A typical customer support experience for a customer looks like this:
- You buy a product (software, hardware)
- Face issues or need help setting up
- You’re assigned a customer rep (hopefully)
- Death 💀
If you need a reminder or a live demo of how bad this is, call your bank and ask for help.
No, I am serious do it now and tell me how did it go 😅
My point is modern customer support should not be so broken.
Instead of waiting in a long queue or explaining your problem three times to different agents, customers are entitled to get the best support possible.
Yes, customers are still KING/ QUEEN 💪🏻
I put together this piece to explore what modern customer support should look like and what role AI is playing in it.
Customer support X AI
Let’s be honest: customer expectations have skyrocketed. Nobody has time to wait, everyone wants a personalized experience and swift resolution.
You buy new software, encounter a problem, and raise an issue. Hours later, someone reaches out—not with a solution, but with more questions.
Would you like that?
NO
Nobody does and that’s the reality. That is why companies need to improve their customer support.
Do I hear the need for speed, efficiency, and 24/7 assistance?
Hell yeah!
AI-powered systems are the fastest route to getting all of the above. They’re your personal assistant who never takes a coffee break doesn’t need sleep and never forgets anything.
Not bad, right?
All of this is cool, but how will this help me?
It’s a fair question, I think AI-powered customer support has 4 clear benefits:
Reducing your costs
Hiring, training, and retaining customer service agents is expensive and may not be as necessary as you think 🤔
How? AI can handle the grunt work, so you won’t need extra hands to answer common questions or maintain knowledge bases.
Smart CS workflows help orgs to run leaner and smarter. So automating routine processes also means companies can scale without doubling their headcount.
10 extra agents for the price of one 🤑
Real-time data analysis
AI is amazing at processing vast amounts of customer data.
It can track customer sentiment, monitor conversations, and predict potential issues before they escalate.
In industries like e-commerce and SaaS, this can mean the difference between saving a sale and losing a customer forever.
To give you an idea, modern Customer Support platforms can sift through hundreds of tickets, pinpoint a common issue, and propose a solution—all before your team has finished their morning coffee. ☕️
No more “Please hold while I transfer you…”
One of the most annoying parts of contacting support is having to explain your problem over and over.
Patience is a virtue…that no one has any more
With AI, each conversation has context, remembers your previous interactions, recognizes your preferences, and will be available at 3:00 am if you need help.
Instant gratification ✅ …and in customer service, this is priceless.
10Xing agent productivity
10X is an overused term (I hate it too)
But…
AI does this for REAL. Agents are awesome, but they’re human. And humans can get bogged down by repetitive tasks like answering the same question for the 50th time that day.
AI frees agents from this monotony, letting them focus on higher-value tasks that need empathy and creativity—things machines still can’t do (at least, not convincingly).
For eg: An AI agent can update Zendesk/ Intercom tickets, raise a new bug in Slack, try and auto answer repetitive customer queries.
So with AI handling the routine, agents can tackle complex issues faster and more effectively.
Personalization at Scale
AI makes every interaction feel personal—without requiring a human to pull up your customer history.
AI can analyze preferences, purchase behavior, and past interactions, and tailor responses in real time. Whether it is suggesting the ideal product or remembering to send your invoices on the 5th of every month, AI can do it all.
Multi-channel AI Support
Today customers interact through multiple channels—email, chat, social media, and phone.
If you need top-notch service, your AI should integrate with all these channels, ensuring that no matter where a conversation starts the experience is uniform and consistent.
If you need to explain yourself 3 times you know you’re never getting an answer, EVER 😅
Types of AI customer support tools
Chatbots:
Generative AI is 100% changing the sacred held belief that chatbots are useless.
Equipped with natural language processing (NLP), they now understand the subtleties of every conversation—they get context, nuance, and intent.
They can do much more than following basic scripts or answering simple questions. They now handle multiple tasks, making interactions smoother and more intuitive.
The 21st-century chatbots can interact with customers using real-time data to find relevant information in seconds. Whether it involves resolving a shipping issue, issuing refunds, or providing a how-to guide, chatbots can solve these issues that once clogged up customer support lines.
Ticketing systems
AI-driven ticketing systems can handle end-to-end ticketing with 0 human input. They can log, categorize, and route tickets.
Why does this matter?
AI can prioritize urgent tickets and direct them to the right agent based on their skill set or current workload.
Result: Improved customer satisfaction, faster resolutions 🚀
Predictive analytics systems
Agents often find themselves playing catch-up. Typically, if a customer has a problem, they reach out and the rep resolves it.
However, predictive analytics prevents issues before they even arise. By analyzing past behavior and current trends, AI can predict a customer’s next move—whether it’s a billing hiccup or a risk of churning.
This allows businesses to proactively provide support, and address concerns before the customer realizes they have them.
(AI is beautiful)
Knowledge Bases
Customer knowledge bases and FAQs should be self-serve, especially for the questions that come often.
Why make customers wait for an agent when they can find the answers themselves?
For this to work, the customers should be allowed to ask questions like how they talk to a friend (in broken words and sentences) without the need to get each keyword right
fix my login, not working its giving an error
AI-driven knowledge bases use NLP and generative AI tech to understand questions and suggest the most relevant solutions.
I like to call these FAQ 2.0.
Customers get instant, accurate answers, and support teams spend less time responding to the same questions. Everybody wins.
Umm, does this even work?
Yes, Yes and 100 times Yes
Jotting down a few use cases where AI in Customer support has been nothing short of impeccable
Software
SaaS companies are under constant pressure to provide fast, effective customer support.
For them, AI manages the huge caseload of tickets, speeds up responses, and gives 2x faster solutions.
Sharing a few examples of how software companies are using AI
Instant troubleshooting
AI-powered bots can quickly handle common software problems, like assisting with installation or fixing bugs. These systems walk users through each step, solving issues on the spot.
This is helpful for level 1 (surface level) tickets or troubleshooting.
Internal support chatbot
As AI keeps on learning continuously from Slack channels (product, bugs, support), support docs, and Zendesk/ Intercom tickets it knows a lot about your customers and their problems
So a support agent can ask a question on their Slack regarding a bug, and the AI might try and intervene and give or redirect them to the correct answer.
This means they don’t have to wait on anyone or look anywhere for their most complex questions.
SaaS orgs often stay connected with their clients via Slack Connect. AI can help by managing all requests raised through these channels, drafting relevant responses using the knowledge base, triaging tickets, and assigning them to the right point of contact (POC).
Automated ticket management
Piggybacking on what I wrote in the above section, AI automates the ticketing process—logging, categorizing, and assigning tickets without human involvement.
It ensures that tickets are routed to the right support agent, with the context needed to resolve the issue faster.
This means customers get quicker resolutions and support teams are more efficient.
Banking
In banking, AI has become essential for keeping up with customers.
Today if a bank asks me to stand in a queue to sort out my issues, I change my bank 😂
In short, long wait times and clunky processes don’t work anymore. So banks are trying their best to adopt AI to improve customer support
Sharing a few below:
Virtual assistants
Most banks are now building their AI virtual assistants for 24/7 support. They can help check your balance or schedule a payment, and they do it instantly.
It means no hold times and no waiting for a branch to open.
They can even monitor accounts for fraud and alert you to issues before you notice them. Think of it like having a personal banker on-call, day or night.
Personalized financial advice:
Chatbots can analyze your spending, saving, and investing patterns to offer personalized advice. It can nudge you to save more or point out a promising investment opportunity, AI can recommend advice that fits your current financial situation.
Banks want to give you a financial planner who understands your habits and helps you make savvy decisions—minus the hefty fees.
Healthcare
Healthcare is facing a massive shortage of workers. With millions of new jobs expected and too few people to fill them, this led to subpar patient care management.
To bridge this gap, the healthcare industry is also turning to AI for help 💪🏻
AI-powered health assistants
AI virtual health assistants handle everything from answering basic medical questions to managing appointments and reminders. They help patients keep track of medications, offer health tips, and follow up on treatments.
So if you need a reminder for your next doctor’s appointment? AI has it covered.
These assistants lighten the load for healthcare staff while keeping patients engaged in their care.
Scaling support
Hospitals and clinics are flooded with patient inquiries—appointment requests, test results, and more.
AI steps in to handle these routine tasks, freeing up human staff to focus on critical care. Patients get answers faster, and healthcare teams can focus where they’re needed most.
Proactive patient monitoring
AI tracks symptoms, flags issues, and prompts action before problems worsen. This proactive approach helps patients get timely care and reduces the burden on doctors and nurses.
Will it be easy to overcome the bumps in the road?
Yes and No
(I swear by the end of it you’ll know what I’m talking about)
Every new tech has its challenges, and AI is no different.
Let me share a few concerns that you’ll mostly hear from folks:
- AI models hallucinate
- Is my data protected
- How do I measure ROI
- Do I even need it?
All these are fair concerns and should be addressed.
I think success lies in understanding where AI excels (and where it doesn’t) and ensuring data privacy while aiming to get maximum buy-in from the teams.
If your team is not sold on the idea, this will not work.
Always start with a small team/ project, once you see value expand internally to different teams or projects.
It typically takes about 3 to 4 weeks of ramp-up time with AI, and then the models start becoming much more accurate.
Yes, patience is a virtue and you should abide by it
Once the models are up and running – measure everything
A few metrics that should move
- First-call resolution ↑
- CSAT scores ↑
- Ticket TAT ↓
…. And never stop iterating and giving feedback to the system.
What’s next?
A few years ago, if you’d told me AI would take over and automate customer support, I would’ve taken it with a huge pinch of salt. But here we are – a few years later – we’ve gone from skepticism to automating most of the mundane support tasks, creating 24/7 virtual assistants, and even predicting customer issues before they arise.
I’m all for people’s power, but AI is doing things humans can’t manage at scale. Its ability to make customer support feel more personal, vs. generic is a superpower.
I doubt AI will replace humans in customer support. It will augment their work so they can have free time for more creative and complex tasks.
The future of customer service is proactive, personalized, and more connected than ever, with AI at the center of it all.
Time to rethink what customer relationships should look like? You tell me!
Time for a plug, obviously 😉
If you think you’re evaluating AI for your customer support team, talk to us and we can figure out if Albus can help you:
In case if you’ve any questions or need any more reading material please feel free to reach out to me at [email protected]
Catch you in the next one!
