10 boring problems that AI solves

Dawid Naude, Director, Pathfindr

Whilst we talk about ethics, avatars, deep fakes and other magical technology, the reality is that for most businesses, the value in AI will come from solving very boring problems.

The average desk worker has a lot of boring tasks. There’s a substantial amount of download, copy, paste, save-as, etc. There’s a lot of responding to messages that could be found on the website. There’s a lot of vlookups and pivottables, meeting minutes and deal updates.

We love solving boring problems, and get less excited about deep fakes and avatars.

So here are the top 10 that most businesses can do today.

1. Answer common queries on your website, or inbox
Chatbots have always sucked, they don’t anymore, and it doesn’t even need to be a chatbot. “What happens with my data” can be entered into a simple box at the top of your FAQ section. You can also have it monitor your company inbox and automatically answer your inbox and give a link “didn’t ask your question, click here to pass to a specialist”.

Chatbots always used to suck, they don’t any longer if they’re using an LLM. Red Cross Lifeblood Australia’s ‘Jamie’ chatbot is a great example of an LLM-powered bot.

2. Process invoices
Right now there are thousands of people looking at an invoice, then entering that invoice information into a finance system. They’re also making sure it reconciles with the original purchase order. This is ridiculous. There are excellent tools like Affinda that are specialised to read invoices and process them accurately, and if it doesn’t get a good read or needs a human, it can ping them on MS Teams, Slack etc, just as a person would. So the future state is that the invoice inbox is monitored by a bot, the invoice is created in the ERP and job done.

3. Summarise calls in your call centre
This is incredibly low hanging fruit. Most contact centre agents spend 2 minutes summarising the call at the end, and it’s subjective, then there’s an auditing process too. All of this can be automated and is low risk, as the agent still approves the notes as a reflection of the meeting.

4. Respond to RFP’s
Responding to a tender is a horrendous process. It usually involves digging through countless documents for relevant information, and often just boils down to sending emails “hey can you send me the Wilson proposal, I think it was a pretty good one right”. Imagine if a bot went through all of your case studies, certifications, content and created the first draft. This is very achievable. Imagine going from the first draft in 10 minutes instead of 3 days.

5. Create narrated videos, for internal or external
How amazing is the above video? Anyone can create rich videos and audio commentary with tools like HeyGen. The Real Institute of Western Australia drove a sell out crowd to their innovate conference with an AI generated avatar of the CEO Cath Hart announcing the event.

6. Update all your SOP’s and documents
SOP’s are out of date and the process is laborious to update them. One customer recently updated all their HR documents, SOP’s and policies in a matter of days using AI, they were able to feed in each policy and state the changes that needed to be made, it would generate the new one and they’d approve the changes.

7. Remove the need to access dozens of systems
“AI is the new UI” is one of those cringe phrases but I do get it. AI is the new user interface, as in chat. Instead of needing to log into your HR system a few times a year, or get your tax statement from another system, you can simply just enter it into a chat that’s on your MS Teams, that’s fully integrated with permissions.

8. Review NDA’s
NDA are important, but you usually need a lawyer to review them before signing one, or technically you should at least. All they’re looking for are a few key issues - why not get an NDA bot to review it instead? You upload it, it’ll tell you if it’s approved or needs formal human review.

9. Create internal and external comms
ChatGPT is not a great tool for creating internal or external comms. It’s a generalist LLM tool and so it requires a lot of prompt engineering to get it in the right tone, but specialist tools like Jasper absolutely are excellent at this. With Jasper, you can give it the previous market commentary reports and tell it to write in that tone going forward. Or internal announcements in your CEO’s voice, or formal point of view in your corporate tone.

10. Automate requirements gathering.
This one is a bit more ambitious but the payoff is massive. Most requirements gathering for any project comes down to a business analyst asking “what do you do today” or observing how they work. Instead, get them to long from complete this into a survey “describe all the systems you use, how you use them, and what the issues are”. Send that to your entire staff. Or better yet, ingest the transcript of all of your previous contact centre phone calls, and automate all the requirements off of that.


You could roll out all of the above in under 3 months and get a material, measurable benefit to your organisation ranging in the thousands of hours.

Other Blogs from Dawid

5 No Brainers

For all the hype around AI, it’s often not clear to business where they should get started. Some convince themselves they have a plan, you’re in this category if you say “We’ve given some people access to MS Copilot”. Let me guess - they seem to like the MS Teams meeting summarisation right?

AI Poor Data

I don’t know if it’s simply a convenient answer whilst there are hundreds of other competing priorities, or if it’s a misinformed opinion, but “our data isn’t ready yet to take advantage of AI” is something we hear regularly. Or similar variants like needing to spend 2024 getting data ready and then they’ll reassess AI in 2025.

How to think about copilots

Bots, like people, have two types of usefulness - knowledge and skills. Things it knows, and work it can do. In this Blog, Dawid Naude of Pathfindr provides practical advice to help you roll out or review your deployment of co-pilots and chatbots within your organisation, to generate both a short time impact and longer term strategic value.

Use whats in front of you

The first step your company should take is learning how to use ChatGPT properly. Very few do. There’s a lot of talk of AI, and businesses need to start tinkering right away. There is a huge opportunity accessible right now, right in front of you. You can completely automate processes with autonomous agents, have all your content created automatically for your learning management platform, and even enable the equivalent of a data science team with a few clicks.

AI Value not in Chat

Chat is only incremental The biggest mistake you’re making right now is thinking about AI in terms of ChatGPT, or chat interfaces. These assistant tools are great generalist support applications and a demonstration of the incredible power of LLM’s. But. They are a single step in a process, typically involving a lot of Ctrl+c, Ctrl+v, a lot of prompting, trial and error, to get a consistent outcome.

How accurate is AI

Is it accurate? Along with “Is my data safe”, this is the most common question about AI. We’ve all experienced mild embraces with inaccuracy, possibly even hallucination of AI models. So how accurate is AI, let’s get a bit more specific.