AI is one of the most overused terms in enterprise technology and one of the least understood. Every vendor claims it. Few organizations know where to apply it.
Ask a board of directors whether AI should be a strategic priority and you’ll get unanimous agreement. Ask three technology leaders what they mean by AI and you’ll get three different answers. One is describing a chatbot. Another means predictive analytics. The third is thinking about autonomous agents.
They’re all talking about AI; they’re just not talking about the same thing. Sound familiar?
That gap has consequences. Vendors are racing to attach AI to every product and service, and the pressure to do something with it is coming from the top. So, organizations buy. Many of them are investing before they’ve defined the business problem they’re solving, or the kind of AI that would solve it.
“The question isn’t whether to invest in AI. Every organization has already answered that one. The harder question is which capabilities will solve the business problem you’re trying to address.“
That distinction matters, because the technologies behind the term do very different things. Descriptive AI tells you what happened. Predictive AI tells you what’s likely to happen next. Generative AI creates something new. Agentic AI decides and acts. Each has value; none of them substitute for another.
Without a shared understanding of those differences, it’s hard to build a strategy that delivers anything. Technology leaders compare solutions that solve different problems. Business units invest in disconnected tools that never integrate into a coherent operating model.
Gartner has put a number on how that plays out. In June 2025, the firm predicted that more than 40 percent of agentic AI projects would be canceled by the end of 2027, citing escalating costs, unclear business value, and inadequate risk controls. In the same analysis, Gartner estimated that only about 130 of the thousands of vendors selling agentic AI are actually delivering it, a practice it calls agent washing.
None of this argues for slowing down. It argues that buying AI and building an AI strategy aren’t the same activity.
For CIOs and IT leaders, the conversation must start somewhere else. Which type of AI suits this business problem? Where does it create the most value for employees and customers? How do these capabilities work together across the IT estate?
Answering any of those requires a common language. Understanding the different forms of AI, what each does well, and where each fit is the first step toward a strategy that can scale.
Four AI Capabilities Every Technology Leader Should Understand
AI isn’t a single technology. It’s an umbrella term covering a range of capabilities built to solve different problems. Knowing what each one does is what separates an informed investment from an expensive experiment.
Most enterprise AI falls into one of four categories: descriptive, predictive, generative, and agentic. They get lumped together under one term, yet they serve very different purposes.
Descriptive AI: Understanding What Already Happened
Descriptive AI answers the first question any organization has to answer: what is going on? It analyzes historical and current data to surface patterns, trends, and anomalies that would otherwise stay buried in systems nobody reads. It powers the dashboards, reports, and monitoring that tell you how your environment is performing right now.
This is the least glamorous category and the one most enterprises already have. Everything else depends on it. You can’t predict what you haven’t measured.
Where it delivers value
- Establishing a clear baseline of current performance
- Surfacing patterns and anomalies across large data sets
- Making complex operational data visible to the people who need it
- Providing the historical foundation predictive models are built on
What it doesn’t do
Descriptive AI tells you what happened. It doesn’t tell you what happens next, and it doesn’t act on what it finds. It gives you an accurate picture of your environment and leaves the decision to you.
Predictive AI: Anticipating What Happens Next
Predictive AI applies patterns learned from historical data to current conditions to forecast what’s likely to happen next. Rather than explaining what already occurred, it gives organizations time to act before it does.
Enterprises use it to anticipate equipment failures, forecast demand, identify security anomalies, and flag service issues before they disrupt operations. For IT leaders, it makes a proactive posture possible. Teams can assess risk and intervene before users feel anything.
Where it delivers value
- Predicting device or infrastructure failures before they occur
- Identifying security anomalies
- Forecasting capacity and resource requirements
- Estimating the likely business impact of an issue
What it doesn’t do
Predictive AI tells you what’s coming but doesn’t do anything about it. Action still depends on people or on other AI capabilities.
Generative AI: Creating and Accelerating Knowledge
Generative AI produces text, code, summaries, and other content in response to natural language prompts. For most organizations it’s become the most visible form of AI, helping employees find information, draft communications, and move through routine work faster.
In service environments it improves knowledge management by summarizing cases, generating documentation, and delivering faster, more consistent responses. It compresses the time between a question and a usable answer.
Where it delivers value
- Creating knowledge articles and documentation
- Summarizing support tickets and technical information
- Drafting communications and reports
- Improving self-service experiences for employees and customers
What it doesn’t do
Generative AI produces content but doesn’t verify it. It also doesn’t act on what it creates.
Agentic AI: Moving from Insight to Action
Agentic AI is the newest of the four and the least widely deployed. Unlike predictive or generative AI, it can make decisions, coordinate tasks, and take action toward a defined objective without a person driving each step.
In an IT environment that might mean diagnosing a service disruption, determining the right resolution, and executing it. Across HR, finance, and facilities, it can run end-to-end workflows across multiple systems. Production use today is concentrated where environments are well instrumented and outcomes are measurable. IT operations is one of the furthest along.
Where it delivers value
- Resolving routine IT incidents automatically
- Coordinating multi-step service workflows
- Automating employee onboarding and offboarding
- Closing the loop between detection and resolution
What it doesn’t do
Because agentic AI acts, it depends on trusted data, clear policies, and defined limits on what it’s permitted to do. It also needs a way to escalate when it reaches the edge of those limits.
Different Capabilities. Greater Value Together.
It’s tempting to treat these as competing approaches but that isn’t how successful organizations use them.
Each solves a different part of the problem. Descriptive AI shows what happened. Predictive AI identifies what might happen next. Generative AI helps people understand information and work faster. Agentic AI takes action.
“The opportunity isn’t choosing one. It’s understanding how they work together to create a connected, intelligent operating model.“
Building an Enterprise AI Strategy: Why Orchestration Matters
Once technology leaders understand the capabilities available to them, the question changes from being “generative or agentic?” and becomes “how do these work together to solve a real business problem?”
“Most organizations deploy AI one use case at a time. A chatbot improves self-service. Predictive analytics forecasts issues. Each initiative delivers something. But without a connected strategy, you end up with more tools rather than better outcomes.“
The result is a fragmented environment. Information stays siloed and employees move between applications. IT governs an expanding portfolio of disconnected solutions. Adding more AI doesn’t automatically reduce complexity. It can just create a different kind.
Organizations seeing real return are taking a different approach. Rather than treating these as separate technologies, they’re orchestrating them into a single operating model that connects insight, decision, and action.
Consider an employee who can’t reach a critical business application. Descriptive AI has already established what normal looks like for that environment. Predictive AI detects unusual authentication activity before the employee notices anything is wrong. A generative assistant gathers the relevant context and explains what’s happening in plain language. An agent diagnoses the issue, determines the resolution, and executes it.
To the employee, it’s one seamless experience. Behind it, four capabilities are each doing the job they were built for, alongside the automation and integrations already running in the environment.
That’s the shift to aim for.
That’s the thinking behind Pomeroy SmartServices, powered by BanzAI. Rather than deploying AI as a collection of disconnected tools, SmartServices brings these capabilities together in an integrated service model. The result is a more proactive, automated, and connected employee experience, with the governance and visibility enterprise organizations require.
The technology is only part of the story.
From AI Buzzwords to Business Value
What matters more than the technology is the operating model behind it. Strong AI strategies aren’t built by collecting tools. They’re built by connecting the right capabilities to the right business processes, supported by governance, integration, and a clear view of where people and AI each create value.
AI will keep evolving. New models, new capabilities, new applications. Organizations that understand what these technologies do and how they fit together will be far better positioned than those chasing whatever comes next.
The ones seeing real return invest with purpose. They start with the business problem, select the capabilities suited to it, and build an architecture that lets those capabilities work together across the enterprise.
For CIOs and technology leaders, that means asking a different set of questions:
- What business outcome are we trying to achieve?
- Which AI capabilities are best suited to that outcome?
- How will those capabilities integrate with what we already run?
- What governance and operating model do we need to scale AI with confidence?
Before investing in another AI tool, evaluate the problems you’re trying to solve, the capabilities you already have, and how anything new fits your broader operating model. A thoughtful strategy will deliver far more than another disconnected implementation.
Nobody will judge your AI strategy by the number of tools you deployed. They’ll judge it by how well you connected people, processes, and technology.
Looking to turn AI strategy into business results? Connect with the Pomeroy team to discuss how an integrated approach to AI can help your organization move forward.
