Artificial intelligence is reshaping how we work — but not in one single, unified way. Beneath the surface of the current hype, two very different AI waves are unfolding. The first is visible and noisy. It is filled with influencers, prompt guides, and off-the-shelf tools promising instant productivity gains. The second is quieter but far more transformative. It includes a deep shift toward agentic systems that reason, plan, and act autonomously.

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Wave 1: The Consumerized AI Revolution

The first wave is what I call consumerized AI. It’s driven by individuals and teams experimenting with readily available tools like ChatGPT, Perplexity, Claude, and Gemini to boost productivity.

In TDWI research, organizations report 30–35% productivity improvements when employees use these tools for writing, summarization, or ideation. Those numbers are encouraging, but they’re estimates, not long-term measures of organizational impact. The reality is that we don’t yet know what happens downstream when auto-generated content starts flowing through business processes.

That’s where the concept of “workslop” comes in. Coined by researchers from Stanford Social Media Lab and BetterUp Labs and published in Harvard Business Review, the term describes low-quality AI-generated output that looks credible but lacks depth, nuance, or factual reliability. When this content is passed downstream, in emails, reports, or analyses, it can actually degrade productivity and decision quality, even as it appears to save time upfront.

The consumer wave is easy to adopt and exciting to use. It’s also highly commercialized. A cottage industry of “AI experts” has emerged to teach prompt frameworks, and thousands of startups are racing to build specialized AI assistants for every conceivable use case — from legal research to physician note-taking.

Generative AI can be powerful when used responsibly and within clear guardrails. But this model has limits. I call it the value ceiling. This is the point where individual productivity gains plateau because the organization’s underlying systems, processes, and data aren’t being used effectively. The result is that many companies are chasing short-term efficiency gains rather than long-term transformation.

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Wave 2: The Agentic AI Revolution

Meanwhile, a second, deeper wave of AI innovation is taking shape; the agentic AI revolution.

Agentic AI systems go beyond responding to prompts. They reason, plan, and act toward goals, often coordinating with other agents and systems. In enterprises, they’re being designed to automate and optimize entire domains:

• Data science, through model development and experiment orchestration (think data science agents).
• Data engineering, by building and maintaining pipelines (think data engineering agents).
• Data integration, by discovering, mapping, and moving data across environments (think data integration agents)
• Data monitoring and governance, by detecting drift, anomalies, and compliance risks.

This evolution marks a paradigm shift; from humans operating tools to software operating software, with humans in oversight and strategy roles (and of course, development roles to create the agents in the first place).

Major vendors are already signaling this future:

• Teradata announced AgentBuilder for building and deploying enterprise-grade agents.
• Google Cloud launched Agentspace and an Agent2Agent protocol for interoperable systems.
• SAP introduced Project Agent Builder and has begun publishing on agentic AI in SAP.
• Snowflake describes “enterprise agentic AI” as the next phase of data management.
• Microsoft unveiled Agent Factory to support agent construction and orchestration.
• Databricks announced Agent Bricks a new way to build production ready agents for use with your data
• AWS announced Amazon Bedrock AgentCore to enable organizations to deploy and operate AI agents

This wave has the potential to reshape organizations at their core. It promises not just task automation, but systemic re-architecture where agents manage workflows, monitor quality, and coordinate across complex systems. It will likely change the nature of work itself, transforming some jobs, eliminating others, and creating entirely new categories focused on AI oversight, trust, and governance.

In short, this is the wave that could redefine the enterprise operating model.

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Why the Second Wave Matters More

Most companies today are still fixated on the first wave; training employees to “prompt better” and exploring tools to draft faster emails or help with marketing content. That’s fine for incremental improvement, but it’s not transformation.

The second wave, the agentic enterprise, represents a structural shift. It’s the difference between using AI tools and building AI-driven systems. The former makes individuals faster; the latter reimagines how work gets done.

Forward-thinking organizations will recognize this and start preparing now — investing in AI-ready data architectures, strong governance frameworks, and cross-functional collaboration between business leaders, data teams, and IT. They’ll think beyond surface productivity to systemic impact. They’ll consider the potential impact (both positive and negative) to their businesses.

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The Bottom Line

We’re living through two concurrent AI waves. The first, the consumerized wave, is exciting, democratizing, and visible. The second, the agentic wave, is quieter (to some) but far more consequential.

In the near term, both will coexist. The productivity story is only the beginning. The transformation story is what comes next. It promises to be disruptive in a way that needs to be carefully considered.

What do you think?