“AI is transforming customer support.”
Lots of people are saying things like that, but as we’ve talked to support leaders, we’ve heard over and over that it can feel like a lot of hype. Sometimes it’s because AI vendors overpromise and underdeliver; other times it’s because all the marketing fanfare sounds the same.
We’ve created this guide to solve that problem.
It’s full of examples of real, tangible workflows that support agents and leaders are improving with AI today, all over the world. These aren’t theoretical use cases—they’re real workflows that teams like yours use all day, every day.
If you’re a support agent, team lead, or executive looking for practical ways to integrate AI into your workflows, this guide is for you. It’s short, because we know you’re busy. Each section will highlight a key workflow, explain how AI improves it, and highlight the impact it can have on your customers, your team, and your business.
Because at the end of the day, AI is a tool that should solve real problems. It should make customer interactions smoother, your team’s life easier, and your company stronger.
I hope it’s helpful and it inspires you with some new ways AI could help your team.
Let’s get started.
Agent assist is a broad term, but it’s fitting because it covers a wide range of use cases. Many tools—like help desks—are rolling out native agent assist features that do things like expand text and improve tone, but they’re only the tip of the agent assist iceberg.
Agent assist tools can also make it easier for agents to find answers, aggregate info from multiple tools, and summarize knowledge base articles or the solutions used in past tickets.
Customer support teams are under constant pressure to deliver prompt and accurate solutions for customers. When they fail, even the most patient customers can lose faith in the support team. By turbocharging research and response workflows, assist tools help support teams do more with less.
Who it’s a great fit for:
Given the wide range of use cases included in agent assist use cases, support teams of any size and across any industry can find value in them.
Real-world example:
Before Ask-AI, Monday.com’s global support team wrestled with scattered information across multiple systems, including Slack, Chrome, their helpdesk, and more. This drove up handle times and caused confusion. By connecting their entire tech stack with Ask-AI and creating a single source of truth, Monday.com’s support team became significantly more efficient.
Average handle time dropped 13.5% among its most active Ask-AI users, a 10x improvement over agents who didn’t use Ask-AI.
Metrics and impact:
AI agent assist and copilot tools primarily impact efficiency-related metrics, including:
These use cases can also improve hard-to-measure areas, like brand voice consistency and response accuracy, which often show up in improved QA scores.
When it works, self-service is transformative for both customers and support agents. But many companies have outdated or siloed knowledge, forcing customers to sort through search results or give up and submit tickets anyway. That frustration translates into higher support volume, longer handle times for agents, and decreased customer satisfaction.
To combat this, many organizations create knowledge management teams. That’s not a bad thing, but oftentimes these teams spend inordinate amounts of time taking knowledge from different sources and crafting it into formats that customers can use.
AI-powered self-service addresses these pain points by unifying siloed knowledge sources and delivering relevant answers in a more intuitive, automatic way. Instead of wading through search results in a knowledge base, customers can ask their question in conversational language and instantly get the information they need, eliminating the need to file a support ticket entirely.
Who it’s a great fit for:
Any support team that’s handling a large amount of repetitive tickets can benefit from AI-driven self-service. It’s especially impactful for companies with large, evolving knowledge bases or those supporting customers in multiple regions or languages.
Real world example:
Customer success platform Totango recently acquired and merged with Catalyst, another platform. Combining two knowledge bases (and customer bases) is messy, so they recently launched an AI-powered self-service solution using Alhena, which is trained on both knowledge bases and previous support tickets. Within a few months, their AI is now resolving ~60% of their inbound ticket volume.
Impact:
Effective AI-powered self-service typically brings improvements in:
There are countless repetitive, manual processes in customer support, from drafting internal knowledge base articles to escalating tickets or creating feature requests and bug reports. Each one eats up time and creates the possibility of human error.
Support teams are using AI to streamline these tasks by building custom apps—trained on their data and tailored to their tech stack—that automate processes end-to-end.
Who it’s a great fit for:
Teams that have complex products, large ticket volumes, and unique workflows that aren’t covered by off-the-shelf solutions. If your team is spending a lot of time on manual work or relies heavily on tools like Zapier or Make.com, custom AI apps will be a huge help.
If you routinely find yourself stringing together multiple tools or manually moving data around, building a custom app can save time, reduce mistakes, and improve your team’s speed.
Real world example:
Own Company (recently acquired by Salesforce) is a data platform that helps SaaS companies manage their data. Given their global customer base, Own’s team found themselves spending time on repetitive tasks like translating tickets. Like every support team, they also spent plenty of time escalating bugs to JIRA and transferring tickets to other teams.
Using Ask-AI, Own’s team created custom apps that automate all three of these processes from end-to-end. Combined with other AI initiatives, Own’s agents now handle 130% more tickets each month, with 15% reduction in handle time.
Impact:
When you streamline processes through custom AI apps, you typically see gains across your efficiency-related metrics:
Creating custom apps through an enterprise AI platform can also reduce costs by eliminating the need to rely on other apps (like Zapier) and reduce reliance on engineering resources.
Support teams have access to a wealth of customer insights, but extracting them from support tickets, live chat, social media, and other channels is challenging and hard-to-scale.
Today’s leading support teams are using AI to simplify this process by automatically categorizing comments, detecting sentiment, and surfacing key trends, helping you react faster and make data-driven decisions.
In practical terms, that means you can spot rising product issues sooner, identify at-risk customers, and share relevant insights with product teams to improve offerings. Instead of drowning in data, your team can focus on taking meaningful action.
Who it’s a great fit for:
Companies that handle a large volume of support tickets and customer feedback across multiple channels.
Real world example:
Monday.com initially used AI to improve their team’s efficiency, but they also realized that their customer conversations should be a key input to building their product roadmap.
Using Ask-AI’s MosAIc platform, they began analyzing their support tickets to identify primary issues for customers. CX management partners with product management to prioritize issues and build a better product, reducing ticket volume and increasing retention.
Impact:
The impact of analyzing customer feedback with AI really depends on the insights you uncover. Common outcomes include:
Even the best support teams struggle with knowledge gaps—outdated documentation, siloed information, and unwritten “tribal knowledge” that only a few experienced agents possess. These gaps lead to inconsistent answers, longer handle times, and frustrated customers who get different responses from different agents.
Enterprise AI platforms can identify missing or outdated information by analyzing tickets, chats, and existing documentation. They can also surface existing resources in real time, ensuring every agent has instant access to the best, most current knowledge.
Support teams are using AI today to capture and create knowledge every day. The benefits of this can’t be overstated, because each new piece of knowledge increases the AI’s impact across all of the other workflows mentioned here.
Who it’s a great fit for:
Any support team struggling to devote time and resources to maintaining their knowledge base can benefit from introducing AI into their knowledge creation workflow. It’s especially useful for teams struggling with high ticket volumes, complex products, and remote team members.
Real world example:
Island is an enterprise browser that enables organizations to work securely. They implemented Ask-AI as an enterprise AI platform, using it to tackle several use cases. One clear benefit has been the way it’s uncovered areas their knowledge base can improve. As they’ve tackled those areas, it’s led to increased self-service, faster response times, and improved resolution times.
Impact:
Solving knowledge gaps has far-reaching impacts across an organization. From helping support agents serve customers faster to reducing internal ticket volume, any workflow or use case that’s dependent on capturing or sharing knowledge can see improvements.
Specific metrics include:
Customer support teams often don’t just help customers—they also field questions from CSMs, sales reps, product managers, and more. This leads to a large number of internal tickets or noisy Slack channels, which often take agents away from helping paying customers.
Enterprise AI platforms are solving this by using AI to bring knowledge to wherever people are working. For instance, Ask-AI’s customers usually make their entire knowledge base available in their help center, through a browser extension, and directly within their Slack workspaces. That means anyone across the entire enterprise can easily interact with the AI, effectively making self-service possible for every internal stakeholder.
Who it’s a great fit for:
Large enterprises with many different stakeholders see significant value from introducing AI to help with this workflow. Fast-growing companies with frequent new hires also realize significant benefits.
Real world example:
Ecommerce email marketing platform Yotpo saw a 20% reduction in internal support tickets (usually asked via Slack) by making their enterprise AI knowledge available in a dedicated Slack channel. Instead of wasting time tracking down answers or distracting support team members, CSMs and account executives can find instant answers by asking questions of the AI in Slack.
Impact:
By reducing internal ticket volume, support teams unlock improvements in areas like:
Since support agents aren’t pulled in to handle internal tickets, most other support efficiency metrics also see improvements.
Hiring and training new team members is costly, and customer support teams often deal with high turnover. Fortunately, enterprise AI makes hire onboarding easier by centralizing knowledge and making it easy to search and interact with.
AI won’t necessarily replace your entire new hire training program, but it will significantly speed up your ramp up times because there’s no longer a need to frontload new hires with tons of product and process knowledge.
Who it’s a great fit for:
Fast-growing companies and remote teams typically see major improvements in ramp-up time and the impact new hires have once they’ve introduced enterprise AI. Of course, any organization struggling with high turnover rates will also benefit.
Real world example:
Organic marketing platform Conductor saw dramatic improvements in agent ramp-up times after using Ask-AI to connect nine different tools and multiple support ticketing systems. While they keep their exact metrics confidential, they’ve shaved anywhere from several weeks to months off the time it takes for new hires to become fully productive members of the team.
Impact:
In many ways, this example represents the sum of all the previous workflows. Because AI centralizes knowledge, makes it accessible, identifies knowledge gaps, and makes team members more efficient, even the newest hires on your team become far more impactful and productive. By leveraging AI, support teams are seeing:
We’ve covered seven practical, real-world ways that AI is impacting customer support teams, from slashing handle times and improving self-service to uncovering insights and fully automating workflows.
You’ve seen real examples of how these AI-powered workflows empower teams to operate more efficiently, save time, and cut costs, while creating happier agents and more satisfied customers.
Here’s the thing I want to emphasize: these are real impacts and solutions to real problems that customer support teams are experiencing today.
These seven workflows are just a glimpse into what’s possible when you implement enterprise AI. Every support organization has unique challenges, processes, and tech stacks, but today’s AI is flexible enough to be tailored to address those needs.
While you’ve already seen some examples from current Ask-AI customers, our enterprise AI platform can help in all these areas (and more). We’ve partnered with mid-market and large companies across industries—like HiBob, Island, Own, and Monday.com—to streamline workflows, unite siloed data, and create better customer experiences.
I said it at the beginning of this guide, and I’ll say it again here at the end:
AI is a tool that should solve real problems for your business. It should make customer interactions smoother, your team’s life easier, and your company stronger—without introducing a ton of complexity or taking months to get started.
I hope this guide sparked some new ideas for how your customer support team can benefit from AI. If you’re curious to learn more or are interested and aren’t sure what the right next step is, our Ask-AI team would love to help you figure out what makes sense for your business.
Alon Talmor
CEO, Ask-AI
