The timeline to fully automated Customer Service

Every Customer Experience and Service leader knows that AI is the future, and their future. Act now, or be left behind.

We already have AI Agents that can instantly, accurately, and reliably resolve 50%+ of customer queries for many businesses. For some, we are at 70-80%+. That alone will change an industry, but this is just the beginning. AI right now is the worst it will ever be. Technology only goes in one direction.

Every Customer Service leader is busy building the next generation of their customer experience. But it’s hard. In particular, it’s hard to keep up with the pace of technology advancement, and the implications. Get your head around a latest LLMs capabilities, and there is a new one. Understand one technical concept in AI (“I know what reinforcement learning is”), and there is another one to learn (“what is distillation?”). 

What we can say with confidence, is that on some timeline, AI is going to be able to answer close to all customer queries. Today, humans still do the majority, but the volume will shift to AI.

The challenge is that no one knows the exact timeline to that world. And depending on the timeline, how we redesign our customer experience will change a lot. Time it wrong, and we will have wasted huge resources designing for a world that doesn’t exist. We need to make an educated guess around two key variables:

1. Technology progress. What types of queries can AI accurately answer today? Soon?
2. Adoption progress. How fast/slow are people adopting, what barriers exist?

To get this right, we need to look at where we are, and what might happen next.

Where we are

History is full of examples of the technology being good enough, but the adoption being slower with some groups due to legitimate and perceived barriers. We’ve done a lot of research at Intercom to understand this, analysing millions of real customer questions, to categorise them and understand how they might be answered by AI. Broadly, there are four types of query, and the timelines are different for each one. How much of each query exists depends on the business type, but aggregated, you can think of them as 4 equal buckets of 25% of volume:

1. Informational.
   The answer is the same for all customers. “Do you ship to Ireland?”, “Do you have a free plan?”.
   This just requires the AI to access up to date knowledge to answer.
2. Personalised.
   The answer is different for each customer. “Where is my order?” “Why is this feature not working as expected for me?”
   This requires the AI to read individual customer records to answer.
3. Actions.
   The answer requires an Action to be taken. “We have refunded you”, “We have  upgraded your subscription”.
   This requires the AI to be able to read and write to customer databases, and communicate with other internal/external people/teams/businesses. 
4. Troubleshooting.
   This requires the AI to be able to understand complex customer situations, complex business logic, and complex business systems, and to be able to read, write and sometimes redesign those systems. 
   It requires the AI to be able to deeply communicate with other people (and at some point other AIs), to learn, and sometimes negotiate.

So where are we on our technology and adoption timelines?

Today, the best AI Agents (check out Fin), set up and configured well, can answer 100% of Informational queries. There is no technology barrier left here. The only barrier is human configuration, which requires CS teams to put in the effort to have accurate content, and a system to keep it up to date. AI is helping here too, with suggested content to close knowledge gaps, etc. Any CS team not trying to implement this is behind on the adoption timeline.

The best AI Agents can also answer close to 100% of Personalised queries. There is no technology barrier here, the barrier is putting in the effort to ensure high quality data, and high quality data integrations. Configuring this (and Actions below) is where many of the leading companies are right now. Fin is doing this for different types of businesses, from showing a customers invoice due date for Tibber, and using customers plan and device details for Firsty. It is possible to get very high numbers resolved here if you put in the effort.

The best Agents are starting to do different types of Actions. This is the current frontier on the technology timeline and we’ve Actions live with over 250 customers. The technology is there, and works for well understood and documented Actions. But it requires more human setup and configuration work, and often it requires business logic to be properly documented for the first time. This has been a key insight from our research and development with customers. Many businesses don’t actually have their processes properly documented. Often it is local knowledge passed on from person to person. And what is documented is often wrong or out of date.

Today, these business processes need to be manually documented so we can teach the AI what to do. But it won’t stay like that forever.

What might happen next

All Customer Service leaders should be planning and executing now for a world where all Informational, Personalised, and Actions queries are 100% resolved by AI. On the technology timeline, this world is already here, we are now working out how to configure things. If you are not here, you are behind on the adoption timeline.

Next, we get into an educated guessing game, and it is part of all CS leaders’ jobs to have an opinion here. 

AI is likely coming that will be able to do two very powerful things:

1. Work out the undocumented business logic. It will be able to analyse deep, complex systems, synthesise what it learns, and create, asking for clarifcations from people along the way.
2. Work out how to connect and code integrations itself. As well as figuring out and documenting the business logic, it will take action based on it. It will write and commit code by itself, improving the underlying systems without human intervention.

This AI is going to feel like magic. This AI is going to really change your job (and many others). 

• When do you think it is coming? 
• And what timeline are you planning for it to arrive?  

There are two timelines, you need to bet on one of them:

The Acceleration timeline: Magic AI Agents within 12-18 months

The biggest tech companies, along with the biggest AI Labs, are making huge capital investments to produce bigger and more powerful models. They wouldn’t be making these investments if they didn’t believe that the scaling laws will continue, and greater scale will give us greater power. If we take the advancements made in 2024, and extrapolate out, we might have Agents that can understand complex business logic by the end of 2025, or early 2026. The founders and leaders of the biggest AI Labs have given us these timelines.

• What does human support look like when we have AI this powerful? 
• Do we still have humans talking directly to customers? 
• Or do humans only talk to other internal humans (like finance, or engineering, or legal) and then instruct the AI Agent on how to proceed? 
• Or does the AI Agent do all of it: talk to the customer, then ask questions in internal Slack channels, then get back to the customer. 

If you think the capital investments will pay off, then you bet on this timeline, and design your customer experience appropriately, starting now.

The Slowdown timeline: No magic AI Agents for 2-3 years

AI advancement slows down. The scaling laws stop. The big capital investments don’t pay off early. We get better versions of what we have today (better reasoning models), but no breakthroughs. We still have a lot of heavy manual configuration to get high % AI resolutions. Humans with deep domain expertise and experience are the only ones who can answer complex customer queries.

Customer Service still changes a lot compared to pre-AI, but we still have very large volumes of human support.

So what should you do?

AI is advancing fast, and it’s hard to keep up. But the best Customer Service leaders are all-in, and are rebranding themselves as AI CS Leaders.

By thinking in terms of query types to automate, they are making things practical and seeing huge success. You can too:

• First do Informational (and do it now or really be left behind).
• Then do Personalised and Actions (and start now because they require deep integrations, and documenting business logic).

Find an AI Agent company you can deeply partner with. Like Intercom. We have a large, sophisticated AI team, and have been first to market with many new models and innovations. We can teach you, and we can learn together. 

Whatever you do, do it now. If you don’t have an AI Agent in beta, or live, it’s getting late. There are so many success stories, so it is almost certainly the case that your competitors are getting an edge on you.

If you are on the Slowdown timeline, and we hit the Acceleration timeline, your entire role and org might be at risk of completely disappearing before you have time to react. However, by assuming the Acceleration timeline, you can not only future proof your career, you can set a new career path, by being part of the group of people who figure this out earliest, enabling incredible experiences and levels of service for your customers.

That at least, is what I am doing.