At first glance, Ask Ada charts may seem like just another dashboarding capability. In reality, they solve different problems. Rather than replacing traditional dashboards, they complement them. Understanding when to use Ask Ada charts and when a dedicated business intelligence tool remains the better choice will help you get the most value from both approaches. 

What are Ask Ada charts? 

Ask Ada allows users to ask questions about their process data using natural language. Instead of navigating reports, applying filters, or designing charts manually, users can simply ask a question and receive an answer. 

With Ask Ada charts, those answers can now be visualized. The system automatically generates charts based on the information requested, helping users understand patterns and trends more quickly. 

Imagine a manager asking: 

"Which regions generated the highest sales during the last quarter?" 

Instead of receiving only a text response, Ask Ada can generate a chart that immediately highlights the answer. 

This creates a more accessible experience for business users who may not be familiar with reporting tools or dashboard design. 

The biggest advantage: speed 

One of the greatest strengths of Ask Ada charts is how quickly insights can be generated. 

Traditional dashboards often require planning, report design, chart configuration, filter selection, and ongoing maintenance. Before a user can answer a question, someone typically needs to build the dashboard. 

Ask Ada works differently. The user starts with a question and receives an answer immediately. 

This is particularly useful when exploring data or investigating unexpected situations. The user does not need to know whether a report already exists. They simply ask. 

For example, a process owner might wonder why approval times increased during the previous month. Instead of requesting a new report, they can ask Ask Ada directly and visualize the results within seconds. 

When Ask Ada charts are the right choice 

Ask Ada charts are ideal when users need quick answers to changing business questions. 

Many organizations face situations where decision makers continuously ask new questions that were not anticipated when dashboards were designed. Building a new dashboard for every question is inefficient and often slows down analysis. 

This is where Ask Ada shines. 

It is particularly valuable for managers, team leaders, and business users who want to investigate process performance without depending on technical teams or reporting specialists. 

Ask Ada charts are also a great fit for exploratory analysis. Users can start with a question, review the answer, ask a follow-up question, and continue refining their understanding of the data. The experience feels much more conversational than traditional reporting. 

Another advantage is accessibility. Employees who are unfamiliar with complex reporting tools can still obtain meaningful insights simply by describing what they want to know. 

When traditional dashboards are still the better option 

Although Ask Ada charts provide tremendous flexibility, they are not designed to replace every reporting scenario. 

Traditional dashboards remain the preferred choice when organizations need highly structured, standardized reporting. 

For example, executive dashboards that are reviewed every week or month often require carefully designed layouts, consistent metrics, and precise formatting. These dashboards are intended to provide the same view every time so that performance can be monitored consistently. 

Business intelligence platforms also continue to offer advantages when working with very large datasets, advanced calculations, complex data models, or cross-system reporting. Organizations frequently use these tools to combine information from multiple applications and create enterprise-wide analytics solutions. 

If a dashboard is considered a critical business asset and must be shared across large audiences with strict governance controls, a dedicated BI solution may still be the best approach. 

The best approach is often both 

The most effective organizations will not choose between Ask Ada charts and traditional dashboards. They will use both. 

Think of traditional dashboards as your organization's official scorecards. They provide standardized metrics that everyone trusts and reviews on a regular basis. 

Think of Ask Ada charts as your exploration tool. They help users investigate, ask follow-up questions, and discover insights that may not yet exist in a formal dashboard. 

A manager might begin by reviewing a monthly operations dashboard. After noticing an unexpected increase in processing times, they could use Ask Ada to investigate further, generating additional charts and asking questions that were never included in the original report design. 

Together, these capabilities create a more complete analytics experience. 

A practical recommendation 

If your users frequently request new reports, ask ad hoc questions, or depend on analysts to build simple visualizations, Ask Ada charts can dramatically reduce the time required to obtain insights. 

At the same time, continue using traditional dashboards for executive reporting, governance, regulatory requirements, and highly structured analytics. 

The goal is not to replace existing reporting investments. Instead, it is to empower users with a faster and more intuitive way to explore their data. 

Ask Ada charts and traditional dashboards serve different purposes. Ask Ada excels at answering questions quickly, supporting data exploration, and helping business users uncover insights through natural language. Traditional dashboards remain the best choice for standardized reporting, enterprise analytics, and long-term performance monitoring. 

By understanding the strengths of each approach, organizations can create a reporting strategy that combines the flexibility of conversational AI with the reliability of well-established business intelligence practices.

At first glance, selecting a model may seem like a technical detail. In practice, however, the model you choose can influence how quickly an agent responds, how deeply it analyzes information, and how many AI resources it consumes. Understanding the difference between reasoning and non-reasoning models will help you design agents that are both effective and cost-efficient. 

Understanding reasoning and non-reasoning models 

Not all AI models work in the same way. Some are designed to provide answers quickly based on patterns, while others spend more time analyzing information before responding. 

Non-reasoning models are optimized for direct tasks. They shine at pattern recognition, summarization, content generation, classification, and other scenarios where the answer can be produced without a deep analytical process. These models are typically faster and consume fewer resources, making them a great choice for routine tasks. 

Reasoning models, on the other hand, are designed to spend additional effort evaluating information before generating a response. Rather than immediately producing an answer, they perform a more deliberate analysis of the request. This makes them useful for situations that involve multiple variables, complex decision-making, comparisons, or detailed analysis. 

Think of it this way. If you ask an agent to summarize a document, a non-reasoning model will likely be the most efficient option. If you ask an agent to evaluate several supplier proposals, compare them against multiple criteria, and justify a recommendation, a reasoning model will typically deliver better results. 

Understanding reasoning levels 

For reasoning models, Bizagi introduces reasoning levels that allow you to control how much analytical effort the model should apply to a task. 

The available options are None, Low, Medium, and High. 

Each level influences how deeply the model evaluates information before responding. 

When to use None 

The None level is appropriate when you want the reasoning model to provide a direct answer without engaging in significant analysis. This setting works well for straightforward tasks such as formatting content, rewriting text, extracting information from a structured source, or producing basic summaries. 

For example, if an agent receives a customer comment and needs to classify it into a category, a None reasoning level may be perfectly sufficient. Because the model performs minimal reasoning, responses are generally faster and less resource intensive. 

When to use Low 

The Low level introduces a moderate amount of analysis while maintaining good response times. This option is useful when the agent needs to evaluate simple business rules or perform light comparisons. 

Imagine an agent that reviews several support tickets and suggests priority levels based on predefined criteria. The task requires some thinking, but not extensive analysis. In this case, Low reasoning often provides the right balance between performance and quality. 

When to use Medium 

Medium is likely to become the default choice for many business scenarios. It provides a balanced approach that combines analytical reasoning with reasonable performance. 

Consider an agent that reviews an employee request, analyzes supporting information, checks policy requirements, and recommends an approval decision. The task involves multiple variables and requires contextual understanding. Medium reasoning gives the model enough analytical capability without introducing unnecessary complexity. 

When to use High 

High reasoning should be reserved for situations where deeper analysis creates measurable value. 

Examples include complex financial evaluations, multi-criteria supplier assessments, technical investigations, risk analysis, or mathematical calculations. 

Imagine an agent responsible for evaluating several vendor proposals. The agent must compare pricing, technical capabilities, service quality, implementation timelines, and previous performance. In this situation, a High reasoning level allows the model to spend more effort analyzing the information and producing a more robust recommendation. 

Start simple and test 

A common mistake is assuming that more reasoning automatically produces better results. In reality, the best configuration depends on the specific use case. 

The best approach is to test different configurations using the Test prompt feature in Bizagi Studio. Try the same prompt with different models and reasoning levels. Compare the responses, review their quality, and evaluate whether the additional reasoning improves the result. 

You may discover that a Medium reasoning level provides the same business value as High reasoning while consuming fewer resources. Or you may find that a non-reasoning model is perfectly adequate for a task that originally seemed more complex. 

During this session, we will highlight key innovations introduced in this release: 

• Next-Generation AI Agents: Take advantage of new AI models, advanced reasoning capabilities, and expanded execution options that make AI Agents smarter, faster, and better equipped to handle complex business scenarios with greater precision and flexibility. 

• AI Monitoring and Consumption Analytics: Gain complete visibility into AI adoption, performance, dependencies, and consumption across your organization. With centralized monitoring, AI metrics, and consumption reporting, leaders can confidently scale AI while maintaining control of usage and investment. 

• Improvements in Real-Time Event Streaming with Kafka Integration: Connect Bizagi to your enterprise ecosystem through Apache Kafka, enabling real-time event-driven architectures. Automatically react to business events, trigger processes, create cases or records, and streamline integrations across systems with greater flexibility, scalability, and agility. 

Don’t miss this opportunity to discover how Bizagi Summer 2026 empowers organizations to move beyond experimentation and operationalize AI at scale—focusing intelligence where it creates the greatest business value. 

Register now! 

During the session, we will discuss specific examples of AI Agents across four key categories: 

• Case Management: Discover how AI Agents can support case management in business scenarios such as Service Requests, Incident Management, or Complaint Management, improving operational efficiency and the user experience. 

• Regulatory and Compliance: Explore how AI Agents can support regulatory and compliance processes by reviewing documents and policies, identifying risks, validating information, and generating findings to facilitate decision-making. 

• Unstructured Data Insights: Learn how AI Agents can extract, interpret, classify, validate, and summarize information from documents, emails, tables, and attachments, turning unstructured data into actionable information to improve decision-making and trigger the next steps within business processes. 

• Content Generation: Discover how AI Agents can use historical information within Bizagi to generate or suggest responses, helping your teams reduce manual work and maintain consistency in the information generated. 

Don’t miss this opportunity to learn how Bizagi AI Agents can be applied to real-world scenarios to transform the way organizations improve their business solutions. Join us to discover how these use cases can help accelerate AI adoption and move toward smarter, more practical, and value-driven automation. 

Register now! 

Vibe coding focuses on quickly generating pieces of software like screens, code, or workflows from prompts. 

Bizagi operates at the solution level. Instead of producing isolated components, it captures business intent and turns it into a governed, executable enterprise solution that runs directly on the Bizagi platform, enriched with built-in AI capabilities such as AI Agents, Ask Ada, AI Workers, and Enterprise Knowledge. 

A useful analogy is this: assembling a desk from a predesigned kit doesn’t make you a carpenter. You do end up with a desk, but not one designed for long-term durability. In the same way, generating components doesn’t create an enterprise solution. Bizagi is designed to deliver complete, resilient business solutions and not just individual parts. 

Bizagi scope 

Bizagi enables organizations to transform what the business wants to achieve into a working enterprise solution. Instead of asking users to design technical components or write code, Bizagi allows them to define business intent: how work should flow, how decisions are made, who participates, and how the system behaves. 

That intent is executed directly within the Bizagi platform, which combines process orchestration, Case and event-driven orchestration, embedded AI capabilities (such as AI Agents for autonomous or assisted task execution, Ask Ada for contextual, conversational interaction with processes and data, AI Workers for task automation at scale and Enterprise Knowledge to ground AI decisions in trusted business information). 

The outcome is not code, but a running enterprise solution with governance, traceability, and operational control built in by design.  

This approach differs from vibe coding tools, which generate components that must later be assembled, deployed, secured, and operated. Bizagi operates at the level of business solutions and orchestration, while vibe coding operates at the level of code artifacts and isolated experiences. 

Fundamental difference: code generation vs. solution execution 

The main difference between vibe coding and Bizagi is what is being created. 

Vibe coding focuses on generating code from prompts. AI produces interfaces, services, or workflows quickly, but these remain technical pieces. Business behavior emerges later from how those pieces are assembled and maintained. 

Bizagi focuses on executing business solutions, where processes, rules, data, and AI-driven decisions are explicitly defined and governed. 

With Bizagi, business behavior is not hidden in code: it is modeled, visible, and enforced at runtime. AI is embedded within this model, not layered on top of disconnected components. 

Deployment and operational responsibility 

In a vibe coding approach, generation is only the beginning. What follows is a series of responsibilities that fall entirely on the customer. Once components are created, teams must handle deployment, configure infrastructure and environments, secure the system, and ensure proper monitoring and observability. The initial speed of generation quickly gives way to the ongoing effort of making those components reliable, secure, and operational in real conditions. 

Bizagi takes a fundamentally different approach. Solutions are not generated as artifacts that need to be deployed; they are executed directly on an enterprise-ready platform. This means that organizations do not need to assemble or maintain the underlying infrastructure to run their solutions. 

This consistency also extends to AI capabilities. Features such as AI Agents and AI Workers are not external services that require separate deployment or lifecycle management. They operate within the same governed runtime as processes, rules, and data, ensuring that everything runs as part of a unified system rather than a collection of independent components. 

Security: configuration vs. platform capability 

In vibe coding, security is constructed. Teams must explicitly define authentication, authorization, secret management, and auditing mechanisms. Each decision introduces potential risk, and any oversight can create vulnerabilities that may only become visible in production. 

In Bizagi, security is not something that needs to be assembled. It is a native capability of the platform, built into how solutions are defined and executed. Access to data, roles, responsibilities, and actions are consistently enforced at runtime, without requiring teams to design security from scratch. 

This unified model also governs AI: AI Agents operate strictly within defined roles and permissions, ensuring they act only within authorized boundaries. Enterprise Knowledge guarantees that AI relies on controlled, approved data sources. Ask Ada interactions are equally governed, responding based on user access rights and the context of the process. 

The result is a secure model that is centralized, consistent, and aligned with the business, rather than dependent on fragmented technical implementations. 

Evolving the solution 

Enterprise systems are not static; they evolve continuously as business needs change. In Bizagi, this evolution is controlled and predictable. Processes, rules, and AI behaviors can be versioned in a way that preserves continuity. Running cases continue under their original definitions, while new cases adopt updated versions, ensuring that change does not disrupt ongoing operations. 

AI-driven behaviors remain tied to governed logic and approved knowledge sources, which means they evolve in alignment with the system rather than independently. 

In vibe coding, evolution is handled at a purely technical level. Changes require redeployment, may involve data migrations, and often introduce operational risk. The system itself does not inherently recognize the concept of a “running process” versus a “new one,” making it harder to implement change without unintended consequences. 

Final thoughts 

At its core, the distinction between vibe coding and Bizagi comes down to what is being delivered. Vibe coding produces components: pieces that can eventually be assembled into a solution if enough effort is invested in integrating and managing them. 

Bizagi delivers enterprise solutions by design. Process orchestration, data consistency, governance, and embedded AI capabilities are combined into a single, unified system. The outcome is not a set of applications, but a cohesive operational model that the business can rely on. 

Rather than viewing these approaches as mutually exclusive, organizations can combine them strategically. Bizagi can serve as the enterprise orchestration backbone, providing governance, consistency, and integrated AI capabilities, while vibe coding can be used to create highly customized user interfaces when needed. 

This approach is particularly valuable when there is a strong requirement for complete control over the user experience. In those cases, vibe coding brings flexibility and speed, while Bizagi ensures correctness, traceability, and operational stability. 

For details on creating and using MCP, see: MCP Servers. 

What Is an MCP Server 

An MCP Server exposes services called tools. Each tool performs a specific action, such as calculating a weighted average, generating a chart, or scheduling a meeting in Google Calendar. What makes MCP unique is that these tools are described in natural language, so the AI Agent can understand what each tool does and decide which one to use based on the prompt. 

This approach is similar in spirit to APIs, but MCP is designed specifically for AI. It allows the agent to read the descriptions of available tools, understand the input parameters, and execute the right action without complex coding. 

Why MCP Servers Matter 

By connecting to MCP Servers, Bizagi AI Agents can extend their capabilities beyond Bizagi’s data. For example, an agent can analyze supplier evaluations stored in Bizagi, then use an MCP tool to generate a radar chart for visual comparison. Or it can schedule a meeting in Google Calendar after selecting the top suppliers for an RFP. These integrations make AI-driven processes more dynamic and practical. 

Examples of MCP in Action 

Imagine you have an agent that evaluates suppliers for a procurement process. After analyzing historical data using Bizagi Data Knowledge, the agent needs to present the results visually. By connecting to an MCP Server that offers a “Generate Radar Chart” tool, the agent can create a chart and return the URL to display it in the process form. 

Another example is scheduling. If your agent selects the top three suppliers for an RFP, it can use an MCP tool to create a meeting in Google Calendar automatically, saving time and reducing manual work. 

Best Practices 

When using MCP Servers, always review the tool descriptions and input parameters to ensure they match your process needs. Test the connection in Bizagi Studio before deploying, and use clear prompts so the agent understands what action to perform. If you want to see how the agent interacts with MCP tools, use the Test Prompt option in Studio to validate the behavior before going live. 

Final thoughts 

MCP Servers open a new world of possibilities for Bizagi AI Agents. They allow your agents to interact with external systems, perform calculations, generate charts, and even schedule meetings—all through natural language instructions. By configuring MCP Servers and testing them in Bizagi Studio, you can make your AI-powered processes smarter, faster, and more connected than ever.

For many Bizagi customers, the starting point of their AI adoption journey is still unclear: where can we begin to generate real, measurable value with Bizagi?

This article is the first in a new series titled “Where to start your AI Adoption journey with Bizagi?”. Throughout this series, we will explore how Bizagi’s AI capabilities—Ask Ada, AI Workers, and AI Agents—can be adopted progressively, responsibly, and with clear business outcomes.

We begin with Ask Ada, Bizagi’s conversational AI experience, and the fastest way to unlock the value of your business data.

Ask Ada: An Early Win on Your AI Journey

Ask Ada is designed to give business users instant access to insights hidden in their processes and data—without technical barriers, custom reports, or constant dependency on IT. That is why Ask Ada represents a natural first step in Bizagi’s AI adoption journey.

Ask Ada enables organizations to build confidence with AI through everyday questions, real operational answers, and visible ROI. Most importantly, it allows you to start small while involving the right people and processes from day one.

A successful Ask Ada rollout typically evolves in three waves.

First Wave: Prove Value Quickly with the Right Process

The goal of the first wave is simple: deliver a visible and credible win.

Start with:

• One business process that already has clean, well‑governed data.

• Minimum viable Data Domains, clearly defined and easy to understand.

• A focused group of users with frequent information needs.

The ideal roles to enable early include:

• Operations Analysts
  These users constantly query process data. Ask Ada can replace dozens of weekly IT requests by allowing them to ask questions directly and get immediate answers. This group often shows the fastest ROI.

• Department Managers
  Managers need real‑time visibility into process status without waiting for scheduled reports. With Ask Ada, they gain autonomy to check performance, bottlenecks, and workload on demand—before critical meetings or decisions.

This first wave is about speed, clarity, and confidence. One well‑chosen process can do more for adoption than an ambitious, multi‑process rollout that no one actively uses.

Second Wave: Expand Reach and Context

Once Ask Ada has proven its value, the second wave focuses on scaling impact.

At this stage, organizations typically:

• Expand Ask Ada to three to five additional processes.

• Activate the Knowledge Base, integrating internal documents such as policies, procedures, and guidelines.

• Refine Data Domains based on real user feedback.

New roles become especially relevant:

• Compliance and Risk Teams
  These teams need fast access to regulatory information, internal policies, and historical cases. By combining process data with curated Knowledge Base content, Ask Ada becomes a trusted assistant for informed and compliant decision‑making.

• Customer Service and CX Teams
  Customer‑facing teams frequently search for case status and customer history. Ask Ada reduces time spent navigating systems, accelerates responses, and minimizes escalations—all while keeping answers consistent and accurate.

This wave transforms Ask Ada from a reporting alternative into a daily operational assistant across multiple teams.

Third Wave: Executive Decisions Without Intermediaries

In the third wave, Ask Ada becomes part of strategic and executive workflows.

Key activities include:

• Integrating Ask Ada into executive decision flows.

• Continuously refining Data Domains together with business users.

• Elevating Ask Ada from insight discovery to decision enablement.

The primary audience here is the C‑suite and senior leadership. Executives no longer need technical training or complex dashboards. Instead, they leverage Ask Ada‑driven queries to fuel executive dashboards and decision moments—with clarity, speed, and confidence.

The value is simple but powerful: visibility without intermediaries, especially when decisions matter most.

Three Non‑Negotiable Principles for Success

Across all three waves, organizations that succeed with Ask Ada consistently apply three principles:

1. Start with a “photogenic” process
   The pilot does not have to be the most critical process—it must be the one with clean data, a clear domain, and users who will immediately see value. A fast, visible win beats a perfect implementation that no one adopts.

2. Champions are your most important asset
   Champions are not random early adopters. They are credible voices within their teams, capable of communicating value and influencing decision‑makers. Investing in their time, recognition, and feedback is what turns a successful pilot into organic expansion.

3. Data governance is a prerequisite, not a consequence
   Ask Ada is only as strong as the Data Domains that power it. Inconsistent data or poorly defined access will create distrust from day one. That is why data quality should be audited before enabling Ask Ada—not after.

Measuring ROI from Day One

One of the advantages of starting your AI journey with Ask Ada is that ROI can be measured early and clearly, and organizations can track tangible impact across three key dimensions:

Recovered Time — Business Users

Ask Ada gives business users direct access to answers that previously required manual analysis, report requests, or system navigation. Operations analysts, managers, and customer‑facing teams recover hours every week by asking questions in natural language and getting immediate, trusted responses.

IT Capacity Released

By reducing ad‑hoc reporting requests and data queries, Ask Ada frees IT teams from repetitive tasks. This released capacity can be reinvested in higher‑value initiatives such as platform optimization, governance, and advanced automation—without increasing headcount.

Faster, Better Decisions

Ask Ada accelerates decision‑making by removing intermediaries between data and decision‑makers. Whether it’s a manager preparing for a meeting or an executive evaluating a critical situation, real‑time insights lead to quicker, more confident decisions with measurable business impact.

Together, these three metrics form a practical and defensible ROI model that resonates with both operational teams and leadership.

Start Smart. Start with Ask Ada.

Your AI adoption journey does not need to start with disruption or complexity. With Ask Ada, you can begin by empowering users, building trust in AI, and maximizing the value of the data you already have.

In the next articles of this series, we will explore how AI Workers and AI Agents extend this foundation into execution and autonomy.