1. Identify high-value, quick-win use cases for AI

Start by aligning on goals for AI

Most teams approach AI to solve for short-term pain: fewer queues, less overtime, more breathing room when people are out.

But the opportunity is much bigger than that. Imagine the majority of your conversations were being resolved automatically: 24/7, in any language, on any channel, with high quality, and for a low cost. What would that do for your business?

You wouldn’t just be reducing pressure on the team, you’d be resetting how they work. Not just saving costs, but changing the cost structure. Not just moving faster, but offering a completely different customer experience.

That’s the bar. Set your goals with that in mind. Ask:

• What problems are you trying to solve?

• How do those goals support broader company objectives – whether that’s scaling without additional headcount or improving the customer experience (or both)?

• How will you measure success? It could be resolution rate, CSAT, time saved, or other metrics that tie directly to your team’s objectives.

Defining this early helps ensure alignment and makes it easier to prove value later on.

Equally important: ensure your executive team understands what AI makes possible. Early alignment with leaders like your CEO, CFO, and CCO will unlock the resources and mandate you need to move fast and sustain momentum.

Analyze your existing conversation data to find high-value or quick-win use cases to automate

Ask:

• What questions do we see again and again?

• What % of support volume is repetitive or predictable?

• Where are the biggest delays happening that AI could speed up?

• What gets handed over to human agents that shouldn’t be?

• What questions could be easily answered with content you already have available?

Involve your team. Their insights can help pinpoint where improvements will have the biggest impact.

Look for workflows where AI can either fully automate resolution, or significantly reduce human effort

The goal isn’t just resolving queries more efficiently, it’s creating capacity to do higher-value work elsewhere.

These are your fast paths to ROI. Some AI Agents, like Fin, can resolve a large percentage of your support volume straight out of the box. (The average Fin customer sees 66%.)

Once you’ve identified a set of high-value, quick-win opportunities, you’ll have the foundation for an AI roadmap that connects your strategy directly to business impact.

This is your starting line. From here, you can begin testing AI against these use cases and proving value quickly.

2. Prove the basic economics

AI changes the economics of customer service. It breaks the linear relationship between business and headcount growth, unlocking the ability to scale without the traditional constraints of hiring.

In this new model, your team can handle more conversations without adding more people, which breaks the logic of traditional support economics.

What traditional support economics miss

Legacy models typically focus on a single metric: dividing total support cost by the number of conversations (or cases) handled. But this is an oversimplification. Here’s why:

• Conversation types vary Some queries are simple, others complex and time-intensive.

• Human resolution rate isn’t 100% It’s easy to assume that human support resolves every customer issue, but just because a conversation was handled doesn’t mean it was fully resolved.

• Team costs go beyond salaries Overhead, tools, training, and attrition all add up.

• Automation value depends on quality If your AI Agent doesn’t resolve queries effectively, it doesn’t save money.

In short, surface-level metrics like average cost per conversation don’t capture the full picture, because not all conversations carry equal weight, and not all costs are obvious. A cheap or “free” AI solution might look efficient on paper, but if it doesn’t actually resolve conversations, those interactions still flow through to your human team – bringing all of the associated costs back into play.

To understand the true economics of AI, you need a more nuanced model that reflects the actual outcomes, distribution of effort, and real cost of human work.

How to measure the real value of AI support

Operating in an AI-first world means you have to think about value differently:

The work you automate determines the value

Not all support work is equal, and neither is the value of automating it.

Solving a high volume of easy questions can reduce some costs, but the real payoff comes when you start automating the complex, time-consuming conversations that tie up your team and slow down resolution.

That’s where AI Agents deliver asymmetric value. Modern AI Agents like Fin can now handle the most complex, multi-step workflows that once required human experience, judgment, and context to resolve. With capabilities like Procedures, Fin can now follow your standard operating procedures while reasoning its way through conversations with customers, just like your human team would. The more you trust your AI Agent with meaningful work, the more your return compounds.

But ROI isn’t linear. Automating 60% of conversations doesn’t mean you cut 60% of costs. Work isn’t evenly distributed, and time savings often come in fragmented chunks across shifts and teams, so it rarely maps neatly to headcount reduction. But that doesn’t diminish the value. AI doesn’t need to replace full-time roles one-for-one. It frees up capacity, absorbs repetitive effort, and enables you to reallocate time. The more you automate, the more efficient, scalable, and cost-effective your support model becomes. The ROI doesn’t level off, it accelerates.

This is why automation rate (AI Agent involvement rate × AI Agent resolution rate) is a metric that starts to matter more and more – not just resolution rate.

When your AI Agent is handling complex workflows and not just answering FAQs, each percentage point of automation rate represents significantly more work off your team's plate. Teams with high automation rates see compounding returns because they're not just deflecting volume, they're eliminating the work that used to take the most time and skill.

To quantify that value accurately, you need to understand the true economics of human-powered support.

Understand your fully loaded cost per conversation

You don’t need to model every line item to understand the economics. But you should recognize that your true human cost per conversation goes far beyond salaries:

• Benefits and taxes.

• Software and equipment.

• Management overhead.

• Coaching, training, and QA.

• Attrition, backfill, and onboarding.

• Factor all of this in and you’ll get a more realistic cost baseline.

Note: Industry benchmarks put the average cost per conversation well north of $1, and often much higher for complex queries. That’s your breakeven point. Once your AI Agent starts resolving conversations below that threshold, it’s not just cost-neutral, it’s ROI-positive.

This is why performance is such an important component to understanding the value of an AI Agent. If it doesn’t resolve, it doesn’t return.

Look at outcomes, not just unit cost

A cheaper AI Agent isn’t better if it resolves fewer conversations. How often the AI actually solves the issue is what matters.

A low-cost, lower-performing AI Agent might seem like a better deal at first, but if it can’t resolve conversations effectively, it ends up costing you more. More escalations. More human handoffs. Less efficiency. Worse customer experience.

For example, an AI Agent that costs $1 per resolution and resolves 50% of conversations is far more valuable than one that costs $0 per resolution but only resolves 30%, because unresolved conversations still require human follow-up, which costs time, money, and even customer satisfaction.

You also need to consider the total cost of ownership – onboarding, integration, training, and maintenance all affect the true ROI.

A simple formula to calculate cost savings

Once you’ve accounted for the variables above, you can use this high-level formula to estimate your potential savings:

((HUMAN COST PER RESOLUTION) - (AI COST PER RESOLUTION)) X (NUMBER OF AI RESOLUTIONS)

Here’s an example of what this might look like:

This is just a starting point, but it gives you a clear way to model potential economic impact and begin forecasting the value of AI in your support organization.

If you want to get a sense of what this could look like specifically for you, use our ROI calculator.

Value beyond cost savings

Cost savings are only one aspect of AI-support economics. In early phases, AI unlocks clear efficiency gains. But the real transformation happens when AI starts to shift the entire shape and purpose of your support model.

Without AI, your team will eventually hit structural limitations:

• Scalability stalls You can’t grow support at the pace of your business.

• Budgets inflate Headcount costs rise linearly.

• Customer expectations outpace your team Customers expect instant, AI-enabled support – and churn if they don’t get it.

• Your agents burn out Repetitive tasks drive attrition.

When you adopt AI fully, however, the equation flips:

• You decouple support growth from headcount growth.

• You reinvest human time where it matters most.

• You redesign support as a strategic lever for growth.

This is where AI starts to pay compounding returns. The real gains don’t come from cost cutting alone – they come from reallocating time and expanding the types of value AI can deliver directly.

As AI becomes more adept at answering complex questions and taking meaningful actions, you’ll need to rethink the economics of delivering customer service – even down to how you define productivity across humans and AI Agents.

With AI handling the bulk of your support volume, both AI and human teammates can focus on higher-value efforts, such as:

• Driving deeper product adoption.

• Increasing retention and LTV.

• Turning support into a value driver.

This shifts the cost/value equation in a meaningful way:

• Higher cost per human conversation.

• Lower total cost per resolution overall.

• Much higher value per interaction – whether delivered by a human or an AI Agent.

But that’s not a bad thing – it’s a sign that the work your support team is doing has evolved.

AI enables scale and value. Humans refine the system and focus on high-impact moments. Together, they make support a driver of growth, not just a cost to manage.

3. Decide whether to build or buy an AI Agent

In most cases, you shouldn’t build your own AI Agent.

It’s getting increasingly easier to build AI-powered software, which makes it tempting. But if you do it for the wrong reasons or misjudge the trade-offs, you’ll likely waste time and money.

You should only consider building if:

• You have a specialized AI team.

• You operate at massive scale.

• You have extremely niche support needs.

If not, you’re better off buying. And even if you do have all of those things, building is still not always the best option.

While DIY may look simple and cheap at first, building an AI Agent is rarely just a matter of connecting an LLM to a UI. It quickly turns into a big infrastructure investment with hidden development, product management, integration, and upkeep costs, unreliable performance, and limited strategic upside.

Building can sound appealing because:

• You think it gives you full control.

• You believe it’ll be cheaper over time.

• You want something tailored to your use case.

But that control comes with a heavy operational burden. It means building and maintaining a full system. You’ll need:

• A retrieval-augmented generation (RAG) pipeline for high-quality responses. Query rewriting and content ranking to structure ambiguous customer questions.

• LLM orchestration logic across vendors and model versions.

• Confidence scoring, handoff frameworks, fallback logic, and human-in-the-loop guardrails.

• A process for feedback loops, versioning, regression testing, and ongoing performance improvement.

• A team of the right people who are keeping up with the technology, moving fast, and adapting.

The bar to beat best-in-class solutions is extremely high

If you go the build route, you’re not competing with today’s market. You’re competing with where top AI Agents will be six months from now. You’ll need to keep pace on quality, safety, reliability, and coverage, without the benefit of the ongoing innovation and infrastructure investment provided by a shared platform.

That’s a serious commitment, and for most companies, it’s one that doesn’t align with their core focus or resourcing.

In most cases, the time, cost, and risk just don’t pencil out.

If your goal is speed to value, high performance, and ongoing innovation, buying an AI Agent is the faster, safer path. You get:

• Faster time to deployment and ROI.

• Ongoing improvements built on usage data from multiple customers.

• Reduced risk around performance, security, and maintenance.

• The ability to focus your teams on higher-leverage work: improving content, training the AI Agent, and analyzing impact.

If you have a strong AI team, massive support volume, and very specialized needs, it might make sense to build your own AI Agent. But even Anthropic, one of the leading AI labs, uses our AI Agent Fin because they recognize the constant iteration required to stay safe, accurate, and deeply integrated with support workflows.

For most companies, buying is the smarter path. You’re not just buying technology. You’re buying time, trust, and future flexibility.