Customer support overwhelmed? A 90-day AI plan for contact centers

If support is overwhelmed, spend two weeks finding what drives volume, use agent assist and automatic summaries to free human capacity in weeks 3 to 6, then automate a few bounded, transactional contacts with an AI agent and score every conversation.

Published Oct 20, 2026 · Updated Oct 20, 2026 · Author: Contact Center AI Index

Primary research hub: Automation and ROI · Entities: Level AI, Observe.AI, Cresta, Verint, Genesys, Gartner

If your customer support is overwhelmed, do not start by buying a chatbot. Spend the first two weeks finding out which contact reasons drive the volume and which of them your own processes cause. In weeks 3 to 6, give your existing agents capacity back with agent assist, automatic call summaries and automated QA. In weeks 7 to 12, put an AI agent on two or three bounded, transactional contact reasons and score every one of its conversations. Platforms such as Level AI, Cresta and Observe.AI cover all three phases on top of your current contact center software. If you already run NiCE, Genesys, Talkdesk or Five9, check what their native AI includes before you add a vendor.

This plan is vendor-neutral. Vendor features are named only as worked examples, checked on October 20, 2026. See the methodology.

What should you do in the first two weeks?

Diagnose before you automate. Overload usually has three causes: too many contacts, contacts that take too long, or too few people. AI helps differently with each.

Pull four to eight weeks of conversations and measure these baselines:

• Volume by contact reason. Tag every conversation, not a sample. Conversation intelligence tools can classify 100% of interactions; spreadsheets and agent dispositions usually cannot.
• Repeat contacts. Customers who contact you again within 7 days about the same issue. These are failed resolutions, and they inflate volume.
• Failure demand. Contacts caused by your own processes, such as "where is my refund" or a broken password reset. Fixing the root cause beats automating the symptom.
• AHT (average handle time) and ACW (after-call work), the wrap-up time after each conversation.
• FCR (first contact resolution), service level and backlog age.

Level AI's AI Virtual Agent page describes this step as identifying "high-impact automation opportunities from customer interaction data" (Level AI). Whatever tool you use, the output should be a ranked list of contact reasons with volume, handle time, and whether the task is transactional.

Which fixes reduce pressure fastest?

Independent research supports starting with the agents you already have. A 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 workers (NBER). That is a single deployment of one assistant, but it is among the strongest public evidence on assist productivity.

Vendor examples for after-call work, all vendor-reported:

• In a Level AI case study, Empyrean's auto-generated case notes free up "almost a minute per call" (Level AI Empyrean).
• Cresta says Achieve "cut after-call work by 75%" (Cresta).
• Verint says a UK energy supplier using its Wrap Up Bot cuts 35 seconds from every call (Verint).
• Observe.AI's Companion Agent page lists a 65% reduction in after-call work, without naming a customer (Observe.AI).

What does the 90-day plan look like?

Two rules keep the plan honest. First, change one queue at a time and keep a control group, so you can attribute the gains. Second, do not expand the AI agent to a new intent until its existing intents pass the same QA bar as your human agents.

Which contacts should you automate first?

Pick contact reasons that are high-volume, transactional and bounded: the customer wants one thing, the system of record can do it, and the rules are clear.

• In a Level AI case study, Uown Leasing's virtual agent handles six collections tasks, including taking payments, checking balances and changing due dates, and covers 30%+ of inbound calls (Level AI Uown).
• In another Level AI case study, a healthcare provider's virtual agent handled 45% of prescription refill calls without agent intervention during a proof of concept. Average time on out-of-scope calls dropped from 90 seconds to under 30 seconds, which matters because misrouted calls also consume capacity (Level AI case study).

Avoid starting with complaints, cancellations with retention offers, or anything that needs judgment about exceptions. Those belong with people, supported by assist.

Speed claims vary. Genesys says some Agentic Virtual Agent deployments go live "in as little as a few weeks" (Genesys). Level AI says its expanded virtual agent moves "from kickoff to production in just weeks" (CX Today). Treat any timeline as applying to a narrow first use case.

How do you protect customers while you automate?

Customers are wary. In a Gartner survey of 5,728 customers, 64% said they would prefer companies did not use AI for customer service, and their top concern was that "it will get more difficult reaching a person" (Gartner).

Three safeguards address that directly:

1. Always offer a way out. A customer who asks for a person gets one, with the conversation history attached.
2. Score the AI like a person. Apply your human QA rubric to every AI conversation. Level AI documents scoring "every virtual agent conversation against the same QA rubrics and performance benchmarks as your human team" (Level AI).
3. Watch recontact, not just containment. An AI agent that ends conversations without solving the problem moves volume to next week.

For a detailed test plan, see how to evaluate voice and chat virtual agents.

How do you know the plan is working?

Track a small scorecard weekly against your week-2 baseline: contacts reaching humans, backlog age, AHT, ACW, FCR, repeat-contact rate, and CSAT or inferred CSAT. Read them together. In a Level AI case study, Empyrean's handle times "initially increased" after rollout, which the company attributed to agents resolving more issues on the first call (Level AI Empyrean). Rising AHT with rising FCR is progress, not failure.

Should you plan to cut headcount?

Plan to absorb volume first. In a Gartner survey of 321 customer service leaders conducted in October 2025, only 20% had reduced agent staffing due to AI, while 55% reported stable staffing levels while handling higher customer volumes. Gartner also predicts that by 2027, half of organizations anticipating major AI-driven workforce cuts will abandon those plans (Gartner).

For an overwhelmed team, that is good news: the realistic first win is answering more customers, faster, with the people you have. For how that translates into money, see where contact center AI ROI actually comes from, and for vendor options, the automation software buyer's guide.

Limits of this guide

• The 90-day timeline assumes an existing CCaaS, clean access to recordings, and a vendor that integrates with your systems. Procurement and security review can add weeks.
• Every vendor result cited (Level AI, Cresta, Verint, Observe.AI) is vendor-reported. The Level AI healthcare figure comes from a proof of concept, and Observe.AI's figure is not attributed to a customer.
• The NBER study measures a single deployment of one AI assistant; your gains will differ by channel, tool and agent tenure.
• Gartner's 2027 statement is a prediction, and its customer survey dates from December 2023.
• Very small teams (under about 20 agents) may get more from help desk automation and self-service content than from contact center AI platforms.