What you need to do about AI in 2025

AI will have a huge impact on Support and Customer Success in 2025, but how can you tackle your own to-do list? I examine 5 trends that matter and propose a simple action plan you can tailor to your own requirements and capabilities. (This post is adapted from a longer post co-written with Olivier Delerm that covers both sales and marketing and post-sales.)

Let’s start with the trends.

Trend #1: AI-enhanced Agents

agents and robotAI tools can give your team members superpowers by performing research, delivering summaries, drafting communications, prioritizing actions based on sentiment analysis, and more.

Compared to other AI trends, AI-enhanced agent technology is pretty mature, with solid commercial tools available that handle most of the simpler use cases. 

Common Use Cases

  • Alert support agents when customers are getting frustrated or are more likely to escalate, allowing them to prioritize their work.
  • Enhance search tools so agents can find answers to customer queries without having to reinvent the wheel–and, if needed, easily collaborate with other agents who are working on similar issues.
  • Use generative AI to draft responses to customers.
  • CSMs can use AI to create custom presentation decks and prepare meeting follow-ups.
  • Draft serviceable knowledge base documents.

Less Common Use Cases

  • Update records in CRM applications based on notes and voice commands, add new contacts based on recent meetings or new employees and promotions.
  • Identify relevant news and changes in accounts, and help prioritize accounts activities using predefined playbooks.
  • Summarize customers’ relative value to the organization, their use cases, and even how technically knowledgeable a particular contact is.

Trend #2: Autonomous AI Agents

AI agents are autonomous robots that can perceive their environment and context, process information, make decisions and take actions to achieve specific goals without human intervention. 

AI agents are still a work in progress but progress is fast and remarkable, with basic interactions being handled very well.

Common Use cases

Less Common Use Cases

  • Some service bots are tackling unstructured customer service conversations, for instance to make appointments. It sounds promising! 

Trend #3: AI-enhanced Managers

Just like AI can help individual agents (trend #1), it can assist managers perform their own tasks of organizing work, prioritizing tasks, and giving suggestions for product improvements. The day-to-day duties of managers will change completely once the coordination function is automated.

(We will cover AI-assisted coaching separately in trend #4.)

Common Use Cases

  • Automatically assign cases, including in complex support settings that today assign cases manually.
  • Predict escalations. 

Less Common Use Cases

  • Forecast staffing needs using past case history, sales forecast, and even turnover predictions.
  • Extract validated requests for product changes based on customer experiences.
  • Identify high-cost customers.
  • Identify high-risk customers. 

Trend #4: AI Coaching

AI coaching delivers personalized, data-driven coaching to customer-facing agents, enabling automated onboarding, just-in-time coaching, and coaching for all customer interactions instead of small-scale audits. We are starting to see role-playing and simulation solutions for practice and training in a risk-free environment.

Full AI-enabled coaching is still a ways away, but some sentiment-analysis tools make solid suggestions for conducting fruitful customer interactions. Some tools are starting to do a decent job evaluating agents’ work in particular language quality and adherence to process. 

Common Use Cases

  • Suggestions for improving written communications, including emails and presentations.
  • Instant feedback during live meetings.
  • Completing case quality QA checklists for language and process (not yet for troubleshooting quality or other sophisticated behaviors).

Less Common Use Case

  • Practice environments for CSMs and support agents

Trend #5: At-scale Hyper-personalization

Hyper-personalization creates bespoke customer experiences by using data analytics, predictive analytics, data analytics, machine learning and LLMs.

It is starting to be used, successfully, for marketing and sales. It’s almost unheard of in support at this point, but the same AI techniques should work very well and take support interactions to the next level, enabling high-end experiences for all customers.

Common Use Case

  • Personalized recommendations based on the products or services the customer  purchased.

Less Common Use Cases

  • Personalized recommendations based on the level of knowledge of the customer, their media preferences (e.g. videos vs technical paper), and their communication preferences.
  • Personalization based on the specific use cases the customer is addressing. 

Now what?

What you actually do (and achieve) this year depends on the maturity of your organization and your technical infrastructure (data, tech stack, IT organization). But do take some action this year. 

  • Create a strategic plan to define where you will invest. Yes, you will need to run experiments but create a framework for where and how you will experiment. 
  • Assess your foundations.
    Do you have a mandate from your top management? Any resources earmaked for AI?
    Is there an AI governance charter establishing principle and guardrails?
    At the department level, what data is available, e.g., customer communications over phone, web meetings, emails, chats in text format? How is it structured? Do you have relvant success metrics such as first-call resolution?
    Are there AI experts within the company that you can work with?
  • Don’t jump into custom tools (yet). Start with free or affordable tools such as ChatGPT and Google NotebookLM. Then, explore the AI features of your existing CRM and communication tools. Most tools have incorporated AI-enabled features that are vastly superior to the ones available a few years ago. And you can stop here. The next step is to consider commercial standalone tools.
    If after exploring commercial tools you see that you need to create your own, then do that. But only then.
  • Focus on a few key priorities for your organization and firmly ignore others.
  • Data is key to enable quality results. For instance, if you are interested in improving search, you’ll want to focus on building a solid knowledge base.
  • Go step by step. Start with a POC, then a pilot, then a rollout. If you are deploying anything to customers, experiment internally beforehand.
  • Manage change within your team. AI can appear menacing to agents and managers who fear that big chunks of their jobs may disappear (show them how the interesting part of their jobs will stay!)

If any of this is too daunting, we can help.

What are you AI priorities this year? Share in the comments.

 

 

 

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