The primary barrier to widespread AI adoption is not a lack of awareness, but rather the disconnect between conceptual understanding of AI and the practical ability to build with it. Many professionals, whose main roles do not involve writing code, are increasingly reliant on AI solutions for their daily work. However, most have never had the hands-on experience necessary to build the tools they discuss, leading to slower adoption, delayed productivity gains, and missed opportunities for identifying new use cases.
Empowering Business Professionals to Build AI
To address this gap, structured programs are emerging that enable business professionals to develop working AI prototypes. One such program involved four customer-facing professionals, none with engineering backgrounds, who successfully built a multi-agent AI financial advisory tool called WealthWise in just six weeks. This prototype included five specialized AI agents for tasks like portfolio analysis and risk assessment, a dual-server architecture using Node.js and Python Flask, Strands Agents SDK for orchestration, and real-time market data integration, achieving sub-5-second response times for complex financial reasoning.
The success of this team highlights that practical AI building skills can be acquired without an engineering background, given the right tools, structured support, and an environment that encourages experimentation. Key principles contributing to their success included prioritizing learning over winning, maintaining regular communication, starting with a Minimum Viable Product (MVP), actively seeking mentorship, and fostering a psychologically safe environment.
Anatomy of an Enterprise AI Solution: Qlik Answers
Building on the idea of specialized AI agents, Qlik, a leader in data integration and analytics, developed Qlik Answers to address enterprise needs for fast, trustworthy answers from their data. This system allows employees to ask natural-language questions and receive grounded, sourced answers from various sources like knowledge bases, live analytics apps, glossary definitions, or documents. Since its general availability in February 2026, Qlik Answers has seen significant adoption, with its Discovery Agent surfacing over 100,000 discoveries for customers.
Architectural Layers for Scalability and Accuracy
Qlik Answers is built with clear architectural boundaries to ensure scalability and accuracy, rather than relying on a single, large assistant. The system comprises several layers:
1. Entry Layer: Provides a stable conversational entry point within Qlik Cloud, allowing new backend capabilities to be added without changing user interaction.
2. Routing Layer: A lightweight component that reads user messages and conversation context to quickly decide where to send the request, focusing solely on routing accuracy.
3. Answer Layer: Coordinates response generation after a request is routed, choosing between a fast path for simple requests and a more detailed path that breaks down complex questions and integrates information from multiple sources.
4. Specialist Agent Layer: A shared runtime environment that defines how specialized agents, tools, state, and human-in-the-loop steps operate, enabling the addition of new specialists without reinventing orchestration models for each.
5. Conversational Analytics Layer: Handles structured data questions by routing them to an app-aware reasoning path designed specifically for analytics rather than general text generation.
6. Retrieval Layer: Manages unstructured document indexing and retrieval using Amazon OpenSearch Service, which stores and searches content to ground responses from knowledge bases and documents.
7. Model Access Layer: Connects to Amazon Bedrock through Qlik’s own large language model (LLM) gateway for chat, streaming, embeddings, and reranking. This layer also applies Amazon Bedrock Guardrails for content filtering and grounding validation. For regional unavailability, Qlik temporarily hosts models on Amazon SageMaker AI.
Ensuring Grounded and Trustworthy Answers
A critical design decision for Qlik Answers was grounding, which ensures answers are verifiable and trustworthy. The answer layer draws context from structured app metadata, retrieved knowledge base content, glossary definitions, and document content for summarization. After assembling the elements of a complete answer, a grounding-validation check is performed using Amazon Bedrock Guardrails, comparing the generated answer against its source content. This transforms grounding from a simple retrieval step into a controlled production check, ensuring accuracy and providing citations for unstructured questions. For structured questions, the conversational analytics path takes over.
The Role of Amazon Bedrock
Qlik chose Amazon Bedrock for its multi-model flexibility, allowing the assignment of the most suitable model to each agent's task. This approach avoids the limitations of a single, general-purpose model, which can become slower and less accurate as more capabilities are added. The platform also helps address data sovereignty requirements across different regions and facilitates forecasting model capacity to plan for demand months in advance.
Practical Uses and Limitations
These AI agent-based systems offer practical benefits across various industries. For instance, Bystronic deployed an AI chatbot in 15 minutes, allowing employees to query real-time operations data. Lintech International indexed over 17,000 technical documents, reducing manual research time by 75 percent. TouchPoint Support Services uses Qlik Answers to provide fast, compliance-aligned guidance to 15,000 staff in regulated environments. The analytical finding suggests that such multi-agent architectures, coupled with robust grounding mechanisms, can significantly improve efficiency and accuracy in enterprise data interaction.
However, limitations include the complexity of orchestrating multiple specialized agents and the ongoing need to manage data sovereignty and model capacity. While the structured programs successfully enable non-coders, the initial setup and mentorship require dedicated resources. The effectiveness of these systems heavily relies on the quality and accessibility of the underlying data sources and the continuous refinement of the routing and answer generation logic.