Scaling with Data & AI: results over hype
Your organization is growing, but the data you need to work smarter is fragmented across systems, departments, and Excel files. New initiatives, including AI ones, keep running into the same wall: a demo that wins people over, a production environment that falls short — because the underlying data is incorrect, incomplete, or doesn’t connect.
Only a solid data foundation and a rigorous business case can break that pattern. Welisa always starts there first, before deploying AI as a targeted layer that adds value right away.
Why most pilots stall before they deliver anything
Bad data always gives bad answers
AI makes mistakes based on context, not logic. If that context is fragmented, outdated, or inconsistent, the system generates flawed assumptions. Garbage in, garbage out.
A pilot without a foundation never gets further than a demo
Many projects start with the tool: licenses, a showcase, a proof of concept. But without streamlined processes and reliable data, the promise of AI never gets fulfilled.
Costs pile up, results don't materialize
Without a clear business case and a per-transaction cost model, you don’t know what AI is actually delivering. You’re investing in something you can’t justify to leadership.
More grip?
The result of getting this right
Reliable data foundation
- A single, harmonized source of data
- AI receives the correct context for decision-making
- Prevents incorrect assumptions (hallucinations)
Operational efficiency
- Reduction in manual labor and errors
- Faster information processing
- Smarter application of automation
Targeted use of AI
- Applied only where it adds genuine value
- Addresses structural bottlenecks and time-consuming tasks
- Complete control over processes
How we make it happen
Wij brengen strategie, architectuur en realisatie samen binnen één geïntegreerde Data & AI-aanpak.
Data foundation (Data Cloud)
We consolidate data from ERP, webshop, and legacy systems into a single source of truth.
Process analysis & optimization
We identify exactly where manual work acts as the primary bottleneck.
AI for bottlenecks
We deploy AI specifically where processes stall due to unstructured data.
+28% hogere forecast-betrouwbaarheid binnen 4 maanden
De verbeterde forecast-betrouwbaarheid bij Vredo kwam niet voort uit één losse optimalisatie, maar uit een samenhangende Data & AI-aanpak binnen Salesforce. Diezelfde structuur is toepasbaar in iedere organisatie waar data strategisch wordt ingezet.
Trusted by organizations such as
The foundation in order (data ready)
Without clean, harmonized data, any AI strategy will collapse. We therefore start at the base. Using solutions such as Salesforce Data Cloud, we filter complex data streams from your ERP, webshop, and legacy systems into a single source of truth. This provides the AI with the correct context to generate reliable answers and prevents the system from making incorrect assumptions (hallucinations). The data remains manageable and is immediately deployable for smarter processes.
Eliminating manual work with precision
We always start with an analysis of your process: where does manual work, such as searching for information or retyping data, slow down efficiency? For structured, predictable steps, traditional automation remains the best choice. You maintain one hundred percent control and consume no unnecessary computing power. We deploy AI very selectively where processes stall on unstructured information. Think of automatically reading complex PDF purchase orders or interpreting free-text emails in customer service.
Pragmatic scaling through a proof of value
We do not believe in massive ‘big bang’ migrations where everything has to change at once. We work ‘use case driven’. We build a prototype on your own dataset, so you can see directly in practice whether the AI understands your orders or customer inquiries. Before we roll this out across the organization, we create a clear cost model. We calculate the token usage per transaction, so you know exactly what the return on investment is before you scale up.
From automation to autonomous agents
Once the foundation is in place, we introduce digital employees that actually take work off your hands. Solutions such as Agentforce independently perform actions within your CRM. These agents do not just talk to users, but also, for example, retrieve live order statuses from your ERP or schedule technicians, strictly within your business rules. The human always maintains final control. As soon as a situation becomes too complex, an automatic escalation to an employee takes place, including the full context of the conversation.
Strategic insights on scaling with Data & AI
Deep dive into scaling with Data & AI
Receive the executive roadmap for a cohesive Data & AI strategy within Salesforc
Jaäl Pekelder
Sales Consultant
In control from day one
A Data & AI project must be predictable, not an experiment. That is why we work with a structured methodology and clear decision-making points.
Analyzing the business case
We always start with a process analysis. We weigh the time saved and increased capacity against the expected implementation costs and usage. We only begin once the ROI is crystal clear.
A prototype based on your data
We build a prototype using your own dataset. This allows you to see directly in practice whether the AI is capable of flawlessly understanding and processing your specific orders, PDFs, or customer inquiries.
Clear cost model per transaction
Before rolling out organization-wide, we make the costs transparent. We calculate the exact token usage per transaction, ensuring your operational costs are always predictable.
Controlled scaling
Only when the prototype proves effective and the business case adds up do we integrate the AI layer into your architecture. You remain in control, without risky, company-wide migrations.
Frequently Asked Questions
When is deploying AI actually profitable?
AI is profitable when it resolves a structural bottleneck in your organization, such as hours of data entry or delayed service. We always weigh the time saved and increased capacity against the implementation costs and usage.
Is my current data ready for AI applications?
Often not entirely, and that is perfectly normal. By using data platforms, we harmonize your fragmented sources. This creates a reliable foundation, providing the AI with the right context to operate safely.
What is the difference between traditional automation and AI?
Traditional automation operates based on fixed ‘if-then’ logic and is perfect for structured data. AI understands context and unstructured information, enabling it to independently extract intentions from an email or process unknown file formats.
How do we guarantee that sensitive data does not end up in public models?
We build our solutions within closed ecosystems. Your business data is used temporarily to perform a task, but it is never stored by the AI provider or used to train their public models.
Do we need to adapt our entire IT landscape to start with AI?
Certainly not. We integrate AI applications into your current systems or make them accessible centrally via middleware. This allows you to build upon your existing foundation step by step, without risky, company-wide migrations.
How does AI billing work and what are token costs?
AI models charge per processed piece of text (a token). Through our pilot projects, we accurately map out this usage per action in advance, ensuring that operational costs are always predictable and in balance with the delivered value.