If you’re a business owner, you’ve probably heard “AI” explained in a way that makes it sound like something marketing uses to write social posts or IT uses to tinker with new tools. And honestly, that’s part of the problem: AI has been marketed like a shiny feature instead of being introduced like what it actually is, a capable teammate.
When we talk with clients at Seisan, we often reframe AI in a simple way: imagine hiring a sharp, entry-level employee who never gets tired, can read a million pages in a minute, and can handle repetitive work without losing focus.
That “employee” doesn’t replace your best people; it removes the grind that keeps your best people from doing their best work.
AI isn’t magic, and it isn’t a strategy by itself. But when you connect it to real workflows (sales follow-up, scheduling, quoting, routing, customer support), it becomes something much more practical: a multiplier.
In this article, I’ll walk through real use examples that are making companies money right now, and I’ll also call out what doesn’t work (because we’ve seen plenty of that too).
AI as a Teammate, Not a Strategy
A strategy is a plan. A teammate is someone (or something) who helps you execute the plan.
When businesses buy “AI” before defining the problem, they usually end up with one of these outcomes:
- A tool nobody uses because it doesn’t match real workflows
- A pilot project that looks cool, but never reaches production
- A shiny dashboard that amplifies messy data instead of improving it
The winning pattern is boring, but effective: pick a workflow that already exists, choose a measurable outcome, and let AI handle the parts that slow people down.
How AI works to enhance your team
You don’t need to understand every algorithm to benefit from AI, but you do want to understand the four “jobs” AI performs in business:
- Predicts — forecasts what’s likely to happen next (demand, churn, delays, conversion)
- Classifies — sorts things into buckets (good leads vs. bad leads, urgent vs. not urgent)
- Generates — drafts content (emails, proposals, summaries, call notes)
- Automates — triggers actions (follow-ups, ticket routing, schedule adjustments)
Think of these as four dependable employees you can hire. The trick is assigning them to the right work.
Where AI Makes Money Today
1) Sales & Marketing
What AI does well here: it reduces “time-to-response,” improves consistency, and helps your team focus on the leads most likely to close.
Practical use cases
- Lead qualification (automated scoring, behavioral signals, routing to the right rep)
- Proposal drafting (first drafts, statement-of-work outlines, scope summaries)
- Pricing guidance (historical deal patterns, discount guardrails)
- Marketing automation (campaign variations, audience segmentation, nurture emails)
Example company: HubSpot (AI-assisted lead scoring)
How it makes money:
- Marketing and sales stop arguing about “lead quality” and start working the same prioritized list.
- Reps spend more time talking to qualified buyers and less time chasing noise.
- Leadership gets visibility into what “high intent” actually looks like.
What to show on the frontend (screenshot ideas):
- The lead scoring setup screen (fit + engagement scoring)
- A CRM contact record showing the lead score and why it was assigned
- A workflow view showing what happens when a lead crosses a score threshold (notify, assign, schedule)
Seisan angle (how we apply this): In complex B2B sales (custom software, IoT, AI, AR/VR), the most expensive waste isn’t ad spend, it’s engineering time on unqualified discovery calls. We’ve helped teams use AI qualification to reduce low-fit calls, speed up scoping, and route the right opportunities to the right technical leaders.
2) Operations
What AI does well here: it reduces idle time, improves utilization, and prevents “surprise problems” (stockouts, missed appointments, last-minute reschedules).
Practical use cases
- Scheduling (auto-rebalancing schedules when cancellations or delays happen)
- Inventory prediction (demand forecasting and reorder recommendations)
- Field service routing (route optimization, dispatch support, travel-time reduction)
Example company/platform: Microsoft Dynamics 365 Field Service (AI-assisted scheduling optimization)
How it makes money:
- Dispatchers spend less time playing Tetris with calendars.
- Technicians spend more time on jobs and less time driving or waiting.
- Customers get tighter arrival windows, fewer reschedules, and faster resolution.
How Seisan applies this: We see the highest ROI when AI touches the messy middle: the daily exceptions. Your schedule looks great on Monday morning, but traffic, cancellations, and emergencies happen. AI that helps teams respond in real time is where the margin lives.
3) Customer Experience
What AI does well here: it increases coverage (24/7), reduces wait time, and makes support more consistent.
Practical use cases
- 24/7 support (triage, order status, policy questions, password resets)
- Personalization (recommendations, next-best-action guidance)
- Proactive outreach (notifying customers before they complain)
Klarna’s AI Ambition, And the Human Touch That Won’t Go Away
In the surge toward AI-driven business transformation, few stories illustrate the limits of automation more vividly than the recent shift at Swedish fintech giant Klarna.
In late 2023 and through 2024, Klarna embraced an AI-first customer service model with real gusto. The company paused hiring for more than a year and leaned heavily on generative AI, even claiming that its AI chatbot was doing the work of 700 full-time customer support agents. CEO Sebastian Siemiatkowski boldly suggested that AI could perform virtually all tasks humans do, and the business touted efficiency and cost savings as core advantages.
By early 2025, however, the story began to change. Despite impressive figures about the volume of AI-handled queries, customer satisfaction dipped and service quality suffered in areas where nuance, empathy, and real judgment matter most. In response, Klarna acknowledged that its all-AI approach had gone too far and began rebuilding its human support infrastructure.
Rather than abandoning AI altogether, the company is now blending automation with human agents, using AI to handle routine or repetitive tasks while making sure customers can always reach a real person when they need one. The company has even reassigned engineers and marketers to customer support roles to help fill gaps left by over-automation.
Klarna’s pivot is a powerful lesson: AI can be a powerful teammate in business, but it doesn’t replace the value (and often the necessity) of human insight and connection. When humans and AI work together thoughtfully, the result is faster service and the empathy and trust that customers still crave.
Takeaway
AI support succeeds when it’s treated like a front desk, not a replacement for your best people. The goal is to solve the common questions instantly, then route the harder cases to humans who now have better context and cleaner notes.
The First Dollar Is Always Boring
The first dollars AI earns are almost never from flashy innovation. They come from improving the fundamentals businesses overlook: answering the phone faster, responding to emails consistently, routing requests correctly, and cleaning messy data that breaks reporting.
AI thrives in the “unsexy” work:
- Summarizing calls into clean notes
- Drafting follow-up emails in your voice
- Updating CRM fields reliably
- Tagging and routing tickets
- Reconciling inconsistent data across systems
If that sounds small, it’s because most companies underestimate how much time (and money) gets lost in invisible friction. We’ve seen organizations reclaim hours per employee per week simply by tightening the basics, and those gains then fund the bigger AI plays.
Contact us today so you can be next.
External resources
- NIST AI Risk Management Framework (AI RMF 1.0) — a practical framework for thinking about risk, trust, and governance as you adopt AI
- U.S. Small Business Administration: AI for small business — a plain-English overview of benefits and risks
- FTC: Operation AI Comply (deceptive AI claims crackdown) — a reminder that “AI” isn’t a legal shield; claims still need to be truthful and substantiated
