Forrester predicts that customer service quality will dip in 2026 as companies wrestle with the complexity of AI deployment and the need for robust change management. The warning is notable coming from one of the most influential technology research firms — and it carries important implications for every company that outsources or manages customer-facing operations.
Instead of the dazzling AI transformation that many expected, Forrester characterizes 2026 as the year of "gritty, foundational work" — the unglamorous but essential infrastructure-building that will determine which organizations can deliver AI-powered customer experiences in subsequent years.
Why Service Quality Will Dip
The quality dip isn't caused by AI failing. It's caused by the organizational disruption of deploying AI:
Process redesign challenges: Existing customer service workflows were designed for human agents. Inserting AI into these workflows without redesigning them creates friction — awkward handoffs, inconsistent responses, and gaps in coverage.
Training and calibration periods: AI systems require extensive training on company-specific knowledge bases, policies, and brand voice. During the calibration period, response quality is variable.
Customer frustration with AI self-service: Forrester warns of a self-service backlash as customers encounter AI systems that can't resolve their issues. One in four brands may see improvements in simple self-service, but complex issues routed to AI often result in customer frustration.
Change management gaps: Customer service agents need training to work alongside AI — when to defer to AI, when to intervene, how to handle AI escalations. This transition is messy in practice.
Expectation vs. reality: Companies that announced AI-powered customer service improvements may face customer backlash when the initial experience doesn't match the promise.
The Parallel AI Organization
One of Forrester's most specific predictions: 30% of enterprises will create parallel AI functions that mirror human service roles. This means entirely new organizational structures designed to manage AI agents as if they were employees:
AI Agent Managers: Professionals responsible for onboarding and coaching AI agents — defining their knowledge base, conversation flows, and escalation rules. This role parallels traditional contact center supervisors.
AI Operations Teams: Specialists who optimize AI performance through analytics, testing, and continuous improvement. They monitor success rates, identify failure patterns, and iterate on AI configurations.
AI Unblockers: Specialists who troubleshoot when AI agents fail — identifying the root cause (missing knowledge, unclear policy, edge case) and resolving it. This role handles the exceptions that AI can't manage autonomously.
Conversation Designers: Professionals who design the interaction patterns, tone, and flow of AI agent conversations. This is a new discipline that combines UX design, linguistics, and customer psychology.
The Self-Service Equation
Forrester projects that one in four brands will see a 10% increase in successful simple self-service interactions by end of 2026, driven by growing trust in generative AI — with 78% of AI decision-makers finding AI outputs trustworthy enough for customer-facing deployment.
However, the "simple" qualifier is critical. Self-service improvements are concentrated in:
- Password resets and account access
- Order status inquiries
- FAQ-type questions with clear, policy-based answers
- Appointment scheduling
Complex issues — billing disputes, technical troubleshooting, emotional complaints, multi-step problem resolution — remain firmly in the human domain. Companies that push AI beyond its competency boundary for these interactions risk the customer backlash Forrester warns about.
The Hiring Shift
The restructuring of customer service around AI is creating new hiring needs. 42% of organizations expect to hire for AI-focused CX roles by end of 2026, including:
- Conversational AI designers
- Automation analysts
- AI quality assurance specialists
- Human-AI interaction coordinators
- Customer escalation specialists
These roles represent a net shift in workforce composition — fewer generalist agents handling routine inquiries, more specialists managing AI systems and handling complex interactions.
Implications for Outsourcing and VA Services
Forrester's predictions create both challenges and opportunities for customer service outsourcing providers:
Quality maintenance: As in-house service quality dips during AI transitions, companies may turn to outsourcing partners who can maintain consistent human service quality while the AI infrastructure matures. Virtual assistants provide a stable human service layer that doesn't require the organizational disruption of AI deployment.
AI transition support: Companies need help navigating the gritty work of AI deployment — process redesign, training data preparation, escalation flow design, and quality monitoring. Outsourcing providers with AI expertise can offer these as managed services.
Hybrid models: The most effective approach during the transition period is likely a hybrid model — AI handling simple, high-volume interactions while human agents (either in-house or outsourced) handle complex, relationship-intensive cases.
New service categories: The parallel AI organization creates demand for outsourced AI agent management, conversation design, quality assurance, and escalation handling — services that forward-thinking VA providers can add to their offerings.
The Forrester prediction is ultimately constructive: service quality will dip because companies are doing the necessary work to build AI-powered customer service infrastructure. The organizations — and service providers — that navigate this transition period successfully will be the ones that deliver superior customer experiences in 2027 and beyond.