Stop Chasing AI Hype: 3 Questions to Consider Before Investing in AI
The difference between AI transformation and expensive disappointment comes down to asking the right questions before you start
Déjà Vu: The Dot-Com Boom All Over Again
Every week, another AI vendor promises to "revolutionise your finance function." Every conference speaker insists AI will "transform everything." Every board meeting includes the inevitable question: "What's our AI strategy?"
Wait a second… where have we heard this before?
I lived in San Francisco during the final months of the dot-com boom in 1999 and early 2000. The energy was intoxicating. Everyone had a website that was going to change everything. Venture capital flowed like Cristal. The logic was simple: it's the internet, so obviously it will make money!
Irrational exuberance in 1999 and evidence AI continues to struggle with hands and the occasional extra leg
Most of those businesses had no clear path to profitability. They were chasing the hype, not solving real problems. The few that survived? They had solid fundamentals and asked hard questions before the money ran out.
Here's what I learned watching that bubble burst: The technology isn't the problem. The problem is skipping the fundamentals in a desperation to avoid being perceived as falling behind.
After 25 years in finance leadership, from that dot-com era through every technology wave since, I can tell you the pattern repeats. Companies that win assess, plan and strike fast remaining outcome-focused.
History Repeats
Today's AI conversation sounds eerily similar to 1999's internet conversation. Some providers are doing groundbreaking work that will genuinely transform how we work. Others are simply trying to grab a piece of the AI pie.
The challenge isn't identifying the charlatans, it's making sure you don't skip the foundational work that determines whether any AI implementation will actually succeed.
Question 1: Are We Actually Ready for This?
The real question: Is your data foundation strong enough? Is your leadership actually engaged to work towards cross-functional solutions or are you on your own?
Why this matters: AI is only as good as the data you feed it. If your finance team spends 60% of their time cleaning and reconciling data, adding AI won't fix that. It will just automate your input of garbage.
If you are trying to transform finance in a silo without the support of your organisation, don’t be surprised when the results reflect that silo.
What to assess:
Do your systems talk to each other, or are you exporting and importing Excel files?
How much time does your team spend on data validation versus analysis?
Are your processes documented and standardised, or does everyone have their own way of doing things?
Does your finance and tech team have a good relationship and open communication of requirements (or are they the forgotten redheaded stepchild whose requirements don’t get met because tech and data analytics are only given capacity to support the business, leaving finance to create their own unique workarounds)?
The hard truth: If your answer is ‘our data is messy but AI will clean it up,’ hahahaha I am laughing with you, my friend. With you, not at you. Bless your cotton socks.
Question 2: What Problem Are We Actually Solving?
The real question: Are we implementing AI because it's exciting, or because it solves a specific business problem?
Why this matters: AI for AI's sake is how you end up with shiny tools nobody uses. The most successful AI adoptions solve one clear problem really well.
What to define:
What manual process is costing you the most time and money?
Where do errors happen most frequently in your workflow?
What decision would you make differently if you had real-time insights?
The hard truth: If you can't explain the business case in one sentence, you're not ready to implement.
Question 3: Are Our People Part of the Solution?
The real question: How will this change empower your team, not replace them?
Why this matters: The best AI implementations don't eliminate people; they eliminate tedious work, preventing your legends from being strategic. This requires intentional change management, not just technology deployment. Adoption, not deployment. The difference is nuanced, but it is the difference between winning and losing. And let’s be real, if you are here, then I know you like winning.
What to consider:
What is the technological skill set of your current team?
How will you retrain your team to work with intelligent systems?
How will you ensure this training sticks and is part of onboarding for new team members?
What new roles and responsibilities will emerge?
How will you communicate that this is about elevation, not elimination?
The hard truth: If your team sees AI as a threat rather than a tool, even the best implementation will fail. Adoption and advocacy are everything. You chose to take this bold step forward – set the tone and lead your people. Adoption and advocacy, not edict.
If you are looking at AI thinking to yourself, this is a chance to sack your AP and AR team within 12 months and replace them with a multi-capacity agent, you are further behind than you think.
The Alternative to Hype
My advice for finance leaders: Don't chase AI; obsess over outcomes.
Start with your biggest operational pain point. Map your current process. Identify where human judgment matters and where it doesn't, then explore how AI can help.
This isn't about being anti-technology; it's about being outcome-focused.
Your Next Step
Before you book another AI demo or issue another RFP, answer these three questions honestly. If you can't, you're not ready for AI implementation but you are ready for AI readiness.
And that's where the real transformation begins.
Don't let messy data and unprepared teams turn your AI investment into an expensive disappointment. Book your AI Readiness Assessment - we'll show you exactly what needs to happen before you spend a dollar on AI tools.
Ready to build an AI strategy that respects your business and your people? Let's explore what AI readiness looks like for your organisation.
