Is AI Going to Replace Customer Support?
Author: Valerie Li, co-founder and CEO of Duckie
TLDR:
My take - AI will not replace customer support but will transform it in every way, in the next 3 years.
Garry, the CEO of YC, put it in a very precise way - “The artistry of creating software or technology products is actually in that interface between the human and technology itself. Take the analogy of the camera: we made it so that you don’t have to paint anymore, but the aesthetics in the world still exist.”
AI has the power to make the work much easier, but there’s still artistry in what it should do and how it should do it. Support is a combination of science and art. There are quite lots of artistry in support.
E.g.
Customers still want that human touch and emotional intelligence which AI can't fully provide.
Support is a very collaborative process involving efforts from different teams— like engineering and product, and these interactions are very dynamic and require artistry.
The future will be people + AI = happy customers + exponential growth.
However, AI will transform how customer support teams work in every single way, enabling them to reach new levels of efficiency and customer satisfaction. With AI, many things will become attainable—like reducing ticket volume, speeding up resolutions, and boosting support quality—on a scale that was previously impossible.
I will share how we arrived at this conclusion, and how AI will transform the way support team works today.
Step 1: Figure out what the support team actually cares about
We've spent the past few weeks talking to over 20+ leaders in customer support and success, asking them: What are the metrics you care most about? What metrics do you use to evaluate your team's performance? What gets you promoted?
Here's what we found. These are the metrics that heads of support care about the most:
Time to Resolution
Customer Satisfaction
Number of Tickets Handled per Headcount
Know what the customers want
Fewer Escalations to Engineers
When we asked how they are currently addressing these pain points to improve these metrics, many mentioned hiring and training. But does this solve all their problems? All of them said no. Traditionally, support has been viewed as a cost center (which will NOT be the case anymore, if you purposely make it not be, which I will talk about in my future article), which often limits the number of headcounts allocated to the support team. However, because support is so critical, companies are willing to allocate budgets to software solutions to keep customers happy and retention high.
Step 2: Figure out what the support team needs
That’s why they are looking for toolings, to solve the problems in smarter, more efficient ways. Their tooling needs boil down to:
Enablement of Support Agents
Efficient Knowledge Sharing: Ensure support agents have quick access to consolidated and up-to-date product knowledge, serving as the single source of truth.
Empowering Technical Support: Equip tech support engineers with the expertise needed to handle complex issues independently.
Self-Served Customer Support
A System for Easy Tracking of Tickets and Insights on Customer Needs
Step 3: Figure out how AI can help
AI is advancing at an incredible pace and can really make a difference for support teams. Here are some key ways AI can directly improve the important metrics and transform customer support.
1. Customer-Facing AI Agents
The most common solution is customer-facing AI agents, like chatbots, embedded onto help sites or support portals to directly handle customer inquiries and provide instant responses.
Here are the catches though - these agents can still only handle common and repetitive inquiries, making them more suitable for B2C companies or B2B companies with a free/indie tier (not so much for serving B2B/enterprise customers, which I will dive deeper in my future article on what’s special about support in B2B SaaS companies). Another thing is, when customers reach out for support, they often want to feel like someone genuinely cares, not just get an answer. AI doesn’t have that emotional intelligence yet to give a human touch, which is why a lot of companies hesitate to rely solely on these bots.
2. AI Knowledge Assistants for Support Agents
The biggest hurdle for support agents in resolving issues quickly is the time it takes to gather knowledge, which is scattered through docs, Slack, tickets, product release emails, etc. AI assistants can consolidate information from all sources and suggest solutions, greatly improve the speed of resolution.
Now, we often hear complaints that many things just aren’t documented—new product changes, edge cases, and so on. The source of truth often comes from code changes that lead to product updates, which means the details are mostly known only to the engineers. So, how do we transfer this knowledge to support teams in real time? It’s a fascinating challenge that our team is currently tackling! ;)
3. AI Troubleshooting Assistants for tech support engineers
Logs are hard to understand and noisy. AI can also help technical support teams troubleshoot complex issues by surfacing and investigating relevant logs and providing insights into potential root causes. This allows the technical support team to address issues more quickly and independently, reducing ticket escalations and the need to rely on engineering.
4. Ticketing System with Automated Workflows
An automated ticketing system powered by AI can streamline ticket tracing and updating. Features like auto-update, auto-nudge, auto-closing, and auto-tagging streamline processes and help track ticket progress and metrics. This is especially true for B2B companies that use Slack as support channels, because Slack itself lacks such features to make it a complete support system.
5. Gaining Customer Insights
The head of support wants to know: what are my customers asking this week? AI helps gather and analyze customer insights by processing large volumes of interaction data. This allows companies to understand customer feature requests, pain points and and areas where the product may be lacking.
6. Curating the Knowledge Base
AI can curate and manage the knowledge base, ensuring it is always up-to-date and comprehensive. By analyzing customer interactions and support tickets, AI identifies gaps in the knowledge base and suggests new content.
Conclusion
Is AI Going to Replace Customer Support?
So no. My take - AI will not replace customer support but will transform it in every way, in the next 3 years. The future will be people + AI = happy customers + exponential growth.
What Is the Future of AI in Customer Support?
The future of AI in customer support is promising. Embracing AI isn’t just a trend; it's a must for staying competitive in the SaaS world. If you're looking to stay ahead in the SaaS industry, integrating AI into your customer support strategy is not just an option—it's a must.
Author: Valerie Li, co-founder and CEO of Duckie
TLDR:
My take - AI will not replace customer support but will transform it in every way, in the next 3 years.
Garry, the CEO of YC, put it in a very precise way - “The artistry of creating software or technology products is actually in that interface between the human and technology itself. Take the analogy of the camera: we made it so that you don’t have to paint anymore, but the aesthetics in the world still exist.”
AI has the power to make the work much easier, but there’s still artistry in what it should do and how it should do it. Support is a combination of science and art. There are quite lots of artistry in support.
E.g.
Customers still want that human touch and emotional intelligence which AI can't fully provide.
Support is a very collaborative process involving efforts from different teams— like engineering and product, and these interactions are very dynamic and require artistry.
The future will be people + AI = happy customers + exponential growth.
However, AI will transform how customer support teams work in every single way, enabling them to reach new levels of efficiency and customer satisfaction. With AI, many things will become attainable—like reducing ticket volume, speeding up resolutions, and boosting support quality—on a scale that was previously impossible.
I will share how we arrived at this conclusion, and how AI will transform the way support team works today.
Step 1: Figure out what the support team actually cares about
We've spent the past few weeks talking to over 20+ leaders in customer support and success, asking them: What are the metrics you care most about? What metrics do you use to evaluate your team's performance? What gets you promoted?
Here's what we found. These are the metrics that heads of support care about the most:
Time to Resolution
Customer Satisfaction
Number of Tickets Handled per Headcount
Know what the customers want
Fewer Escalations to Engineers
When we asked how they are currently addressing these pain points to improve these metrics, many mentioned hiring and training. But does this solve all their problems? All of them said no. Traditionally, support has been viewed as a cost center (which will NOT be the case anymore, if you purposely make it not be, which I will talk about in my future article), which often limits the number of headcounts allocated to the support team. However, because support is so critical, companies are willing to allocate budgets to software solutions to keep customers happy and retention high.
Step 2: Figure out what the support team needs
That’s why they are looking for toolings, to solve the problems in smarter, more efficient ways. Their tooling needs boil down to:
Enablement of Support Agents
Efficient Knowledge Sharing: Ensure support agents have quick access to consolidated and up-to-date product knowledge, serving as the single source of truth.
Empowering Technical Support: Equip tech support engineers with the expertise needed to handle complex issues independently.
Self-Served Customer Support
A System for Easy Tracking of Tickets and Insights on Customer Needs
Step 3: Figure out how AI can help
AI is advancing at an incredible pace and can really make a difference for support teams. Here are some key ways AI can directly improve the important metrics and transform customer support.
1. Customer-Facing AI Agents
The most common solution is customer-facing AI agents, like chatbots, embedded onto help sites or support portals to directly handle customer inquiries and provide instant responses.
Here are the catches though - these agents can still only handle common and repetitive inquiries, making them more suitable for B2C companies or B2B companies with a free/indie tier (not so much for serving B2B/enterprise customers, which I will dive deeper in my future article on what’s special about support in B2B SaaS companies). Another thing is, when customers reach out for support, they often want to feel like someone genuinely cares, not just get an answer. AI doesn’t have that emotional intelligence yet to give a human touch, which is why a lot of companies hesitate to rely solely on these bots.
2. AI Knowledge Assistants for Support Agents
The biggest hurdle for support agents in resolving issues quickly is the time it takes to gather knowledge, which is scattered through docs, Slack, tickets, product release emails, etc. AI assistants can consolidate information from all sources and suggest solutions, greatly improve the speed of resolution.
Now, we often hear complaints that many things just aren’t documented—new product changes, edge cases, and so on. The source of truth often comes from code changes that lead to product updates, which means the details are mostly known only to the engineers. So, how do we transfer this knowledge to support teams in real time? It’s a fascinating challenge that our team is currently tackling! ;)
3. AI Troubleshooting Assistants for tech support engineers
Logs are hard to understand and noisy. AI can also help technical support teams troubleshoot complex issues by surfacing and investigating relevant logs and providing insights into potential root causes. This allows the technical support team to address issues more quickly and independently, reducing ticket escalations and the need to rely on engineering.
4. Ticketing System with Automated Workflows
An automated ticketing system powered by AI can streamline ticket tracing and updating. Features like auto-update, auto-nudge, auto-closing, and auto-tagging streamline processes and help track ticket progress and metrics. This is especially true for B2B companies that use Slack as support channels, because Slack itself lacks such features to make it a complete support system.
5. Gaining Customer Insights
The head of support wants to know: what are my customers asking this week? AI helps gather and analyze customer insights by processing large volumes of interaction data. This allows companies to understand customer feature requests, pain points and and areas where the product may be lacking.
6. Curating the Knowledge Base
AI can curate and manage the knowledge base, ensuring it is always up-to-date and comprehensive. By analyzing customer interactions and support tickets, AI identifies gaps in the knowledge base and suggests new content.
Conclusion
Is AI Going to Replace Customer Support?
So no. My take - AI will not replace customer support but will transform it in every way, in the next 3 years. The future will be people + AI = happy customers + exponential growth.
What Is the Future of AI in Customer Support?
The future of AI in customer support is promising. Embracing AI isn’t just a trend; it's a must for staying competitive in the SaaS world. If you're looking to stay ahead in the SaaS industry, integrating AI into your customer support strategy is not just an option—it's a must.
Author: Valerie Li, co-founder and CEO of Duckie
TLDR:
My take - AI will not replace customer support but will transform it in every way, in the next 3 years.
Garry, the CEO of YC, put it in a very precise way - “The artistry of creating software or technology products is actually in that interface between the human and technology itself. Take the analogy of the camera: we made it so that you don’t have to paint anymore, but the aesthetics in the world still exist.”
AI has the power to make the work much easier, but there’s still artistry in what it should do and how it should do it. Support is a combination of science and art. There are quite lots of artistry in support.
E.g.
Customers still want that human touch and emotional intelligence which AI can't fully provide.
Support is a very collaborative process involving efforts from different teams— like engineering and product, and these interactions are very dynamic and require artistry.
The future will be people + AI = happy customers + exponential growth.
However, AI will transform how customer support teams work in every single way, enabling them to reach new levels of efficiency and customer satisfaction. With AI, many things will become attainable—like reducing ticket volume, speeding up resolutions, and boosting support quality—on a scale that was previously impossible.
I will share how we arrived at this conclusion, and how AI will transform the way support team works today.
Step 1: Figure out what the support team actually cares about
We've spent the past few weeks talking to over 20+ leaders in customer support and success, asking them: What are the metrics you care most about? What metrics do you use to evaluate your team's performance? What gets you promoted?
Here's what we found. These are the metrics that heads of support care about the most:
Time to Resolution
Customer Satisfaction
Number of Tickets Handled per Headcount
Know what the customers want
Fewer Escalations to Engineers
When we asked how they are currently addressing these pain points to improve these metrics, many mentioned hiring and training. But does this solve all their problems? All of them said no. Traditionally, support has been viewed as a cost center (which will NOT be the case anymore, if you purposely make it not be, which I will talk about in my future article), which often limits the number of headcounts allocated to the support team. However, because support is so critical, companies are willing to allocate budgets to software solutions to keep customers happy and retention high.
Step 2: Figure out what the support team needs
That’s why they are looking for toolings, to solve the problems in smarter, more efficient ways. Their tooling needs boil down to:
Enablement of Support Agents
Efficient Knowledge Sharing: Ensure support agents have quick access to consolidated and up-to-date product knowledge, serving as the single source of truth.
Empowering Technical Support: Equip tech support engineers with the expertise needed to handle complex issues independently.
Self-Served Customer Support
A System for Easy Tracking of Tickets and Insights on Customer Needs
Step 3: Figure out how AI can help
AI is advancing at an incredible pace and can really make a difference for support teams. Here are some key ways AI can directly improve the important metrics and transform customer support.
1. Customer-Facing AI Agents
The most common solution is customer-facing AI agents, like chatbots, embedded onto help sites or support portals to directly handle customer inquiries and provide instant responses.
Here are the catches though - these agents can still only handle common and repetitive inquiries, making them more suitable for B2C companies or B2B companies with a free/indie tier (not so much for serving B2B/enterprise customers, which I will dive deeper in my future article on what’s special about support in B2B SaaS companies). Another thing is, when customers reach out for support, they often want to feel like someone genuinely cares, not just get an answer. AI doesn’t have that emotional intelligence yet to give a human touch, which is why a lot of companies hesitate to rely solely on these bots.
2. AI Knowledge Assistants for Support Agents
The biggest hurdle for support agents in resolving issues quickly is the time it takes to gather knowledge, which is scattered through docs, Slack, tickets, product release emails, etc. AI assistants can consolidate information from all sources and suggest solutions, greatly improve the speed of resolution.
Now, we often hear complaints that many things just aren’t documented—new product changes, edge cases, and so on. The source of truth often comes from code changes that lead to product updates, which means the details are mostly known only to the engineers. So, how do we transfer this knowledge to support teams in real time? It’s a fascinating challenge that our team is currently tackling! ;)
3. AI Troubleshooting Assistants for tech support engineers
Logs are hard to understand and noisy. AI can also help technical support teams troubleshoot complex issues by surfacing and investigating relevant logs and providing insights into potential root causes. This allows the technical support team to address issues more quickly and independently, reducing ticket escalations and the need to rely on engineering.
4. Ticketing System with Automated Workflows
An automated ticketing system powered by AI can streamline ticket tracing and updating. Features like auto-update, auto-nudge, auto-closing, and auto-tagging streamline processes and help track ticket progress and metrics. This is especially true for B2B companies that use Slack as support channels, because Slack itself lacks such features to make it a complete support system.
5. Gaining Customer Insights
The head of support wants to know: what are my customers asking this week? AI helps gather and analyze customer insights by processing large volumes of interaction data. This allows companies to understand customer feature requests, pain points and and areas where the product may be lacking.
6. Curating the Knowledge Base
AI can curate and manage the knowledge base, ensuring it is always up-to-date and comprehensive. By analyzing customer interactions and support tickets, AI identifies gaps in the knowledge base and suggests new content.
Conclusion
Is AI Going to Replace Customer Support?
So no. My take - AI will not replace customer support but will transform it in every way, in the next 3 years. The future will be people + AI = happy customers + exponential growth.
What Is the Future of AI in Customer Support?
The future of AI in customer support is promising. Embracing AI isn’t just a trend; it's a must for staying competitive in the SaaS world. If you're looking to stay ahead in the SaaS industry, integrating AI into your customer support strategy is not just an option—it's a must.
Author: Valerie Li, co-founder and CEO of Duckie
TLDR:
My take - AI will not replace customer support but will transform it in every way, in the next 3 years.
Garry, the CEO of YC, put it in a very precise way - “The artistry of creating software or technology products is actually in that interface between the human and technology itself. Take the analogy of the camera: we made it so that you don’t have to paint anymore, but the aesthetics in the world still exist.”
AI has the power to make the work much easier, but there’s still artistry in what it should do and how it should do it. Support is a combination of science and art. There are quite lots of artistry in support.
E.g.
Customers still want that human touch and emotional intelligence which AI can't fully provide.
Support is a very collaborative process involving efforts from different teams— like engineering and product, and these interactions are very dynamic and require artistry.
The future will be people + AI = happy customers + exponential growth.
However, AI will transform how customer support teams work in every single way, enabling them to reach new levels of efficiency and customer satisfaction. With AI, many things will become attainable—like reducing ticket volume, speeding up resolutions, and boosting support quality—on a scale that was previously impossible.
I will share how we arrived at this conclusion, and how AI will transform the way support team works today.
Step 1: Figure out what the support team actually cares about
We've spent the past few weeks talking to over 20+ leaders in customer support and success, asking them: What are the metrics you care most about? What metrics do you use to evaluate your team's performance? What gets you promoted?
Here's what we found. These are the metrics that heads of support care about the most:
Time to Resolution
Customer Satisfaction
Number of Tickets Handled per Headcount
Know what the customers want
Fewer Escalations to Engineers
When we asked how they are currently addressing these pain points to improve these metrics, many mentioned hiring and training. But does this solve all their problems? All of them said no. Traditionally, support has been viewed as a cost center (which will NOT be the case anymore, if you purposely make it not be, which I will talk about in my future article), which often limits the number of headcounts allocated to the support team. However, because support is so critical, companies are willing to allocate budgets to software solutions to keep customers happy and retention high.
Step 2: Figure out what the support team needs
That’s why they are looking for toolings, to solve the problems in smarter, more efficient ways. Their tooling needs boil down to:
Enablement of Support Agents
Efficient Knowledge Sharing: Ensure support agents have quick access to consolidated and up-to-date product knowledge, serving as the single source of truth.
Empowering Technical Support: Equip tech support engineers with the expertise needed to handle complex issues independently.
Self-Served Customer Support
A System for Easy Tracking of Tickets and Insights on Customer Needs
Step 3: Figure out how AI can help
AI is advancing at an incredible pace and can really make a difference for support teams. Here are some key ways AI can directly improve the important metrics and transform customer support.
1. Customer-Facing AI Agents
The most common solution is customer-facing AI agents, like chatbots, embedded onto help sites or support portals to directly handle customer inquiries and provide instant responses.
Here are the catches though - these agents can still only handle common and repetitive inquiries, making them more suitable for B2C companies or B2B companies with a free/indie tier (not so much for serving B2B/enterprise customers, which I will dive deeper in my future article on what’s special about support in B2B SaaS companies). Another thing is, when customers reach out for support, they often want to feel like someone genuinely cares, not just get an answer. AI doesn’t have that emotional intelligence yet to give a human touch, which is why a lot of companies hesitate to rely solely on these bots.
2. AI Knowledge Assistants for Support Agents
The biggest hurdle for support agents in resolving issues quickly is the time it takes to gather knowledge, which is scattered through docs, Slack, tickets, product release emails, etc. AI assistants can consolidate information from all sources and suggest solutions, greatly improve the speed of resolution.
Now, we often hear complaints that many things just aren’t documented—new product changes, edge cases, and so on. The source of truth often comes from code changes that lead to product updates, which means the details are mostly known only to the engineers. So, how do we transfer this knowledge to support teams in real time? It’s a fascinating challenge that our team is currently tackling! ;)
3. AI Troubleshooting Assistants for tech support engineers
Logs are hard to understand and noisy. AI can also help technical support teams troubleshoot complex issues by surfacing and investigating relevant logs and providing insights into potential root causes. This allows the technical support team to address issues more quickly and independently, reducing ticket escalations and the need to rely on engineering.
4. Ticketing System with Automated Workflows
An automated ticketing system powered by AI can streamline ticket tracing and updating. Features like auto-update, auto-nudge, auto-closing, and auto-tagging streamline processes and help track ticket progress and metrics. This is especially true for B2B companies that use Slack as support channels, because Slack itself lacks such features to make it a complete support system.
5. Gaining Customer Insights
The head of support wants to know: what are my customers asking this week? AI helps gather and analyze customer insights by processing large volumes of interaction data. This allows companies to understand customer feature requests, pain points and and areas where the product may be lacking.
6. Curating the Knowledge Base
AI can curate and manage the knowledge base, ensuring it is always up-to-date and comprehensive. By analyzing customer interactions and support tickets, AI identifies gaps in the knowledge base and suggests new content.
Conclusion
Is AI Going to Replace Customer Support?
So no. My take - AI will not replace customer support but will transform it in every way, in the next 3 years. The future will be people + AI = happy customers + exponential growth.
What Is the Future of AI in Customer Support?
The future of AI in customer support is promising. Embracing AI isn’t just a trend; it's a must for staying competitive in the SaaS world. If you're looking to stay ahead in the SaaS industry, integrating AI into your customer support strategy is not just an option—it's a must.
