Next-Gen BI: from data to decisions, faster
Next-Gen BI: from data to decisions, faster
Business intelligence has spent the last decade getting more powerful and less accessible at the same time: as dashboards multiplied, data teams became bottlenecks. A new generation of tools (analytics chatbots, AI-assisted dashboards, and modern data platforms) are changing that with what we call Next-Gen BI.
Agilytic helps Belgian organizations adopt these capabilities honestly, starting from the questions that matter and building what actually gets used.
Business intelligence has spent the last decade getting more powerful and less accessible at the same time: as dashboards multiplied, data teams became bottlenecks. A new generation of tools (analytics chatbots, AI-assisted dashboards, and modern data platforms) are changing that with what we call Next-Gen BI.
Agilytic helps Belgian organizations adopt these capabilities honestly, starting from the questions that matter and building what actually gets used.
Why we wanted to address Next-Gen BI
Most of the Belgian executives we speak to have data. But they are short of answers.
A business question that should take thirty seconds like "what drove the drop in gross margin last month?" takes days:
two days to reach a data team,
two more days to get back into a report…
and by then the decision has already been made, on instinct.
It is a design problem, not an analytics one. And it has a solution!
Over the past two years, three converging capabilities have matured to the point where they are practical for mid-market Belgian businesses, not just global enterprises with hundred-person data teams. This page explains what they are, and how to know whether your organization is ready.
What has changed in BI, and why it matters now
Business intelligence as most organizations practice it today was designed for a world of weekly reporting cycles and centralized analytics teams. Business users submit requests, data teams build dashboards… and by the time the output lands, the question has evolved.
The good news is that three things have recently shifted:
Language models can now reliably translate business questions into data queries
Asking "what is our revenue by region this quarter compared to last year?" and getting a correct, sourced answer (without writing SQL) can now be an actual production capability. It’s available in tools like Databricks Genie, Snowflake Cortex Analyst, and Microsoft Fabric Data Agent.
AI can now accelerate the entire dashboard development cycle
What used to take weeks, from business brief to delivered, iterated-upon Power BI report, now takes days in our experience.
AI handles the repetitive parts:
Translating requirements into new measures
Generating initial visuals
Fine-tuning the visual identity of the dashboard
The human work shifts from production to judgement.
The data platform has become the prerequisite
Analytics chatbots and AI-assisted dashboards only work reliably if the data underneath is clean, governed, and trusted. The good news is that modern platforms (Fabric, Databricks, Snowflake) make that foundation genuinely achievable for organizations that previously could not staff for it.
Fear not: none of these capabilities require a transformation programme. They can be introduced incrementally, starting from where your organization actually is.
Three capabilities, one shift: from reporting to answering
Analytics chatbots: ask your data, get the answer
An analytics chatbot connects your business users directly to your data through a conversational interface. Instead of waiting for a report, a user types a question in plain English (or any other language) and gets a sourced, accurate answer, often in seconds.
The real value lies in the operational changes made possible when business teams stop waiting:
Fewer ad hoc requests to the data team
Faster data-driven decisions
A shared "version of the truth" across departments, that stop arguing about which spreadsheet is current
A word of warning, though: it only works if the data underneath is trustworthy. A chatbot built on inconsistent, ungoverned data will confidently give wrong answers. And that’s way worse than a slow report…
So before recommending a chatbot to any client, we assess the data quality and governance layer first. Sometimes the right recommendation for Next-Gen BI implementation is actually to fix that layer before adding the conversational interface on top.
Tools we work with:
Databricks Genie
Snowflake Cortex Analyst
Microsoft Fabric Data Agent
Custom builds for organizations with specific security or integration requirements
AI-assisted dashboards: deliver insights faster
The traditional Power BI delivery cycle has a familiar shape:
Discovery workshops
Data modeling
Visual design
Feedback rounds & revision
Sign-off
This usually amounts to four to six weeks for a moderately complex report, longer if stakeholders keep changing the brief.
AI changes two parts of that cycle:
It compresses the translation step, taking business requirements and generating initial measures and visual layout that is close enough to be reacted to immediately, rather than imagined from a blank page.
It makes iteration fast enough that "can we try it another way?" becomes a thirty-minute conversation instead of a two-week delay.
In our experience, a report that would have taken two weeks now takes three days. It is what we observed across engagements where we applied AI-assisted development workflows using Claude Code. The work that remains (stakeholder alignment, data logic validation, storytelling, governance…) is the work that actually requires human expertise.
The result, beyond speed: dashboards are more likely to be used, because the iteration cycle was short enough to genuinely align with what users need.
Data platform: the foundation that makes it all reliable
Every organization that adopts analytics chatbots or AI-assisted dashboards eventually arrives at the same question: "Can we trust what the system is telling us?" The answer depends almost entirely on the data platform underneath.
A modern data platform like Microsoft Fabric, Databricks, or Snowflake provides three things that traditional BI stacks often lack:
A single place where data is stored and governed
Clear ownership of who defines what a metric means
The scalability to support more users and use cases without constant firefighting.
For smaller and mid-sized organizations, the practical question is: "which platform fits our stack, our team, and our budget?" There is no universal answer. We have helped clients choose Fabric because they were already Microsoft-invested and had a small data team. We have helped others choose Databricks because their data volumes and ML use cases demanded it. And we have told some clients that they do not yet need a new platform at all; that a better-governed use of what they already have will serve them well for two more years.
Is your organization ready for Next-Gen BI?
The most common mistake we see is adopting one of these capabilities before the prerequisites are in place. Some honest questions to ask before you start:
On analytics chatbots
Do your key metrics have agreed definitions across teams? If "revenue" means three different things in three departments, a chatbot will faithfully surface all three. That is not helpful.
On AI-assisted dashboards
Is there a clear owner for the dashboards you already have? If not, AI will help you build new ones faster. But the underlying problem of dashboards nobody maintains will compound.
On data platforms
Do you have at least one person internally who owns the data architecture, even part-time? A modern platform without an internal owner tends to drift into the same fragmentation it was meant to replace.
If you answer no to any of these, that is not a reason to stop. But it is a reason to start there, with support, before adding capability on top.
How Agilytic helps
Agilytic's approach to Next-Gen BI follows three phases, adapted to where you are in the journey.
Phase 1: BI readiness assessment (1-2 weeks)
We review your current data landscape (quality, governance, tooling, team) and identify which of the capabilities is most valuable and most achievable for you right now. The output is a prioritized recommendation with no vendor pitch.
Phase 2: focused implementation (3-15 weeks)
We build the first chatbot, the first AI-assisted dashboard cluster, and/or the first data platform module alongside your team. We work iteratively so you see working output asap.
Phase 3: adoption and scale
The technology is the easy part; getting business teams to change how they get answers is the harder part. We stay involved through training, feedback cycles, and governance design because a tool that nobody uses is not a success.
We don’t replace your data team. Our role is to give them leverage, and to give business users the autonomy they have been asking for.
Why Agilytic
Agilytic has been a Belgian data and AI consultancy since 2016, with more than 400 projects delivered across finance, retail, healthcare, logistics, real estate, and the public sector. We are independent: no vendor partnerships, no reseller margins, no incentive to recommend one platform over another.
Our Managing Partner, Julien Theys, is Professor of Data Science at Solvay Brussels School and IHECS, and advises regularly on data strategy at board and executive level. That combination of academic rigor and hands-on delivery is unusual in this market, and it is what allows us to be useful in a boardroom and in a data engineering sprint in the same week.
We also know the Belgian context. The data maturity curve here, the typical team sizes, the regulatory environment, and the realistic pace of change all differ from what global vendor content describes. We translate what is possible into what is practical, for your organization, in Belgium, now.
Questions we get asked about Next-Generation business intelligence
Do we need to replace our entire data stack to benefit from AI in BI?
No. In most cases, a chatbot or AI-assisted dashboard can be introduced on top of what you already have, if the data quality is sufficient. The platform question becomes important when you want to scale beyond a first use case.
How do analytics chatbots differ from Power BI Copilot?
Power BI Copilot helps you create and interact with dashboards inside Power BI. Analytics chatbots connect to the underlying data directly, often without going through a fixed dashboard at all. They serve different user needs: Copilot is for analysts refining reports, while a chatbot is for business users needing quick answers.
Is AI going to replace our data analysts?
No, but the work changes. AI handles the repetitive production tasks, while the human expertise that remains is more valuable: knowing what question to ask, validating whether the answer makes sense, and turning a correct number into a decision. Organizations that adopt well tend to free their analysts for higher-value work, not reduce headcount.
How long before we see results?
For an AI-assisted dashboard project on a clean data layer: three to five days for a first working version
For an analytics chatbot on a clean data layer: two to four weeks.
For a data platform foundation: eight to twenty weeks depending on scope
The fastest wins come from organizations that have already invested in data quality, because they are ready to layer AI on top immediately.
Talk to us about next-generation business intelligence
If you are wondering whether your organization is ready for analytics chatbots, AI-assisted dashboards, or a modern data platform, the most useful starting point is a short conversation. Thirty minutes, no slides, no obligation. We will give you an honest read on where you are and what the realistic next step looks like.
Why we wanted to address Next-Gen BI
Most of the Belgian executives we speak to have data. But they are short of answers.
A business question that should take thirty seconds like "what drove the drop in gross margin last month?" takes days:
two days to reach a data team,
two more days to get back into a report…
and by then the decision has already been made, on instinct.
It is a design problem, not an analytics one. And it has a solution!
Over the past two years, three converging capabilities have matured to the point where they are practical for mid-market Belgian businesses, not just global enterprises with hundred-person data teams. This page explains what they are, and how to know whether your organization is ready.
What has changed in BI, and why it matters now
Business intelligence as most organizations practice it today was designed for a world of weekly reporting cycles and centralized analytics teams. Business users submit requests, data teams build dashboards… and by the time the output lands, the question has evolved.
The good news is that three things have recently shifted:
Language models can now reliably translate business questions into data queries
Asking "what is our revenue by region this quarter compared to last year?" and getting a correct, sourced answer (without writing SQL) can now be an actual production capability. It’s available in tools like Databricks Genie, Snowflake Cortex Analyst, and Microsoft Fabric Data Agent.
AI can now accelerate the entire dashboard development cycle
What used to take weeks, from business brief to delivered, iterated-upon Power BI report, now takes days in our experience.
AI handles the repetitive parts:
Translating requirements into new measures
Generating initial visuals
Fine-tuning the visual identity of the dashboard
The human work shifts from production to judgement.
The data platform has become the prerequisite
Analytics chatbots and AI-assisted dashboards only work reliably if the data underneath is clean, governed, and trusted. The good news is that modern platforms (Fabric, Databricks, Snowflake) make that foundation genuinely achievable for organizations that previously could not staff for it.
Fear not: none of these capabilities require a transformation programme. They can be introduced incrementally, starting from where your organization actually is.
Three capabilities, one shift: from reporting to answering
Analytics chatbots: ask your data, get the answer
An analytics chatbot connects your business users directly to your data through a conversational interface. Instead of waiting for a report, a user types a question in plain English (or any other language) and gets a sourced, accurate answer, often in seconds.
The real value lies in the operational changes made possible when business teams stop waiting:
Fewer ad hoc requests to the data team
Faster data-driven decisions
A shared "version of the truth" across departments, that stop arguing about which spreadsheet is current
A word of warning, though: it only works if the data underneath is trustworthy. A chatbot built on inconsistent, ungoverned data will confidently give wrong answers. And that’s way worse than a slow report…
So before recommending a chatbot to any client, we assess the data quality and governance layer first. Sometimes the right recommendation for Next-Gen BI implementation is actually to fix that layer before adding the conversational interface on top.
Tools we work with:
Databricks Genie
Snowflake Cortex Analyst
Microsoft Fabric Data Agent
Custom builds for organizations with specific security or integration requirements
AI-assisted dashboards: deliver insights faster
The traditional Power BI delivery cycle has a familiar shape:
Discovery workshops
Data modeling
Visual design
Feedback rounds & revision
Sign-off
This usually amounts to four to six weeks for a moderately complex report, longer if stakeholders keep changing the brief.
AI changes two parts of that cycle:
It compresses the translation step, taking business requirements and generating initial measures and visual layout that is close enough to be reacted to immediately, rather than imagined from a blank page.
It makes iteration fast enough that "can we try it another way?" becomes a thirty-minute conversation instead of a two-week delay.
In our experience, a report that would have taken two weeks now takes three days. It is what we observed across engagements where we applied AI-assisted development workflows using Claude Code. The work that remains (stakeholder alignment, data logic validation, storytelling, governance…) is the work that actually requires human expertise.
The result, beyond speed: dashboards are more likely to be used, because the iteration cycle was short enough to genuinely align with what users need.
Data platform: the foundation that makes it all reliable
Every organization that adopts analytics chatbots or AI-assisted dashboards eventually arrives at the same question: "Can we trust what the system is telling us?" The answer depends almost entirely on the data platform underneath.
A modern data platform like Microsoft Fabric, Databricks, or Snowflake provides three things that traditional BI stacks often lack:
A single place where data is stored and governed
Clear ownership of who defines what a metric means
The scalability to support more users and use cases without constant firefighting.
For smaller and mid-sized organizations, the practical question is: "which platform fits our stack, our team, and our budget?" There is no universal answer. We have helped clients choose Fabric because they were already Microsoft-invested and had a small data team. We have helped others choose Databricks because their data volumes and ML use cases demanded it. And we have told some clients that they do not yet need a new platform at all; that a better-governed use of what they already have will serve them well for two more years.
Is your organization ready for Next-Gen BI?
The most common mistake we see is adopting one of these capabilities before the prerequisites are in place. Some honest questions to ask before you start:
On analytics chatbots
Do your key metrics have agreed definitions across teams? If "revenue" means three different things in three departments, a chatbot will faithfully surface all three. That is not helpful.
On AI-assisted dashboards
Is there a clear owner for the dashboards you already have? If not, AI will help you build new ones faster. But the underlying problem of dashboards nobody maintains will compound.
On data platforms
Do you have at least one person internally who owns the data architecture, even part-time? A modern platform without an internal owner tends to drift into the same fragmentation it was meant to replace.
If you answer no to any of these, that is not a reason to stop. But it is a reason to start there, with support, before adding capability on top.
How Agilytic helps
Agilytic's approach to Next-Gen BI follows three phases, adapted to where you are in the journey.
Phase 1: BI readiness assessment (1-2 weeks)
We review your current data landscape (quality, governance, tooling, team) and identify which of the capabilities is most valuable and most achievable for you right now. The output is a prioritized recommendation with no vendor pitch.
Phase 2: focused implementation (3-15 weeks)
We build the first chatbot, the first AI-assisted dashboard cluster, and/or the first data platform module alongside your team. We work iteratively so you see working output asap.
Phase 3: adoption and scale
The technology is the easy part; getting business teams to change how they get answers is the harder part. We stay involved through training, feedback cycles, and governance design because a tool that nobody uses is not a success.
We don’t replace your data team. Our role is to give them leverage, and to give business users the autonomy they have been asking for.
Why Agilytic
Agilytic has been a Belgian data and AI consultancy since 2016, with more than 400 projects delivered across finance, retail, healthcare, logistics, real estate, and the public sector. We are independent: no vendor partnerships, no reseller margins, no incentive to recommend one platform over another.
Our Managing Partner, Julien Theys, is Professor of Data Science at Solvay Brussels School and IHECS, and advises regularly on data strategy at board and executive level. That combination of academic rigor and hands-on delivery is unusual in this market, and it is what allows us to be useful in a boardroom and in a data engineering sprint in the same week.
We also know the Belgian context. The data maturity curve here, the typical team sizes, the regulatory environment, and the realistic pace of change all differ from what global vendor content describes. We translate what is possible into what is practical, for your organization, in Belgium, now.
Questions we get asked about Next-Generation business intelligence
Do we need to replace our entire data stack to benefit from AI in BI?
No. In most cases, a chatbot or AI-assisted dashboard can be introduced on top of what you already have, if the data quality is sufficient. The platform question becomes important when you want to scale beyond a first use case.
How do analytics chatbots differ from Power BI Copilot?
Power BI Copilot helps you create and interact with dashboards inside Power BI. Analytics chatbots connect to the underlying data directly, often without going through a fixed dashboard at all. They serve different user needs: Copilot is for analysts refining reports, while a chatbot is for business users needing quick answers.
Is AI going to replace our data analysts?
No, but the work changes. AI handles the repetitive production tasks, while the human expertise that remains is more valuable: knowing what question to ask, validating whether the answer makes sense, and turning a correct number into a decision. Organizations that adopt well tend to free their analysts for higher-value work, not reduce headcount.
How long before we see results?
For an AI-assisted dashboard project on a clean data layer: three to five days for a first working version
For an analytics chatbot on a clean data layer: two to four weeks.
For a data platform foundation: eight to twenty weeks depending on scope
The fastest wins come from organizations that have already invested in data quality, because they are ready to layer AI on top immediately.
Talk to us about next-generation business intelligence
If you are wondering whether your organization is ready for analytics chatbots, AI-assisted dashboards, or a modern data platform, the most useful starting point is a short conversation. Thirty minutes, no slides, no obligation. We will give you an honest read on where you are and what the realistic next step looks like.
Ready to reach your goals with data?
If you want to reach your goals through the smarter use of data and A.I., you're in the right place.
Ready to reach your goals with data?
If you want to reach your goals through the smarter use of data and A.I., you're in the right place.
Ready to reach your goals with data?
If you want to reach your goals through the smarter use of data and A.I., you're in the right place.
Ready to reach your goals with data?
If you want to reach your goals through the smarter use of data and A.I., you're in the right place.