With all the new technology at our finger tips, you’d think email would be old news by now. Instead, it’s still very much running the show in the world of services. Despite being clunky, manual, and prone to mistakes, email is still how most tickets and cases are resolved.
Here’s the thing, this reliance on emails is hurting your bottom line. Someone has to physically open your email, figure out what it’s about, route it to the right team, and trigger the right action. That’s all before anything gets logged or teams can start to work on, y’know, the actual service.
McKinsey puts the email cost into perspective: Employees spend up to 28% of their week just reading and replying to emails. In service delivery, this means an unsustainable productivity drain.
Why email still dominates B2B service delivery
Email is still around because it works. It’s familiar, simple and easy for customers to use. It suits the messy, open-ended nature of real-life service requests, where no two issues look the same. And, most importantly, you can’t force people to stop using it. Try funneling clients through rigid portals or clunky ticketing systems, and they’ll go around you. If your process is slower than their inbox, they’ll ignore it, and who could blame them?
But the reality is, shared mailboxes become a pick ‘n’ mix of unstructured and inconsistent requests. While your customers may prefer to use email, there’s no reason for your teams to be stuck sifting through thousands of manual messages on outdated systems. Spending valuable time reading, interpreting, tagging, and forwarding emails, before the real work even starts, is no way to run a service.
Why most email automation efforts fail to start at the start
Most email automation strategies skip the beginning of the process. Automation kicks in once the task is clearly defined. Workflow tools route work once it’s neatly packaged and labelled. But that’s not where real service delivery starts.
Automation should start in the inbox. To deliver a service, first you need to work out what the request is. That means someone has to read through all the email, interpret the requests, understand the context, and decide what to do with them. None of that process is automated, and none of it scales.
If you’re only automating what happens after triage takes place, you’re missing a crucial piece of the puzzle. Meanwhile, the real drag on your margins is happening earlier, buried in shared mailboxes, waiting for a human to make sense of it.
What AI can do today
Here’s the good news. AI can help you process inbound service email at scale, right now, in live environments. Here’s how it looks in practice:
- AI categorises by service type, reading emails and attachments to work out what the client needs and where it should go. No guesswork or manual handoffs. It sets the SLA, finds the right team, and makes sure the work kicks off with the right data.
- Sentiment analysis flags urgency, spots unhappy customers, and highlights churn risks or upsell moments. It tracks sentiment trends over time and across regions, turning cultural nuance into actionable insight.
- Auto-triage and SLA assignment happen instantly. AI pulls what it needs from the message, triggers the right process, and gets it to the right person with the right priority. No more delays while teams figure it out manually.
- Real-time insight into customer health means you’re not waiting six months for an NPS report. You can see how sentiment shifts week to week and act fast, before the relationship starts to go sideways.
How to make progress without ripping everything out
One of the biggest blockers to AI adoption is the myth that you have to start from scratch. Burn down your tech stack, get in a crack team of data scientists, and spend months training custom models from the ground up. It’s expensive and time-consuming, and (in most cases) entirely unnecessary.
The smarter move is productised AI. These are pre-trained off-the-shelf models, already fine-tuned on real-world data, that plug directly into your existing workflows. These proven tools focus on solving focused problems such as interpreting inbound email, categorising requests, or assigning the right SLA to the right resource. They’re built to scale and work just as well for a small business as they do for a Fortune 500.
Take email orchestration. It’s one of the fastest ways to see impact, with tools like auto-triage, sentiment analysis, and SLA assignment creating a better flow from inbox to action.
This kind of AI is built for running modern services. Tools that really understand how your work gets done and what needs to happen next. You don’t need to train a model to understand your processes. The right solutions already exist, and they’re ready to be used in the services you run today.
Email = The quickest way to get value from AI
MIT released a study revealing that 95% of businesses are yet to see return on investment from AI initiatives. I’d counter this argument by suggesting these businesses start with using AI in email environments.
AI is ready to use in service environments. It can bring structure to the noise, extract meaning, and trigger the right action, before a human ever needs to lift a finger. That means quicker turnarounds, less time lost in the weeds, and an easier way to grow. If your AI strategy doesn’t start with email, you’re missing a trick.
