BI platforms are setting the bar high when it comes to harvesting insights from vast amounts of data.
But what if business intelligence could go one step further? Enter agentic AI—where AI agents not only process data, but also engage in autonomous reasoning and provide proactive insights. This capability offers immense potential, coupled with unfamiliar challenges. Businesses need to learn how to effectively use AI agents while ensuring their behavior remains controlled and policy-driven.
In our recent webinar with aiXplain and TDWI, experts Fern Halper (TDWI), Vice President and Senior Director of QWI Research for Advanced Analytics, Ebrahim Alarecki, Principal Machine Learning Engineer at Incorta, and Nur Hamdan, Product Lead at aiXplain, explore how organizations are transitioning from traditional data analytics to more autonomous, agent-driven systems: trailblazing an exhilarating new era of business intelligence.
Shifting from analysis to action with AI
While AI encompasses several methodologies like machine learning, neural networks, and natural language processing (NLP), organizations are keen to experiment across diverse GenAI use cases: such as predicting churn, understanding customer behavior, product recommendations, and even fraud detection.
Research shows organizations using sophisticated AI tools are more likely to derive significant top or bottom-line benefits than those using basic analytics.
The transition from simply collecting and analyzing data to integrating AI into applications marks an exciting shift toward developing solutions that not only interpret data – but also act on it
What is Agentic AI?
Accelerating this shift is Agentic AI. This refers to intelligent systems designed to act autonomously and proactively, making decisions and taking actions based on data without needing constant human input (like traditional AI models would).
AI agents can “sense” their environment (through data inputs), “reason” about it (using AI models), and “act” by executing tasks or making decisions to achieve specific goals. This means these systems can process large amounts of data, engage in independent reasoning, and provide insights without relying on human input.
- Autonomous: AI agents are designed to act independently, taking action toward specific goals without needing continuous direct human oversight.
- Proactive: These agents engage in autonomous reasoning and provide proactive insights, often without human intervention. For example, an AI agent could autonomously plan a travel itinerary, book flights, and make payments.
- Multi-Agent Systems: Often, AI agents are part of a larger system where multiple agents work together to perform different tasks. For instance, in a translation system, one agent might handle linguistic research, another validates terms, and a third ensures contextual accuracy.
- Data-Driven: AI agents rely heavily on data to make decisions. The data can be real-time, operational, or transactional, and the agent uses this data to learn, adapt, and make timely decisions.
- Learning and Adaptation: As new data is provided, AI agents can adapt their actions and decisions accordingly. They are designed to process and analyze live, real-time data to respond to dynamic conditions.
When asked “Is your organization thinking about agentic AI?”, 25% of respondents said it was the first they had heard of it, while others were already planning or experimenting with it.
Steps to building agentic applications
As Nur Hamdan explained, developing agentic AI applications involves several steps, including defining the desired outcomes and selecting the appropriate AI models and tools. The architecture of these applications typically includes a combination of large language models (LLMs) and specialized agents that work collaboratively to achieve specific tasks. Integrating data retrieval systems and governance frameworks further enhances the effectiveness and security of these applications.
- Prepare and Consolidate Data: Aggregate data from all systems – ensuring it’s unified and accessible in real-time.
- Integrate and Map Data: Use data mapping tools to structure and integrate operational and analytical data.
- Build the AI Framework: Choose the AI model (e.g., language models) and set up the agent’s reasoning loop.
- Implement Security and Guardrails: Apply access controls and data governance to protect sensitive information.
- Create Multi-Agent System: Set up agents for specific tasks and orchestrate their interactions.
- Process and Adapt in Real-Time: Enable agents to access live data and adapt to new patterns as they emerge.
- Test and Fine-Tune: Identify issues, refine models, and optimize decision-making accuracy.
- Deploy and Monitor: Deploy the system, ensuring scalability, and monitor for ongoing performance.
However – even with these steps in place, using agentic AI hinges on two key elements that must be implemented before AI can be fully deployed.
Key 1: Agentic AI demands a strong data foundation
As with any AI, the phrase “garbage in, garbage out” applies. Agentic AI needs a reliable, accessible, strong data foundation to function effectively, and live data to make accurate decisions. Companies often have data siloed across multiple data sources (ERP systems like SAP, or Oracle), which must be unified and accessible to be usable by AI agents.
“For an agent to make a decision, it has to be timely – it can’t operate on outdated information. It should be relevant, and that’s where Incorta comes in – providing live, refreshed data to support these agents.”
Incorta provides direct access to live, operational data from any source system – ensuring AI agents have relevant, timely information to work with. Incorta’s platform allows data to be mapped and consolidated in real time across various systems, providing a unified data environment for AI agents to consume.
Key 2: Agentic AI demands accountability and transparency to reduce error and risk
As AI systems become more autonomous, their actions must be understandable and explainable. aiXplain lets users see how decisions are made based on data and algorithms, promoting an accountable and understandable approach to using AI agents
- Error Prevention: aiXplain cross-references data sources and uses built-in tools like I inspectors to reduce the chances of AI producing incorrect or biased outputs.
- Safeguarding Against Hallucinations: aiXplain also helps prevent “hallucinations”—incorrect or misleading results—by validating AI outputs before they are acted upon.
aiXplain’s focus on transparency and reliability reduces the risks of errors, biases, and incorrect outputs as organizations experiment more with Agentic AI.
Incorta + aiXplain: A perfect match
The combination of Incorta’s data foundation and aiXplain’s advanced AI capabilities offers organizations a powerful platform for deploying agentic AI applications.
Incorta ensures that businesses have access to live, operational data in real-time, integrating data from multiple sources such as ERP systems and transactional platforms. This real-time data is crucial for AI agents, allowing them to make timely and accurate decisions. aiXplain complements this by providing the tools to build and manage AI agents that can autonomously process this data, reason about it, and take action.
Together, Incorta and aiXplain enable businesses to shift from traditional analytics to sophisticated, AI-driven decision-making. With Incorta’s unified data platform and aiXplain’s focus on transparency, accountability, and safeguarding against errors like “hallucinations,” organizations can confidently develop and deploy agentic AI solutions.
The future of business intelligence fueled by agentic AI
As AI evolves, we must evolve alongside it – committing to continually redefine our data strategies to keep pace. In this next frontier of business intelligence, AI isn’t just a tool for analysis—it’s an autonomous agent that can drive innovation and deliver answers at an unprecedented speed and scale.
With platforms like Incorta and aiXplain, businesses can seamlessly integrate real-time data and autonomous AI agents into their operations, unlocking new opportunities for proactive insights and agile responses. By ensuring a solid data foundation and robust governance frameworks, organizations are better equipped to navigate the complexities of AI, reduce risks, and stay ahead of market demands.
Embracing agentic AI is not just about adopting new technology—it’s about transforming how data is used to drive smarter, faster, and more informed decisions across every facet of business. The future is here, and with the right tools, we’re ready!