AI agents can transform support in 2025 and beyond — but only if you deploy them against the right problems.
Moving faster at the wrong things isn’t innovation; it’s just expensive noise.
The best teams don’t adopt AI just to keep up — they deploy it with purpose:
- Unifying systems and data
- Anticipating needs before customers ask
- Saying “no” to automation that doesn’t serve the customer or the business
Here are five strategic plays to explore in Support, Knowledge Management (KM), and IT Service Management (ITSM) — not as hype, but as a roadmap for where AI can truly matter.
1. Unify Your Ecosystem
Fragmented systems create friction for both customers and agents. Imagine AI agents and human agents working in sync across portals and desktops, instantly sharing context to cut that friction by 30%.
In this setup, an AI assistant could pass a customer’s full journey — from self-service search to live chat — directly into an agent’s view. That means less time asking for details, fewer repeated questions, and a faster resolution.
Question to consider: Where could connected workflows elevate your customer journey?
2. Dream Big (But Measured)
Bold goals inspire change — but the key is making them measurable.
Maybe that’s cutting escalations by 45%, boosting self-service by 20%, or turning reactive fixes into proactive loyalty-building.
Pair every goal with a clear ethical guardrail: keep humans in the loop for sensitive cases, ensure data transparency, and maintain privacy compliance from day one. Big gains without governance aren’t wins — they’re risks.
Question to consider: Which high-impact metric would reshape your support strategy?
3. Make Data a Superpower
AI thrives on quality, connected data. Start by unifying core sources — cases, KB articles, CRM records — then expand to chats, call logs, and customer feedback.
High-quality, tagged, and cleansed data can fuel powerful predictions: spotting churn risks, identifying early issue patterns, and uncovering opportunities to delight customers before they ask for help.
Question to consider: What could cleaner, connected data tell you today?
4. Experiment with Intent
Not all AI pilots are equal — and speed isn’t the only measure of success.
Launch a focused 30-day experiment to automate a high-volume, low-risk task. Track results against your strategic goals, not just efficiency.
For example, automating knowledge retrieval might deflect 30% of tickets, but the real question is: does it improve the customer journey and free agents to solve higher-value problems?
Question to consider: What could one smart win teach you about your AI potential?
5. Stay Ethical, Stay Trusted
Trust is the foundation of long-term AI success. Let AI handle routine work, but keep humans in the loop for decisions with legal, financial, or reputational implications.
Build governance into your AI strategy from the start — privacy controls, compliance alignment, and full audit trails. Customers, regulators, and your board should all see that your AI program meets the highest standards.
Question to consider: How will you ensure your AI earns — and keeps — trust?
Measuring the Impact
Once you’ve launched, track both immediate outcomes (resolution speed, CSAT, deflection rates) and predictive signals (sentiment trends, churn indicators). Use these insights to scale AI into new workflows — from fraud detection to proactive support.
Dodge Pitfalls with Smart Strategies
AI can over-automate, fall prey to data bias, or get stuck in silos. Counter these with:
- Human oversight to review edge cases
- Regular content checks to ensure accuracy
- Cross-team coordination to keep AI aligned with business priorities
The Takeaway
Don’t let silos or shiny-object syndrome drive your AI agenda.
Break down barriers. Focus on the right problems.
Turn support into a growth engine.
Question to consider: Which of these 5 strategic plays could make the biggest impact for your team in 2025?