The Rise of Generative AI: Transforming Technology and Business in 2025

Generative AI moved from pilots to daily workflows in 2024 and 2025. Most organizations now report using AI in at least one business function, and adoption keeps rising across IT, customer operations, and marketing. Now more than ever, with AI on the rise, managed security services help business protect their data and secure their employees.

What leaders care about right now

Use this list to brief executives and set targets for a first wave.

• Tie AI to outcomes you already track, revenue influenced, cost per ticket, time to resolution, content throughput, cycle time.

• Define guardrails up front, privacy, PII handling, prompt injection testing, data retention, human in the loop.

• Pick one workflow, ship in 90 days, measure it, then expand.

Use cases by function, quick wins and expected outcomes

Customer service and help desk

• Suggested replies with citations from your knowledge base, lower handle time and faster first response.

• Self service answers that surface policy and product details, reduce tickets that repeat common questions.

• .AI copilot for agents, summarize chats and create follow up notes, improve consistency

Sales and marketing

• Draft outreach that is grounded in firmographic data, raise reply rates with better targeting.

• Content refresh with product facts and citations, increase throughput while keeping brand voice.

Finance and operations

• Invoice, receipt, and contract extraction with validation steps, cut manual entry and speed approvals.

• Forecast commentary drafts that analysts review and edit, scale the monthly close narrative.

IT operations and software engineering

• Code suggestions with safe patterns, unit test generation, speed up routine work

• Log and incident summary with next best actions, shorten investigation time

Architecture choices, RAG versus fine-tuning versus tool use

Pick the simplest path that satisfies accuracy, cost, and latency.

Option	When to choose	Pros	Trade offs
RAG, retrieval augmented generation	You have changed source content, policies, KB articles, product docs	Grounded answers with citations, fast to iterate, keeps data outside the model	Adds retrieval latency and ops work for indexing and relevancy
Fine tuning	You need stable style or task behavior, or you have many labeled examples	More consistent behavior for narrow tasks, smaller prompts at runtime	Training cost and MLOps overhead, content is baked into weights until you retrain
Tool use and functions	You must take actions, search systems, run workflows	Deterministic actions with audit trails, better for transactions	Requires robust auth, rate limits, error handling

The 90-day pilot plan, week by week

Run this in your PM tool with owners and due dates.

Weeks 1 to 2, scope and safety

• Choose one workflow with measurable outcomes, for example, password reset or FAQ replies.

• Inventory data permissions and redactions, set retention, and define who can see what.

• Draft prompts and response formats include instructions for citing sources.

• Stand up a safe sandbox and add logging for prompts and outputs.

Weeks 3 to 6, build and test

• Implement RAG or a fine-tuned model for the chosen workflow.

• Tune retrieval relevance, test with real tickets or emails, and add guardrails for prompt injection.

• Define evaluation, accuracy checklist, latency targets, and cost per request budget.

• Shadow launch to a small group, capture human edits and reasons.

Weeks 7 to 10, ship and measure

• Roll to a larger group with a kill switch and rollback plan.

• Track resolution rate, handle time, deflection to self-service, citation quality, and edit rate.

• Publish a one-page scorecard to leadership, including next steps and budget.

• Hand off to production owners with weekly reviews and a backlog.

Governance and safety controls that matter

Use a simple policy that covers privacy, acceptable inputs, and review steps, then add technical guardrails.

• Route requests through a gateway that logs prompts and responses.

• Sanitize and label untrusted inputs, including content retrieved from the web.

• Separate system instructions from user content, restrict tools to the least privilege.

• Red team with prompt injection tests before production, record the test set.

• Require human sign-off for sensitive actions and public content.

Quality and measurement, keep it simple

• Latency, set a p95 per use case so teams can plan.

• Cost per request, set a budget, and enforce rate limits and caching.

• Grounding quality, percent of answers with valid citations for RAG flows.

• Hallucination rate, percent of answers that fail factual checks.

• Outcome metrics, resolution rate, CSAT, code review pass rate, or content acceptance rate.

Pricing and cost control

• Use retrieval filters and small models for simple requests.

• Cache frequent answers and chunk content to reduce tokens.

• Batch background jobs and set per-user rate limits.

• Monitor unit economics weekly and renegotiate model choices as volume grows.

In-house versus Flagler, who does what

Function	In-house team	With Flagler
Use case selection and KPIs	Product and business owners	Co define scope and success metrics
Data prep and permissions	Data owners and IT	Framework for classification and access rules
Build RAG or fine-tune workflow	Engineers	Architecture, build, eval, and handoff
Guardrails and testing	Security and QA	Prompt injection tests and logging patterns
Production run and reporting	Ops owners	Weekly scorecard and model cost controls

Simple ROI calculator, baseline versus with AI

Input	Baseline value	With AI target
Monthly tickets or tasks	8,000	8,000
Average handle time, minutes	9.0	6.0
Fully loaded cost per minute, dollars	1.10	1.10
Deflection rate to self service	5%	20%

FAQs

What is the fastest way to pilot generative AI in a business setting?

Pick one workflow with clear outcomes, set guardrails, use RAG for accuracy with citations, run a 90-day plan, then expand.

When should we use RAG instead of fine-tuning?

Choose RAG when content changes often or you need citations. Choose fine-tuning when you want stable behavior or a specific style, and you have training examples.

How do we measure quality and reduce hallucinations?

Use checks that compare outputs to retrieved sources, track a hallucination rate, and require citations for answers that rely on internal knowledge.

What data risks should we address before launching a copilot?

Access control, data retention, redaction of PII, vendor review, and prompt injection testing. Treat all inputs as untrusted and log everything.

How much does a small pilot cost and what drives spend?

Main drivers are tokens, retrieval calls, and evaluation time. Control costs with smaller models, caching, and clear rate limits.

What roles and skills are needed to run AI in production?

A product owner, an engineer with LLM experience, a data owner, and a security lead. Add support from Flagler for architecture, guardrails, and weekly reporting.