The 3 things early AI pioneers are doing
Dawid Naude, Director, Pathfindr
Our clients are trailblazers of AI, and it’s clear what the leaders are doing.
Number 1: Getting some practical AI training for their team
Interacting with ChatGPT is not a normal thing for a person to do, we’re not used to new things like prompt engineering and GPT’s. In our experience, without any training, it will get 10% maximum adoption. Whether you’re using Copilot, ChatGPT or Gemini, it doesn’t matter. You need to get a great trainer in and inspire your team on all the incredible things these tools can do. That will flip the mindset.
Number 2: Fix a process
Copying/pasting into ChatGPT in different ways by different teams isn’t ever going to get more than an incremental improvement, but mapping an entire process, say your customer onboarding or complaint management process, and then seeing how long it currently takes, mapping the ways AI can improve each of those steps, finding the right tools or building your own solution will have a radical impact, and in some cases of over 90% improvement.
This will also be far more measurable, you can tweak the process, and get multiple team members using it.
Number 3: Get the CEO driving it
I love how we’re seeing videos of CEO’s speaking different languages, embracing this tech. It’s the only way you’re going to lead a change, start driving it from the top. Finally, get the senior leadership team to identify their top 3 initiatives and pitch it to the CEO, the top ones get funding. Put it clearly on the agenda.
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.
10 Boring Problems
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.
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.
AI Assessments
Checking, marking, critiquing, assessing, scoring We recently reflected on a common theme we’re noticing in the work we’re delivering. The projects had different names like “accreditation certifier”, “clause validator” or “skilled migrant assessment”, but they were all doing the same thing, assessing something. What we also realised is that AI is often great at a first draft, or a last pass. In this case, an initial assessment.