Implementation
Generative AI for enterprises: practical ways to cut costs and boost sales
Where generative AI and AI agents cut costs and grow revenue in large companies: support, back office, documents, sales and marketing, and how to measure it.
Vitalii DukFounder and CEO6 min readUpdated 
In short
Generative AI pays off fastest where work is high-volume, text-heavy and rule-bound: customer support, back-office processing, document review, and sales and marketing preparation. Agents now finish these tasks end to end instead of only drafting text. Pick one process, measure its cost per case before and after, and put controls in place before you scale.
Enterprises cut costs with generative AI by giving agents the high-volume, text-heavy work: support requests, invoice and document processing, report drafting and internal questions, with people reviewing the exceptions. They grow sales by giving sellers and marketers researched accounts, personalized content and faster responses. The gains are real but uneven, so the practical path is one measurable process at a time.
This guide lists the processes where the savings and revenue show up first, how to measure them, and what keeps the gains from eroding.
Where does generative AI cut costs?
Savings come from processes with three traits: many cases, mostly text, and clear rules for what a good outcome looks like. Today these processes are handled by agents that use tools and act in your systems, not by chatbots that only draft answers.
Customer support
Support is the most proven area. Agents resolve routine requests end to end, such as order status, account changes and policy questions, and hand complex cases to a person with the full context. For the requests that stay with people, an assistant suggests answers and pulls up the right policy.
The evidence is measured, not projected. An NBER study of 5,179 customer support agents found that access to a generative AI assistant raised issues resolved per hour by 14% on average, and by 34% for novice and low-skilled agents.
See how a full customer service agent is put together, from knowledge base to handoff.
Back-office processing
Finance and operations teams spend their days matching, checking and routing: invoices against purchase orders, payments against ledgers, expense claims against policy. An agent does the matching, prepares the exceptions with the evidence attached, and routes them for approval. Nothing posts until a person signs off, so the saving comes from preparation time, not from removing control. Read more about back-office agents.
Document review
Contracts, insurance claims, loan files and supplier onboarding packs are long and inconsistent. Agents parse them, extract the fields your policy needs, compare them with your rules and flag what is missing or out of range. Reviewers start from a prepared summary with the source page attached instead of a blank page.
Internal questions
HR, IT and policy questions take up a surprising amount of expert time. A knowledge assistant answers them from your own documents, with a citation for every claim and only the documents each employee is allowed to see.
Software engineering
Coding assistants and agents write tests, document legacy systems and handle routine migrations. The output is reviewed by an engineer before it ships, which makes this one of the lowest-risk places to start.
Where does generative AI grow revenue?
Revenue gains are harder to attribute than savings, but the mechanisms are clear.
- Sales preparation. An agent researches accounts, summarizes past conversations and drafts meeting briefs and follow-ups, so sellers spend more time with customers and less on preparation.
- Speed to response. Leads and customer questions get an accurate answer in minutes, at any hour and in the customer's language.
- Personalized marketing at scale. Campaigns, product descriptions and localized versions are drafted per segment, then reviewed for brand and legal compliance before they go out.
- Retention. Support agents that resolve issues on the first contact, and flag unhappy customers early, protect revenue you already have.
- New product features. AI features inside your own product, such as natural-language search, can be sold or used to win deals.
That last point can move fast. A B2B accounting platform shipped document search in one week with one of our engineers, turning a long-requested feature into part of its product.
For recurring sales and research work, a ready-made agent such as AI Coworker can run a prompt on a schedule against your connected apps, with each run saved as a conversation to review. Sellers can also hand it work from Slack or Microsoft Teams, without anyone building a workflow first.
Which processes should you start with?
Start where volume is high, the rules are written down and a mistake is cheap to catch. This table shows common starting points and what to measure:
| Process | What the agent does | What to measure |
|---|---|---|
| Support requests | Resolves routine requests, hands off the rest | Resolution rate, handle time, customer satisfaction |
| Invoice and payment matching | Matches records, prepares exceptions | Cases per analyst per day, error rate |
| Contract and claims review | Extracts fields, checks rules, flags gaps | Review time per file, missed issues |
| Employee questions | Answers from policies with citations | Tickets avoided, answer accuracy |
| Sales preparation | Researches accounts, drafts briefs and follow-ups | Selling time per rep, pipeline created |
| Marketing localization | Drafts versions per market for review | Cost and time per asset |
How do you measure the ROI of generative AI?
Measure cost per case, not usage. The steps are simple:
- Baseline. Record volume, handle time, error rate and cost per case for the current process.
- Count every cost. Include model usage, the platform, integration work and the time people spend reviewing agent output.
- Run a controlled pilot. Put real cases through the agent next to the current process and compare quality, not just speed.
- Track after launch. Cost per run, latency and quality drift over time as volumes and models change.
The AI agent ROI calculator helps you build the first estimate for a business case.
Broad estimates are useful for context. McKinsey estimated in 2023 that generative AI and related technologies could automate work activities that take up 60% to 70% of employees' time. Your own baseline is what will convince a CFO.
What keeps the savings from eroding?
Most disappointing results trace back to the same few gaps:
- No baseline. Without before-and-after numbers, a working agent still looks like a cost.
- Hidden review work. If people must check every output, the saving moves rather than disappears. Evaluations and targeted approvals keep review where it matters.
- Cost creep. Longer prompts, bigger models and retries push up the cost per run. Traces with cost and latency per step show where the money goes.
- Quality drift. Models and data change. Scoring a sample of live traffic catches regressions early.
- Risky actions without a gate. An agent that can send, pay or file needs an approval step before it does, or one bad run erases a quarter of savings.
On Dynamiq, evaluations run on test sets before release and on live deployments after, and observability shows cost and latency for every step of every run.
FAQ
How does generative AI reduce costs for enterprises?
By taking over high-volume, text-heavy work such as support requests, document processing, reconciliations and internal questions, and by preparing cases so that people only review exceptions. The saving shows up as lower cost per case and faster handling, provided review work and model costs are counted.
How can generative AI increase sales?
It gives sellers more selling time through account research and meeting preparation, responds to leads and customers faster, personalizes marketing at scale, improves retention through better support, and lets companies add AI features to their own products.
What is a realistic ROI for generative AI?
It depends on the process. High-volume support and back-office processes usually show returns first because the cost per case is easy to measure. Build the estimate from your own baseline and include model, platform and review costs.
Which departments benefit first from generative AI?
Customer support, finance and operations, legal and compliance review, IT and HR service desks, software engineering, and sales and marketing. They share high volumes of text-based work with clear rules.
What is the difference between generative AI and AI agents?
Generative AI produces content such as text, code or images. An AI agent uses a generative model to plan steps, call tools and act in other systems until a task is done. Most cost savings now come from agents, because they finish the task instead of only drafting part of it.
