Artificial intelligence (AI) is software that performs tasks that normally need human judgment: recognizing patterns, understanding and producing language, making predictions, and, increasingly, taking a series of actions toward a goal. In enterprise technology the term covers several quite different kinds of capability sold under one label. Knowing which kind a vendor is actually selling is one of the most useful things a buyer can establish before an evaluation starts.
At a glance
- Most modern AI learns statistical patterns from data rather than following only hand-written rules, so its outputs are useful but sometimes wrong, and some systems can give different answers to the same input.
- Enterprise AI includes predictive, generative, conversational and agentic systems; they behave differently and carry different risks.
- AI speeds up or automates specific tasks within a process; it doesn’t redesign the process around them.
- Results depend heavily on the quality of the data the system uses.
- AI doesn’t remove the need for human oversight, and more autonomous systems usually need more of it.
What problem it solves
Traditional software follows explicit rules: if X, then Y. That works well when every case can be written down in advance. Many business tasks can’t: reading an invoice in an unfamiliar layout, working out what a customer is asking for, spotting an unusual transaction among millions, or summarizing a long contract. AI handles tasks like these by learning from examples instead of following a complete set of rules.
In practice, organizations use it to take repetitive judgment work off people, to handle volumes no team could review by hand, and to make information easier to find and use. Common examples include document processing, customer service chatbots and agent assistance, fraud and threat detection, code assistance, search and summarization across internal documents, and forecasting.
How it works
Learning from data. Most enterprise AI is built on machine learning: a model is trained on large amounts of data and learns patterns it can apply to new inputs. A fraud model learns what fraudulent transactions tend to look like; a language model learns the relationships between words and ideas. Outputs are scores, predictions or content based on those learned statistical relationships. Whether the same input always gives the same result depends on the model and its settings: many predictive models are repeatable, while generative systems often vary from run to run, depending on how they are configured. Either way, the system can be confidently wrong, often called hallucination in generative systems.
The main types.
- Predictive AI uses historical data to forecast or score: demand forecasts, churn risk, fraud scores. It is the most established category, and many organizations have run it for years under other names.
- Generative AI creates new text, images, code or audio. Large language models (LLMs) are the best-known example.
- Conversational AI handles dialogue with people through chat, voice or search, often built on LLMs.
- Agentic AI takes sequences of actions toward a goal, such as querying systems, writing and running code, or sending messages, with limited human input at each step. It is the least mature category in enterprise use.
A product described as “AI-powered” may use any of these, or a combination.
What it depends on. AI results are only as good as the data behind them. Inconsistent or incomplete data produces confident-sounding wrong answers. A model built for one domain also tends to perform worse outside it: a tool tuned for legal contracts may struggle with engineering specifications. And the steps before and after the AI step still have to work; automating one task in a broken process mostly moves the bottleneck.
When it matters for buyers
- When a vendor adds AI to an existing product. Ask which type of AI it is, what it does with your data, and whether it costs extra now or at renewal.
- When the use case is chosen before the problem. Projects that start from “we need AI” tend to disappoint. Those that start from a specific problem with good data behind it are more likely to pay off.
- When data leaves your control. Whether your data is used for training, where it is processed and how long it is kept vary by vendor and contract, and are easy to miss.
- When the demo looks perfect. Demos use curated data. Insist on a proof of concept with your own data before committing.
- When staff are already using tools on their own. Unapproved use, often called shadow AI, is a reason to set policy and governance early.
Our AI solutions guide maps the enterprise AI market from the buyer’s side.
Questions to ask vendors
- Which kind of AI does this product use (predictive, generative, conversational, agentic), and for which features?
- Is our data used to train or improve your models, can we opt out, and is that in the contract?
- Where is our data processed and stored, and how long is it retained?
- How do you measure accuracy for our use case, and what happens when the system is wrong?
- What human review or approval steps are built in, especially for actions the system takes on its own?
- How is AI usage priced, and how could that change as our usage grows or at renewal?
- Can we run a proof of concept on our own data before signing?
How it differs from machine learning (ML)
AI is the broad goal of making software handle tasks that need human-like judgment. Machine learning is the main way that goal is achieved today: software learns patterns from data instead of being explicitly programmed. All machine learning is AI, but not all AI is machine learning; older rule-based expert systems, for example, are AI without learning from data. Generative AI and LLMs are a newer branch of machine learning focused on producing content.
Problems this helps with
- So many vendors now say their product is "AI-powered"
- Not knowing whether our data will be used to train a vendor's model
- A great demo that doesn't hold up on our own data
- Staff using AI tools nobody has approved
