Making customer support automation as simple as writing a document
Our vision on automation of procedural work in customer ops
Imagine automating a complex customer support process by simply describing it in plain English and running it as cheaply as machine code, without needing to hire people for months or roll out new processes for weeks. You might not have to wait much longer!
We believe that over the next 5 years, there will be the first bank, food delivery service, or travel agency with most of its customer ops work done fully autonomously. This will unlock an unprecedented ability to scale and the agility. This new breed of companies will have a fundamental advantage when it comes to cost and quality.
Let's first consider why this was not possible until now and what has changed.
Today, repetitive work gets done manually or automated with workflows
There are two fundamental ways to complete repetitive customer support tasks in a company: through humans or machines.
Humans need to be hired, trained, and given Standard Operating Procedures (SOP) to follow.
Machines currently only execute deterministic tasks/flows that can be expressed as code.
Workflow automation tooling has been widely adopted by many forward-thinking businesses in an attempt to make automation through code simpler and more accessible. Some of these tools have a graphical interface to enable non-technical staff to contribute to process automation.
However, this approach has largely failed to replace a huge proportion of the manual, repetitive labor in customer ops organisations.
Let's consider a simple example to better understand the limits of current workflow automation approaches
"I can't login" is a great example that applies to almost all businesses that offer a digital account— it is along the same lines of "I can't signup," "Why was my transaction declined?" or "I can't find my deposit." Customers are reporting a problem which may have a variety of causes and a variety of solutions. In the average enterprise setting, more than 50% of support queries are the types of issues that require a non-linear set of steps to resolve (aka “Troubleshooting issues“).
A human customer support agent would receive guidance that might look something like this (simplified version for illustration purposes):
It's fast to write, easy to understand, and simple to edit by a domain expert. Let's now look at the same procedure represented as a deterministic dialog workflow where a customer can interact with a bot:
For simplicity, we have skipped most of the paths in that tree, however, modelling all the relevant scenarios and edge cases mentioned in the SOP above would require 60-80 workflow elements in a carefully arranged order. And that's a trivial example.
Procedural AI agents provide a vastly superior customer experience
For workflows to help customers to resolve their issues, customers need to correctly pick the triage tree entries that will lead them to the right resolution path. While this can work well in a simple linear workflow, it often leads to frustrating experiences where customers need to navigate the triage tree back and forth to find the relevant entries. I’m sure we've all been there and given up occasionally. 😊
Procedure-following AI agents provide a more fluid and enjoyable experience similar to talking to a human. At the current rate of progress, it's also easy to imagine that experience becoming superhuman in 2-3 years. AI agents will be able to figure out the straightest path possible to the resolution, and everything will happen in real-time.
Beyond the experience of executing a single workflow or SOP, what's really important for the customer experience is how those are triggered and the ability to narrow down on really relevant issues. To use the login issue example above, if the customer says, "I can't login and see a red banner in the app," should they really be shown the whole login issues workflow with all the irrelevant choices where an intelligent operator would jump straight to offering a solution? Unlike AI agent, workflows are generally not able to rely on conversational context.
Workflows are complex to create, maintain, and test
Anyone who has had to model even a moderately complex process through workflows knows how laborious and complicated it is. The writer needs to painstakingly enumerate all the possible paths in the tree and make sure there are no loops. Once created, the burden doesn't stop there; try keeping up with business changes if there are hundreds of connected boxes in front of you and you need to adjust exactly the right ones.
SOPs, on the other hand, are much more scalable because they are written with intelligent operators in mind. These operators can fill in the gaps, make common-sense assumptions, and hold relevant company and product context in their memory, which the SOP itself does not need to be explicit about. We have found that even smaller enterprises will usually quickly grow to having hundreds of SOPs documenting different customer support procedures.
Workflow tooling is too technical for domain experts (and too visual for engineers)
Despite the promise of low or no-code tools, ops staff often cannot operate workflows by themselves—they need to call in Engineers to configure the workflow to do anything meaningful. Conversely, engineers’ regularly feed back that they prefer to use code rather than draw boxes on the screen or copy & paste API endpoints into a form. Hence, the ideal user profile for workflow creation and maintenance is rare in organisations.
The solution? Procedural AI agents
At Gradient Labs, we’re building AI agents that automate manual, repetitive work. As part of this, we’re doing away with the concept of box & arrow workflows altogether. Instead, we’re developing an engine for AI agents to safely follow SOPs that are written in plain English. Drawing from the lessons above, we’re aiming for:
Customers to get a faster, more fluid, and enjoyable experience, similar to talking to a human;
Ops domain experts to be empowered to own the logic of automation in an accessible way, similar to instructing a colleague, and
Engineers to be able to give the AI agent relevant data and actions in a similar way that they ship code to production
Put together AI agents that can follow procedures (SOPs) have the potential to unlock meaningful automation that was never feasible before. Based on our estimates, it's possible to automate 70-80% of today's manual customer ops work, and most likely even more as the technology advances over the years to come. The key is to combine the simple yet expressive authoring of SOPs with the ability to execute those fully autonomously through AI agents.
If you are equally excited about defining the future of automation, either by becoming a part of our team or as a potential customer, please don't hesitate to reach out. 😊