Collaborative agentic AI systems have already started to deliver measurable impact, as evidenced by Spitch customers’ testimonies. The shift is clearly from “talking” AI – and what Gartner calls “agent washing,” the quiet rebranding of assistants as agents – to “doing” AI that delivers measurable impact with humans-in-the-loop to control risks.

The OpenAI and Apollo Research recently published a study on AI “scheming” – AI learning to hide its true intentions, secretly pursuing misaligned goals while appearing to follow its instructions. That underlines the critical importance of collaborative AI that remains aware it is working with a human partner and stays continuously aligned, e.g., customer service AI agents that understand context, take safe actions, and improve every interaction.

Warning that over 40% of agentic AI projects will be canceled by the end of 2027, due to escalating costs, unclear business value or inadequate risk controls, Gartner recommends agentic AI only be pursued where it delivers clear value or ROI.  For Spitch and our clients, this has always been the backbone of our approach for successful implementations.

Winners today aim for deployments that deliver measurably faster resolution, better-trained human agents, and higher trust in automated solutions with rock-solid guardrails. We see the best results when agentic AI is applied where there is clear value added and risk control, and when workflows are rethought for agentic patterns.

The Spitch vision at the heart of its Collaborative Agentic AI Platform and solutions is to unify humans and AI, maximizing the automation of routine tasks while preserving human empathy, judgment, and control. We leverage the latest agentic AI and LLM/GenAI capabilities, backed by robust data security, legal and ethical compliance (alignment with the EU AI Act and Swiss national legislation), and a strong focus on customer experience – ensuring that AI supports, not replaces, human agents. Spitch’s vision is rooted in its successful implementations experience in Switzerland and world-wide, as well as the following trends analysis.

Key trends

1) Correction towards Agentic AI that acts under supervision

What’s new: LLMs grounded with specific knowledge bases and guardrails-based workflow orchestration will be moving into production. Approve-to-act gates, policy-aware decisioning, and reversible actions will become standard.

2) Trust-by-design guardrails

What’s new: Grounded generation tied to enterprise knowledge, citation gating, explicit consent and disclosure, action whitelists, and end-to-end observability.

3) Human-AI team flows instead of handoffs

What’s new: Orchestrated collaboration where AI agents and human agents share context live. AI handles the busywork; humans focus on empathy and complex judgment. This will lead to fewer handoffs, eliminate the need to repeat information, and ensure higher first-contact resolution.

4) Quality, analytics, and continuous learning

What’s new: Conversation QA for 100% of interactions, outcome tracing from prompt to action, and closed-loop retraining on real outcomes

Expected market trajectory in 2026

• From pilots to platform rollouts: Budgets shift from experimentation to scaled programs in customer service, sales support, and claims. Buyers require quantified ROI and production-grade guardrails.
• Voice outgrows chat for complex tasks: As latency drops, voice becomes the preferred channel for multi-step, regulated, or emotionally sensitive interactions.
• Hybrid deployments become default: Data residency, privacy, and latency drive on-prem options for ASR, NLU, biometrics, and even domain LLMs – while leveraging cloud elasticity where allowed.
• Regulation shapes vendor selection: The EU AI Act and sector rules (finance, healthcare, public services) push for auditability, risk management, bias testing, and clear accountability.
• Security and fraud prevention rise: Passive voice biometrics and anti-spoofing capable of detecting and preventing deepfake attacks become mainstream as generative voice fraud increases. Authentication times drop while security improves.
• AI agents emerge for every role: Supervisors and back-office teams get workflow-integrated AI agents to relieve reporting and compliance workloads.
• Toward 2028 mainstreaming: Trajectory aligns with Gartner’s outlook that by 2028, agentic AI will autonomously make at least 15% of day-to-day work decisions and be embedded in roughly one-third of enterprise applications – raising the bar for platform scalability, governance, and integration readiness.

A brief forecast for Spitch in 2026

• Agentic orchestration you can trust: We will extend our orchestration to combine low-latency LLM responses with action templates and approvals. Every action will be traceable, explainable, and reversible – suited to regulated environments.
• Security-grade biometrics: Stronger passive verification with advanced anti-spoofing, frictionless, constant authentication during calls, and tighter integration with risk engines to reduce both handle time and fraud.
• Governance by design: EU AI Act-ready documentation, audit trails for prompts, automated compliance checks, and guardrails that link AI behavior to KPIs and policy.
• Quantified outcomes: We expect customers to measure and achieve improvements in AHT, FCR, containment, authentication time, compliance adherence, and fraud loss—supported by our QA analytics.

Bottom line

In 2026, collaborative agentic AI will be pragmatic, voice-first, and accountable. Rather than replacing people, it will reduce friction and amplify human judgment in contact centers, delivering value where it fits best. Spitch has already demonstrated this by earning the trust of marquee clients, including Migros Bank, PostFinance, Baloise, several Volksbank institutions, and other German, U.S., and Latin American customers. With more than 175 clients and numerous positive testimonials, Spitch expects stable, above-market growth in FY2025–2026 and is capitalizing on agentic AI trends for the benefit of its valued customers.