Artificial Intelligence (AI)

What is Artificial Intelligence (AI)?

Artificial intelligence is a broad label for systems that learn from data or examples instead of following only hand-written rules. A traditional program says "if the invoice total is above this amount, send it for approval." An AI system studies many past invoices and learns which ones tend to need a second look, even when no single rule captures the pattern.

Most AI in business today falls into two families. Machine learning finds patterns in structured data to classify, score or forecast: which leads are likely to buy, which orders look fraudulent, how much stock to reorder. Generative AI produces new text, images or code, usually on top of a large language model.

In practice, AI shows up inside ordinary tools: a support inbox that sorts tickets by topic, a CRM that suggests the next follow-up, a finance team that extracts line items from supplier PDFs, a dashboard that flags an unusual dip in sales. The value comes less from the model itself than from where it sits in the workflow and what it is allowed to do.

What can AI do for a business today?

AI is useful wherever people spend time on repetitive judgment calls or on reading and writing large volumes of text. Common uses include:

• Classification and routing: sorting emails, tickets, documents or leads into the right queue.
• Extraction: pulling fields out of invoices, contracts and forms.
• Forecasting and scoring: estimating demand, churn risk or the likelihood of a deal closing.
• Drafting and summarizing: first drafts of replies, reports and meeting notes for a person to check.
• Question answering: letting staff ask questions of internal documents in plain language.

The common thread is a task that is frequent, has a clear input and output, and where an occasional mistake can be caught before it causes harm.

How is AI different from automation?

Classic automation follows fixed rules: when a form is submitted, create a CRM record and send an email. It is predictable and cheap to run, but it breaks when the input does not match what the rule expects. AI handles the messy cases: a free-text email that could be a complaint, a refund request or a sales lead, or a scanned document in an unfamiliar layout.

Most useful systems combine the two. Rules handle the steps that are always the same, and AI handles the step that needs interpretation, such as reading, classifying or choosing which path to take. A workflow automation that calls an AI model at one step is often more reliable than an AI system asked to run the whole process on its own.

What are the risks of using AI?

AI systems produce probable answers, not guaranteed ones. Language models can state wrong information with confidence, known as AI hallucination. Predictive models can quietly lose accuracy when customer behavior or data sources change. Both can reflect biases in the data they learned from.

There are practical risks too: sending sensitive customer or employee data to a third-party service without checking its terms, letting an AI system take actions nobody reviews, and building on a tool that changes behavior after an update.

The answer is not to avoid AI but to design for these risks. Keep a person in the loop for decisions with real consequences, log what the system does, test it against known examples before and after changes, and limit the data and actions it can reach.

How do you get started with AI in a company?

Start with a process, not a technology. Pick one workflow that is frequent, well understood and currently done by hand, such as triaging inbound requests or extracting data from documents. Write down what goes in, what should come out, and how a person decides today.

Then check the data. AI needs examples of good outcomes and access to the systems where the work happens. If records are scattered across spreadsheets and inboxes, part of the first project is getting them into one place.

Build a small version that runs alongside the current process, compare its output with what people actually did, and only then let it act on its own for the cases it handles well. Track time spent, error rate and how often people override it.

How does Vizio AI put AI to work?

Our DX Studio builds AI into the way a business already runs. We design AI-powered internal workflows for research, classification, reporting, document processing and decision support, and connect them to the CRM, finance, marketing and support tools a team already uses through API and systems integrations. Where the question is about prediction, we build forecasting and scoring models on a clean data platform and surface the results in dashboards people actually check. We stay involved after launch so the system keeps working as data and tools change.

A typical first step is mapping one current workflow end to end and agreeing where AI would help and where a plain rule is enough.