Enterprise Connect 2025: Conclusion & Buyer Recommendations
Enterprise Connect 2025 made one thing clear: the contact center industry is racing full-steam toward AI-infused platforms that promise unprecedented levels of automation and insight
Enterprise Connect 2025 made one thing clear: the contact center industry is racing full-steam toward AI-infused platforms that promise unprecedented levels of automation and insight. But as a CCaaS decision-maker, it’s critical to separate visionary rhetoric from practical reality. Here’s how to navigate these developments:
Focus on Near-Term Wins: Identify AI capabilities that can deliver immediate improvements in your environment. For instance, AI-assisted agent tools (real-time suggested answers, call wrap-up summaries) are relatively easy to deploy and can boost agent productivity quickly. Features like Cisco’s AI Assist or Microsoft’s Copilot hand-off summaries fit this billcrn.comtechtarget.com. Virtual agents for well-defined tasks (IVR replacement for FAQs, simple chats) can also provide quick ROI by deflecting volume – e.g., Zoom’s Virtual Agent for Voice or AWS’s Amazon Q bots. Start by tackling your lowest-complexity, highest-volume interactions with AI; this secures early ROI and builds internal confidence.
Demand Proof and Transparency: When evaluating a vendor’s AI claims, request concrete evidence. Ask for customer case studies or pilots. If a vendor touts “agentic AI that reduces handle time by 30%,” ask to speak to a reference client or see the feature in action on real data. For cutting-edge offerings (like NICE’s Orchestrator or Talkdesk’s AI Voice Agent), inquire about availability and deployment timeline – is it in beta? GA? What resources (professional services, training) are needed to implement? Vendors willing to show you behind the curtain (even if under NDA) are more likely to have substance. Be cautious of those that can’t demo beyond canned scenarios. As noted by an analyst, some fully autonomous visions “feel like science fiction right now”techtarget.com, so look for vendors who acknowledge limitations and roadmap to improvement, not just perfection out of the box.
Evaluate Integration Effort in Context: Map out how these AI solutions will integrate into your existing systems. For example, if you use Salesforce CRM, how does each vendor connect to it? (Many do: Genesys, AWS, Cisco, and others have native Salesforce integrationsbcstrategies.combcstrategies.com). If you rely on a proprietary backend, can the AI agent access it via API? The goal is to avoid “islands” of automation. The value of an AI agent or orchestrator is drastically higher when it’s plugged into your customer data and business processes. So favor platforms and tools that have proven connectors or an open architecture to incorporate your CRM, order system, knowledge base, etc. Ask vendors to demonstrate a use case with your data if possible (many will do pilots where they ingest some of your knowledge base or use a sample data set). This will surface integration challenges early. Remember, a fancy AI that can’t pull up a customer’s account or update an order is not very useful in the real world.
Consider AI Openness and Flexibility: Determine how “locked-in” you’d be with each vendor’s AI. Some questions to explore: Can you bring your own AI models or choose among AI engines? (Cisco hinted at custom model optionsblog.webex.com, Genesys and NICE largely use their own but integrate others via API, etc.) If you want to leverage, say, OpenAI’s latest model for your bots, can you? Or if in a year a new superior AI emerges, will your platform let you swap or add it? Also, can you export your conversation data and insights easily? Owning your data and training from it is crucial for long-term AI strategy. Vendors like Five9 emphasizing easy export and analytics flexibilitybusinesswire.com is a positive sign. Openness also means multi-vendor ecosystems: Microsoft enabling third-party CCaaS with Teamsbcstrategies.com, or AWS partnering for CRM. Favor providers that don’t force an all-or-nothing adoption – the reality is you might want the vendor’s AI self-service but a different vendor’s workforce optimization, for example. Modular, API-driven platforms give you that freedom.
Align AI with Business Outcomes, Not Hype: It’s easy to be enamored with “GPT-powered agents” and “self-optimizing workflows,” but always tie it back to your KPIs: Are you trying to reduce customer effort score? Increase self-service containment to 50%? Improve NPS? Decrease training time for agents? Use those goals as your north star when assessing AI features. For each major announcement, ask “How would this help with X business metric or problem?” For instance, NICE’s Orchestrator is aimed at breaking silos and improving process efficiency – if siloed processes are hurting your CSAT, it’s worth exploringnice.com. Genesys’s AI scoring targets quality consistency – if quality variance is an issue, that’s directly relevanttechtarget.com. By focusing on your objectives, you can cut through extraneous features and invest in capabilities that move the needle. Also be mindful of time-to-value: some solutions might improve metrics significantly but take a year to implement (e.g., a full orchestration overhaul), whereas others might give a smaller improvement but in weeks (e.g., an AI assist tool). You might pursue both, but set expectations appropriately.
Pilot, Measure, Iterate: The beauty of many cloud AI solutions is that you can often run trials. Do a pilot or proof-of-concept with a vendor before full commitment. A/B test the AI: for example, route a portion of calls through an AI agent and compare outcomes to the control group. Collect data on things like containment rate, customer satisfaction, average handle time, and escalation rate. This empirical approach will validate vendor claims in your environment. It will also highlight unforeseen issues (maybe certain dialects are not well understood, or the AI’s suggested answers aren’t used by agents). Use those learnings to iterate – maybe you need to tweak the knowledge base or adjust the AI’s confidence thresholds. Vendors that support pilots and actively help tune during them likely have more operational substance. Those that shy away or only offer generic demos might not yet be ready for prime time. Insist on measurable criteria for success during any trial (e.g., “we expect the AI virtual agent to handle at least 30% of chats with >85% CSAT” or “agent assist should cut wrap-up time by half”). This keeps everyone honest and focused on outcomes, not just cool factor.
Plan for Change Management: Even the smartest AI won’t succeed if your people and processes aren’t prepared. Factor in training for agents on new AI-driven workflows (e.g., how to use an AI summary, how to oversee an AI agent’s work). Set guidelines for supervisors on how to use AI quality scores or coaching prompts. Update your KPIs if needed (you might start tracking AI containment rate or average transfer rate from bot to human). And communicate to stakeholders (including customers, if customer-facing AI is introduced) about what’s changing. For example, if you deploy an AI voice agent, let customers know you’re introducing a new system to help serve them faster, and provide an easy route to a human if needed. Internal buy-in is crucial: involve your experienced agents and supervisors in evaluating AI outputs – this will both surface issues and gain their trust when they see their feedback shapes the AI. The vendors can supply technology, but operational substance comes from how you embed it into your workflows. Start with co-pilot modes (AI assists humans) before fully autonomous modes, so everyone gains confidence.
Monitor and Guardrail the AI: As these solutions roll out, continuous monitoring is vital. Set up dashboards to watch metrics like AI success/failure rates, escalation reasons, and any anomalies. Implement guardrails – for instance, define scenarios where the AI should always hand off (complex billing issues, high-value clients calling, etc.). Many vendors mentioned tools for guardrails (AWS explicitly talked about ensuring AI doesn’t say unapproved thingsnojitter.com, Talkdesk uses keywords for escalationtalkdesk.com, etc.). Use them. Have a feedback loop: agents should flag if the AI assist gave a wrong suggestion; customers should have an easy way to indicate the bot didn’t help. Feed that data back into refining the system (most platforms allow updating intents, retraining on new examples, etc.). In essence, treat your new AI “teammates” as you would a new class of trainees – watch their performance, coach them, and gradually increase their responsibilities as they prove competence. This pragmatic approach will prevent unpleasant surprises and ensure the AI is truly adding value.
Consider Vendor Viability and Roadmap: Since you are likely investing in a platform for several years, assess the vendor’s long-term commitment and vision for AI. Are they investing heavily (e.g., acquisitions, R&D centers)? Do they have partnerships that strengthen their offering (like Salesforce+AWS, or Cisco integrating third-party models)? And importantly, how do they handle security, compliance, and data privacy in AI? Enterprise buyers in regulated industries must ensure things like data residency, GDPR compliance for stored interactions, no learning on sensitive data without consent, etc. Vendors who address these topics transparently (encryption of AI transcripts, options to exclude PII, etc.) are more ready for enterprise deployment.
In closing, the promise of “agentic AI” in contact centers is exciting – lower costs, faster service, and new insights are on the table. But 2025 is a transition year where hype and reality coexist. By critically evaluating each vendor’s announcements in terms of strategic fit, product readiness, and proven value, you can make informed decisions rather than getting swept up in AI fever. Some of these AI capabilities are truly ready to drive ROI (many buyers are already seeing benefits from AI assistants and analytics), while others will require patience and co-creation.
For enterprise and mid-market CCaaS buyers, the best approach is a balanced one: capture the “low-hanging AI fruit” now, plan and pilot the more revolutionary stuff for tomorrow. Ensure any platform you choose can support you in both endeavors – agile in delivering quick wins, and robust to evolve with more AI as it matures. If you apply skepticism, demand evidence, and keep your business goals front and center, you’ll cut through the hype and harness AI in ways that genuinely improve your contact center’s performance and your customers’ experience. The technology is more ready than ever to help – just make sure you pick the right tools and implement them with eyes wide open.