While AI has long been a topic of discussion within business teams, we often felt like spectators rather than actors. Today, that changes. Thanks to AI agents, we are no longer sidelined — we are empowered. AI agents aren’t just the next chapter of enterprise AI — they are the chapter being written right now.
Agents bring creativity to business teams and enable them to create value with AI, while data & IT teams help manage possible chaos and guarantee quality. It should be a true partnership. In this blog, we equip you to speak with your data team about the potential of AI agents. To make it a win-win, master why the time is now, areas where agents can drive incremental value, and how to build them (whether you're code-first or code-free).
Let's Back Up: What Is an AI Agent, Anyway?
What exactly is an AI agent? My favorite metaphor is from the famous children’s movie “Despicable Me,” in which the hero Gru works with “Minions” — not convinced?
- Minions have access to tools and common sense to determine how best to use them according to circumstances.
- Some Minions are more autonomous than others.
- Minions speak with each other and learn from their mistakes.
- Overall, Gru needs to orchestrate their work to achieve valuable outcomes. Without orchestration, it is a mess.
In essence, an AI agent is very much the same: It is an LLM-powered system that can gather information, make decisions, and take actions across multiple steps — not just spit out an answer. Agents can perceive, reason, and act autonomously, unlike traditional AI models or first-generation generative tools. They don’t just generate content — they orchestrate outcomes. The most successful companies won’t just experiment with AI agents in isolated pilots — they’ll deploy them across the enterprise with confidence, clarity, and control.
Why Should You Care About AI Agents, Urgently?
This evolution marks a significant turning point in helping business teams to activate AI. Here are six core reasons why you must start the conversation with your data team as soon as possible:
1. The world is learning fast:
The term reached its peak popularity on Google Trends in March 2025.
You risk being left behind if you do not join the conversation now. AI agents are quietly embedded across functions from marketing automation to customer support. If you're not experimenting, you're not learning — and that's a strategic liability.
2. If you don’t, others will define the future without you:
Business teams must shape AI adoption by proposing practical, value-driven use cases, or risk being mere consumers of technology decisions made elsewhere.
3. AI agents are the missing link for real transformation:
We’ve played with AI — now it’s time to put it to work. Just as factories once restructured to harness electricity, we must rethink work in the age of AI agents. With no-code, goal-driven agents, any business professional can solve problems and bring ideas to life — no IT or consultants needed. This is AI truly democratized.
4. The cost of inaction compounds daily:
Every week spent hesitating means missed opportunities, falling productivity gains, and higher costs later. Early movers are already redesigning workflows and gaining efficiency leaps.
5. Regulatory and ethical questions need your voice:
If business leaders don’t engage early, AI guardrails may be shaped without practical input. This is your chance to advocate for balanced, workable governance.
6. AI agents scale your thinking:
Instead of replacing roles, AI agents extend your capacity to analyze and execute instantly within safe guardrails. This is leveraged on a historic scale — ignoring it is like declining compounding interest.
When Do You Need AI Agents?
Bringing concrete and unique agent use cases to the table will allow you to influence discussions. Here are three tips to identify opportunities to improve current processes with agents.
1. When rigid processes stifle growth.
Traditional automation is rigid; agents offer adaptive, context-aware solutions. Agents are especially useful in automating processes that require:
- Gathering and sorting unstructured data
- Deciding what to do next based on the context
- Has several edge cases that require judgment (making it cumbersome to hard-code via traditional automation approaches)
- AND where mistakes aren't critical (e.g., a human will still be in the loop).
2. When chatbots fall short.
Chatbots answer — agents act. When you lose your suitcase, you don't want a phone number — you want your bag found and returned. What if an airline’s customer service could instead build a search agent to scan videos to track your luggage and a shipping agent to send it to you?
3. When time is your most precious asset.
The most precious resource isn't data — it's your time. AI agents handle repetitive or tedious tasks so you can focus on strategic thinking and customer relationships. AI agents can also kick-start your thought process by being a valuable sparring partner when you investigate a topic you may not be familiar with.
For instance, imagine your financial controller deploying an “Expense Reconciliation Agent” instead of juggling spreadsheets, emails, and policy documents. This agent would:
- Automatically review receipts (including images or PDFs).
- Cross-check each entry against company policy using up-to-date rules.
- Flag anomalies like duplicate submissions or non-compliant items.
- Route exceptions to the correct approvers, based on context.
- Update the finance system in real time.
- Prompt employees for missing details.
Traditional automation would struggle with edge cases like partially reimbursable travel or project-specific rules, requiring complex logic. A chatbot might explain policy, but wouldn’t handle the workflow.
With an AI agent, your controller no longer handles every task — they simply step in to review and resolve the exceptions the agent flags. This shift from doing to overseeing means faster closings, fewer errors, and more time for strategic financial insights.
How Can You Build AI Agents Without Coding?
You don’t need to be an engineer to get started with AI agents. With the right platform, building one feels less like software development and more like solving a business problem with the tools you already know.
In Dataiku, agents are enabled with specific tools to do common tasks such as searching the web, querying a database, sending an alert, generating predictions using your ML models, and more.
These tools can be mixed and matched to create agents that fit your real-world processes.
As a result, you can imagine creating:
Research Agents: Gather information from multiple sources, synthesize findings, and deliver insights. Perfect for market research, competitive intelligence, or news monitoring.
Process Automation Agents: Handle multi-step workflows that require some judgment. Ideal for document processing, approval routing, or customer service triage.
Decision-Support Agents: Analyze situations and recommend actions based on your business rules and historical data. They are great for inventory management, resource allocation, or risk assessment.
Agents as Collaborative Assistants: Work alongside your team to enhance productivity through proactive support. Useful for meeting summaries, follow-up coordination, or preparing briefing materials.
Of course, once you develop your skills, you can build more sophisticated use cases. Dataiku also supports full code agents leveraging popular frameworks like LangChain and enabling the creation of custom tools.
So, Are Agents Worth Your Time and Budget?
Absolutely.
Agents leverage your existing LLM infrastructure, amplifying ROI without requiring a new stack. The impact can be measured through the classic lenses of AI value: cost savings, productivity gains, risk mitigation, and accelerated iteration.
Beyond the numbers, there are human benefits: less stress, greater creativity, and a renewed sense of agency for business creators.
Moreover, agent-driven initiatives can unlock new revenue streams by creating new services:
- Subscription fees for access to an agentic app
- Usage-based pricing (number of tasks performed, amount of data processed)
- Licensing fees: License your agentic AI technology
Check our research report on the agentic ROI potential, in partnership with Economist Impact, for more examples.
From AI Consumers to AI Creators With Agents
While infrastructure and tooling have improved dramatically, building impactful agents takes more than plugging in a large language model (LLM). It requires a strategic foundation that fuses agentic AI with your company’s existing analytics, machine learning, and enterprise systems.
By building agents, business users regain control over their digital environment. You no longer need to wait for IT or data teams. You can initiate, create, and solve while partnering closely for secure, scalable deployment.
The shift is profound:
From passive user ➔ to active creator.
From dependent team ➔ to empowered innovator.
But it all begins with one thing: Choosing the correct use case. Something repetitive yet variable. Discover how some teams, most particularly Solidigm (jump to 2:07), are already revolutionizing their working methods. So, business teams, which AI agent will you build first?