The future of artificial intelligence and how leaders can prepare

While the initial generative AI buzz may have calmed down, the tech isn’t going anywhere. Instead, it’s evolving, taking on new shapes and creating new use cases.

Here's how to prepare your organization for the future of artificial intelligence.

The first wave of generative AI has focused on experimentation. Organizations and individuals alike have started using tools like ChatGPT, AI assistants, and vibe coding to understand how they work and where they're valuable.

According to McKinsey, nearly two-thirds of organizations are still in this experimental phase. Individual productivity may have improved, but most organizations haven’t yet achieved full-scale transformation with AI and automation.

What’s your organization's AI maturity? Use the AI Readiness Index to find out.

The next wave of generative AI will shift from experimentation to orchestration with more advanced technologies and robust governance. 

In fact, some organizations are already starting to scale AI and automation across the business. RAG, multi-modal AI, MCP, and agentic AI are just a few of the technologies that will enable this shift and define the next AI evolution.

Before retrieval augmented generation (RAG), models could only rely on the information they were trained on. If that data was inaccurate or outdated, their outputs would be, too.

RAG changed the game by enabling systems to pull in relevant, up-to-date data from external sources. This allowed them to provide more accurate responses. 

RAG++, or contextual reasoning, takes it a step further. Instead of just looking for matching keywords, it considers context and aims to retrieve the most meaningful information based on the user’s goal.

Agentic RAG is the next step, and it goes even further. With agentic RAG, AI agents collaborate to conduct retrieval augmented generation, allowing them to handle even more complex queries.

AI assistants that use RAG++ and agentic RAG don’t just pull the right documents or data. They also assess context and explain why they made certain decisions. This streamlines and optimizes decision-making for users.

Think of the original version of ChatGPT: You typed something, and ChatGPT “typed” something back. 

The original version of ChatGPT was unimodal, or only able to process one type of data. You put text in, and you got text out.

Now, newer versions of ChatGPT are multi-modal. Multi-modal AI can “understand” more than text. It can process inputs such as audio, video, images, code, and even actions.

By analyzing different types of data, it can form a more comprehensive picture and generate more relevant outputs. For example, you might put images in and get text and audio back.

Multi-modal AI provides more accurate and “human-like” predictions. A model can analyze a video, summarize a meeting, generate a design, and then trigger a workflow—all in the same conversation.

This makes it ideal for copilots that listen to customer calls or service agents that can see what a user sees and guide them through a solution.

Model Context Protocol (MCP) allows LLMs to communicate with external tools, systems, and data sources. They don’t need APIs or connectors to interact with each other. Instead, MCP creates a shared language, allowing each model to request what it needs (like documents, data, or instructions) without hardcoding those connections.

As AI systems become more prevalent, MCP allows organizations to move from siloed pilots to AI at scale.

Agentic AI, or AI agents, can complete complex, multi-step tasks like draft code, send emails, or book a flight. Agent2Agent (A2A) protocols allow agents to communicate, collaborate, and negotiate with each other.

For example, you might have one agent that books a flight, one that secures a hotel, and one that finds a rental car. With A2A protocols, they can work together to share information and plan your entire trip.

By designing agent ecosystems, organizations create a unified system that streamlines operations and enables AI automation at scale.

Leaders who want to prepare their organizations for the future need to start taking action today. 

• Create an acceptable AI use policy: Assume everyone is using AI. Set up guardrails, train developers on safe usage, and define escalation procedures in case mistakes happen.

• Promote responsible AI: Use frameworks like RAISE to ensure responsible AI and governance.

• Treat your data as a strategic asset: Data is the foundation of AI. Structure, clean, and govern data to power generative AI now.

• Equip managers to talk about AI: Make sure managers are prepared to have hard conversations about AI. Instead of focusing on AI replacing jobs, highlight upskilling and redeployment opportunities.

• Upskill your people: Build AI literacy and skills across teams, from engineers to executives and business teams.

• Understand long-term costs: AI costs aren’t always obvious. Treat AI as a metered utility and build in monitoring, caps, and monthly reconciliation processes. 

• Define AI success metrics: Evaluate your organization’s AI readiness, set up AI centers of excellence, and define what success looks like to measure ROI.

The future of generative AI will focus on scaling it across organizations and orchestrating workflows between humans and agents to deliver ROI in the form of greater productivity, speed, or innovation.

To benefit from the latest technologies, though, you need to start cleaning your data, implementing agent frameworks, and adopting governance models now.

Get started today—help your teams build future-ready AI skills with Pluralsight.