Agentic AI vs Generative AI: The Next Big Shift in Enterprise Intelligence
- Arkon Data
- Sep 22
- 6 min read
Updated: Sep 30
Over the past two years, Generative AI (GenAI) has dominated headlines, boardroom conversations, and IT budgets. From copilots to creative assistants, GenAI showed the world that AI could generate tangible outputs (text, images, code) in ways that felt almost magical.
But 2025 is already showing a shift. The industry is now turning to the next frontier: Agentic AI. Unlike GenAI, which responds to prompts, Agentic AI systems can reason, make decisions, and act autonomously across workflows.
This shift is happening in a market already saturated with hype and failed pilots. According to MIT, 95% of AI pilots fail to scale into enterprise-wide deployments. At the same time, McKinsey projects that Agentic AI will represent nearly one-third of all GenAI interactions by 2028.
The question for leaders is no longer “Should we invest in AI?” but “How do we choose the right approach and ensure it scales responsibly?”
Defining Generative AI
Generative AI refers to models that produce new content, whether that’s text, images, audio, video, or code. Its rise has been explosive: the GenAI market is forecasted to reach $1.1 trillion in revenue by 2028, up from just $45 billion in 2024. This growth reflects both business and consumer adoption, from marketing content creation to software development.
For enterprises, the appeal is clear. A 2025 study shows that 60% of IT leaders consider AI a top priority for the next 12 months, and 53% plan to increase GenAI budgets by up to 25%. Popular use cases include drafting reports, automating customer service responses, and accelerating research and development.
Yet, limitations persist. GenAI is heavily dependent on human prompts, struggles with hallucinations, and cannot act independently. It is most powerful when augmenting human work as a creative partner or copilot rather than an autonomous decision-maker.
In other words, GenAI gives enterprises productivity gains, but it rarely delivers true operational autonomy.
Defining Agentic AI
Agentic AI is often described as the “next step” after GenAI. Where GenAI outputs content, Agentic AI systems operate as autonomous or semi-autonomous agents capable of reasoning, planning, and taking action toward defined goals. These systems don’t just answer questions but also complete tasks.
Gartner highlights Agentic AI as one of the fastest-advancing technologies in 2025, with adoption expected to accelerate rapidly in the next three to five years. As mentioned before, by 2028, Agentic AI is projected to power around one-third of all GenAI interactions.
Examples are already emerging:
BlackRock’s Asimov platform utilizes Agentic AI to monitor and analyze complex market signals in real-time.
LVMH uses agentic systems to track customer and operational data, identifying risks and opportunities proactively.
In healthcare, Agentic AI agents automate claims processing, coordinate diagnostics, and reduce administrative burdens.
Unlike GenAI, which relies on human-in-the-loop engagement, Agentic AI thrives on autonomy.
This raises both opportunities (scalability, efficiency, end-to-end orchestration) and challenges (risk, governance, explainability).
Agentic AI vs Generative AI: Key Differences
Although they share a technological foundation, Generative AI and Agentic AI serve very different purposes inside enterprises.
Generative AI is about what can be created. Agentic AI is about what can be achieved.
In practice, the two complement each other. Generative AI provides the building blocks (summaries, code, insights), while Agentic AI connects those outputs to enterprise systems and workflows, transforming information into actionable insights. Enterprises that treat them as a continuum rather than competitors will be best positioned to scale responsibly.
The Missing Ingredient: Context
When it comes to Generative AI and Agentic AI, context is one of the most critical factors for success. Models can only deliver reliable outputs if they understand the meaning behind the data they consume. Without context, numbers and fields remain abstract, leading to errors, ambiguity, and limited business value.
This is where Arkon Data Platform creates a distinct advantage. By extracting data from systems like Oracle Fusion Cloud while preserving its original structure and metadata, ADP automatically adds the context that AI systems need to interpret information accurately. Platforms like Databricks Genie can then consume this enriched data directly, enabling precise insights, reducing hallucinations, and unlocking AI that truly understands your business.
Escaping Pilot Purgatory: Why AI Governance Matters
The rise of Generative AI exposed a painful truth: most enterprise AI pilots never scale.
A recent MIT report found that 95% of generative AI pilots fail to deliver enterprise-wide impact.
Despite massive budgets, enthusiasm, and talent investments, companies often stall in what has been called “pilot purgatory.”
The reasons are consistent across industries:
Fragmented data scattered across legacy ERPs, CRMs, and cloud systems.
Unclear accountability for AI ownership leaving compliance and oversight gaps.
Security and risk blind spots, including privacy and security breaches, as a top concern.
Cultural resistance, as employees hesitate to trust AI decisions they cannot explain.
This is where AI Governance emerges as the missing link. Far from slowing down innovation, AI governance provides the rules, guardrails, and accountability that transform fragmented pilots into scalable, trusted solutions. Without it, enterprises remain locked in cycles of experimentation. With it, they can operationalize both Generative and Agentic AI responsibly.
Business Impact: From Creativity to Autonomy
AI’s value is no longer confined to R&D labs or innovation hubs. Today, it sits at the heart of business strategy: powering customer engagement, financial decisions, logistics, and executive leadership. But the type of value delivered depends heavily on whether organizations are leaning on Generative or Agentic AI.
Generative AI accelerates productivity. It drafts reports, writes code, creates marketing assets, and enables faster insights. For example, 60% of IT leaders rank AI as a top priority in the next 12 months, with 53% planning to increase GenAI budgets by up to 25%. It excels at augmenting human work.
Agentic AI transforms operations. By autonomously connecting to enterprise systems, it takes on higher-order tasks like risk analysis, resource allocation, and operational oversight. BlackRock’s Asimov is already demonstrating how agents can analyze markets overnight and prepare reports for executives by morning.
Together, these approaches reshape enterprise decision-making. Generative AI provides the creative spark; Agentic AI executes the strategic action. Enterprises that build the right foundations, unified data, clear governance, and secure infrastructure will be the ones that harness both dimensions at scale.
From Pilots to Scaled Impact
Generative AI and Agentic AI are complementary forces. One creates, the other acts. Together, they can reinvent how enterprises work. But without the right data foundation, even the most advanced models stall in endless pilots.
That foundation starts with Arkon Data Platform (ADP). We extract structured, trusted data from systems like Oracle Fusion Cloud and make it enterprise-ready for AI. From there, you can power innovation on Databricks Lakehouse, Microsoft Fabric, or the ecosystem that best fits your strategy.
The result:
AI-Ready data with full context.
Decisions you can trace back to the source.
AI that scales consistently across regions and teams.
Governance and security are built into every workflow.
The Road Ahead
2025 is the year enterprises stop experimenting and start operationalizing. Generative AI boosts creativity. Agentic AI delivers autonomy. The companies that connect them on a governed foundation will lead their industries.
At Arkon Data, we’re helping organizations in 30+ countries build exactly that foundation, combining flexibility with governance to move AI from promise to performance.
5 FAQs for Agentic AI vs Generative AI
1. Can enterprises adopt Agentic AI without already scaling Generative AI?
Yes, but most organizations will benefit from treating them as complementary. Generative AI often supplies the content, summaries, or insights that Agentic AI then operationalizes into workflows. Skipping GenAI entirely risks missing out on the building blocks agents often require.
2. How does context influence the effectiveness of Agentic AI vs Generative AI?
Generative AI is powerful at producing outputs, but without contextualized enterprise data, its results can be generic or error-prone. Agentic AI thrives when systems feed it structured, context-rich data that it can use to act autonomously, turning context into a competitive advantage.
3. What governance challenges are unique to Agentic AI compared to Generative AI?
With GenAI, risks center on hallucinations, bias, and IP misuse. With Agentic AI, the risk expands to autonomy itself who is accountable for decisions when an agent acts across systems? This makes governance frameworks like AI TRiSM and lineage tracking critical.
4. Will Agentic AI replace the need for human oversight in enterprise workflows?
Not in the foreseeable future. While Agentic AI can execute tasks with minimal human input, high-stakes contexts like finance, healthcare, or logistics require humans-in-the-loop. The real shift is that oversight moves from doing the work to monitoring the orchestration.
5. How should CIOs decide where to invest first: Generative AI or Agentic AI?
It depends on maturity. Organizations struggling with fragmented data and low trust should prioritize governance and GenAI use cases that build adoption. Those with stronger foundations can begin introducing Agentic AI in high-value workflows such as risk analysis or supply chain orchestration.
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