribute creates the same business value.

➡️ A missing product image affects a different outcome than a missing sustainability declaration.
➡️ An incomplete technical specification creates different consequences than a missing logistics dimension.

The real challenge is identifying which attributes matter most for each business objective.

Product information now supports a wide range of activities:

🔸 Product discovery across search engines and marketplaces
🔸 Logistics, fulfilment, and supply chain operations
🔸 Regulatory reporting and compliance
🔸 Partner onboarding and product syndication
🔸 AI-powered search, recommendations, and agentic commerce

Each use case depends on a different set of attributes.

➡️ Marketplace teams may prioritize category attributes and technical specifications.
➡️ Compliance teams may focus on certifications, declarations, and traceability data.
➡️ Logistics teams depend on dimensions, weights, and packaging information.

Leading organizations therefore assess product data completeness in relation to the outcomes they want to achieve.

They identify the attributes that directly influence:

🔸 discoverability
🔸 revenue
🔸 operational efficiency
🔸 regulatory compliance

Then they focus governance and improvement efforts accordingly.

As product ecosystems grow more complex, deciding which information deserves the highest level of governance becomes a strategic business decision.

❓ Which product attributes create the greatest business value in your organization?

06/08/2026

One insight from the new “2026 State of B2B eCommerce Report” by our partner SAP and Master B2B eCommerce may prove more important than every chart inside it:

❗ 2026 may be remembered as the year B2B stopped investing primarily in commerce platforms and started investing in intelligence.

The numbers point in the same direction.

For the first time in the history of this survey, AI, search, recommendations, product content, and data quality attract more investment attention than the commerce platform itself.

That is a profound shift.

For nearly two decades, digital commerce strategies revolved around building the transactional engine. Today, executives are asking a different question:

How can we make every decision inside that engine smarter?

The report contains several signals that reinforce this shift.

Only 6% of executives see competing with Amazon as one of their biggest challenges.

Instead, executive attention is centered on digital transformation, data, and customer experience, the capabilities that determine how effectively organizations can use intelligence at scale.

2 other results are equally revealing.

🔸 Only 37% expect AI-powered chatbots to become one of the defining purchasing channels in B2B over the next three to five years.
🔸 Yet 81% are increasing AI spending.

That tells us many organizations currently see AI as an accelerator for operations, search, product discovery, and decision-making long before it becomes the customer's primary buying interface.

The winners will be the companies that turn AI from a feature into an operating capability across commerce.

03/08/2026

With most provisions of the EU AI Act taking effect on 2 August, the challenge extends beyond compliance into the foundations of enterprise architecture.

As more of the regulation becomes applicable, many organizations are focusing on policies, risk classifications, and documentation.

But compliance becomes difficult when companies cannot reliably answer basic questions:

🔸 Which AI systems are currently in use?
🔸 Which business processes and decisions do they influence?
🔸 What data do they access?
🔸 Who is responsible for their operation and oversight?
🔸 How are outputs, interventions, and changes recorded?

These questions cannot be answered by legal teams alone.

They depend on how AI is embedded across applications, data platforms, workflows, and enterprise systems.

For higher-risk use cases, the EU AI Act requires capabilities such as risk management, data governance, logging, technical documentation, human oversight, robustness, and cybersecurity.

This means compliance cannot simply be added after an AI solution has been developed.
It must be reflected in the architecture from the beginning.

Organizations need to know where AI is operating, which data it uses, how decisions can be traced, when human intervention is required, and how responsibilities are distributed across the organization.

That requires more than an AI policy.

It requires:

🔸 a reliable inventory of AI systems
🔸 clear ownership and access controls
🔸 traceable data and decision flows
🔸 integrated monitoring and logging
🔸 defined approval and escalation processes
🔸 architecture that supports governance throughout the AI lifecycle

The companies best prepared for the EU AI Act will not necessarily be those with the most documentation.
They will be those that have made transparency, accountability, and control part of their systems.

Because you cannot govern AI you cannot see.

Is your enterprise architecture ready to support responsible AI at scale?

30/07/2026

💳 The future of B2B payments will depend on intelligent transaction decisions.
Enterprise commerce operates across multiple markets, currencies, payment methods, regulatory environments, and customer requirements.

As this complexity grows, payment infrastructure needs to make more decisions during every transaction:

🔸 Which provider can deliver the highest approval probability?
🔸 Which payment route fits the current context?
🔸 When should a failed payment be retried?
🔸 How can fraud risk be assessed without adding unnecessary friction?

Modern payment platforms are increasingly designed to answer these questions automatically.

This is driving the evolution of payment orchestration, where payment methods, routing logic, retries, risk controls, and regional requirements are coordinated within a single adaptive infrastructure.

For some enterprises, this may involve multiple payment providers. For others, the priority is to consolidate a fragmented payment landscape and manage greater complexity through a single strategic platform.

The objective is not to add further complexity. It is to create an infrastructure that improves authorization performance, resilience, customer experience, and operational efficiency.

The payment stack increasingly resembles a traffic control system.

Each transaction follows a path determined by a combination of customer requirements, market conditions, payment methods, risk signals, and business rules.

The next stage of this evolution is driven by intelligence.

Payment decisions increasingly incorporate transaction history, risk signals, customer behavior, and machine learning models to improve outcomes in real time.

For B2B commerce leaders, payment infrastructure is becoming a strategic capability connecting revenue growth, risk management, customer experience, and operational efficiency.

Companies building adaptive payment architectures today will be better prepared for the increasing complexity of global commerce tomorrow.

How intelligent and adaptable is your payment infrastructure today?
Share your perspective in the comments.

Striped Giraffe Innovation & Strategy GmbH

01/09/2026

27/08/2026

25/08/2026

20/08/2026

18/08/2026

13/08/2026

10/08/2026

06/08/2026

03/08/2026

30/07/2026

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