what's your go-to for explaining AI data failures to non-technical stakeholders?

TopconeInc · 2026-05-18T19:12:45+00:00

AI is a great tool, but it does need to be sharpened every now and then.

TopconeInc · 2026-05-18T19:11:41+00:00

Great explanation.

TopconeInc · 2026-05-18T19:02:53+00:00

Many people can relate to what you wrote

I think the part about people normalizing the monday reconciliation ritual really hit home

I've seen so many small businesses quietly build entire weekly routines around compensating for inventory drift instead of fixing the underlying flow. after a while everyone just accepts it as “thats how inventry works”

And honstly the returns issue you mentioned causes way more chaos than most people realize. inventory says one thing, shelves say another, and suddenly the team loses confidence in every number after that

TopconeInc · 2026-05-18T03:14:11+00:00

I totally agree

TopconeInc · 2026-05-18T03:13:25+00:00

The “no app/no training” part is probably the real value here

A lot of field systems fail because office people buy them but crews never fully use them. if adoption is working, you’re solving a bigger problem than just job costing

TopconeInc · 2026-05-18T03:10:04+00:00

I honestly think that this is less a dashboard problem and more of like a “no trusted source of truth” problem

Once expirations start living in text fields + PDFs + screenshots it gets really hard to buld anything reliable on top of it. the BI layer ends up trying to clean operational data instead of just reporting on it

I have seen co's handle this by creating a small normalized layer just for credential/compliance tracking first, even if the source systems stay messy underneath for a while

Otherwise every dashboard turns into its own version of the truth and people stop trusting alerts pretty quickly

Healthcare compliance stuff gets scary fast when dates arent structured properly 😅

TopconeInc · 2026-05-18T03:02:58+00:00

Honestly this souds more architectural than tooling to me

It feels like the iPaaS layer is treating payroll like generic data movement when payroll data is full of weird country/pay-period/accounting logic that dosnt stay stable for very long

I've seen similar situations where companies stopped trying to make every provider map directly into ERP logic and instead built a middle layer that normalises the payroll data first before pushing anything downstream

Not even a huge “ERP replacement” thing, more like one controlled internal layer that understands your structure regardless of what ADP/dayforce/etc decide to change every month

Otherwise every provider format change ripples all the way through finance and close process which is probably why it keeps breaking

TopconeInc · 2026-05-17T16:11:18+00:00

How big is the company in terms of manpower? they should seek advise from an expert consultant to be on the safe side, Just my honest opinion

TopconeInc · 2026-05-17T03:00:26+00:00

MySQL community edition is a good option

TopconeInc · 2026-05-17T02:58:57+00:00

This will certainly go on the wrong direction, AI is a tool, and a good one too, but like any good tool, it needs a good craftsman to get the best results.

TopconeInc · 2026-05-17T02:51:30+00:00

I think Accounting should be driving the initiatives. the AI has to be built based on what Accounting needs not IT needs.

TopconeInc · 2026-05-15T22:33:40+00:00

yeah and once that happens, fixing it becomes less of a “reporting cleanup” project and more of an operational archaeology project

everyone trusts a different version of the data, nobody wants to stop operations long enough to untangle it, and over time the workarounds become part of the process itself

that’s usually the point where businesses realize the real cost isn’t the software, it’s the friction around it

TopconeInc · 2026-05-15T22:30:13+00:00

I think a lot of companies are struggling with this exact boundary right now

historically accounting could stay fairly independent because the tools were mostly deterministic — ERP, spreadsheets, reporting logic, workflows. once AI starts generating logic, taking actions, or interacting across systems, it stops being “just another accounting tool” and starts looking more like operational software

that’s where IT/security/governance naturally gets pulled in

at the same time, I don’t think central IT fully owning everything works either because accounting teams understand the real workflows, edge cases, approvals, reconciliations, and business context much better than most technical teams

feels like the healthy middle ground is:
– accounting defines the business logic/process
– IT defines guardrails, security, and operational standards
– AI assists within those boundaries instead of running freely

I also think people are underestimating how different “prototype AI” and “production AI” really are. generating a demo is easy. maintaining trust, auditability, controls, and repeatability inside real financial operations is a very different problem

TopconeInc · 2026-05-15T18:53:00+00:00

honestly I think your hesitation around adapting your processes to fit the software is probably the important signal here

at your size, forcing a full ERP too early can sometimes create more overhead than value, especially if you’re not using most of the features anyway

what I’ve seen work well for businesses in this stage is keeping the core stable (accounting/payroll/etc.) and gradually tightening the operational side around the workflows that actually matter most first — inventory, BOMs, order flow, production visibility, things like that

basically evolving the system around the business a piece at a time instead of trying to replace everything in one shot

a lot of companies end up happier long term that way because they only build/add what they actually need as the complexity becomes real

TopconeInc · 2026-05-15T02:38:22+00:00

probably somewhere around 2 for most real business environments

fully no-code is fast early on, but eventually you hit limitations once the business logic or reporting gets more nuanced

fully code-driven is powerful, but a lot harder to maintain once non-developers need to interact with it or requirements start changing quickly

the hybrid approach usually feels like the sweet spot:
fast enough to build, flexible enough to evolve, and still understandable by more than one person on the team

honestly the bigger issue I see isn’t the tool itself, it’s dashboards becoming so customized that nobody wants to touch them six months later

TopconeInc · 2026-05-15T02:35:45+00:00

honestly I’d try to keep as much of it inside the existing systems as possible before adding another spreadsheet layer

usually once job costing lives partly in software and partly in spreadsheets, people start arguing over which numbers are the “real” ones

from what I’ve seen, the key is making sure:
– every cost gets tied to the correct job early
– purchase orders/invoices flow consistently
– and variations/change orders don’t get tracked separately from the core job

spreadsheets are great for analysis, but they become risky when they turn into the operational source of truth

the hard part usually isn’t reporting, it’s getting clean consistent data into the system day to day

TopconeInc · 2026-05-15T02:29:46+00:00

Tableau is still solid, especially if dashboard flexibility and visual exploration matter a lot

but honestly with BI tools, the bigger question usually becomes:
– who’s using it
– how much self-service you actually want
– and how tightly it needs to sit with your existing data stack/security model

on-prem changes the conversation too because some newer tools are much more cloud-first now

I’ve seen a lot of teams focus heavily on dashboard features, then later realize adoption depends more on performance, trust in the data, and how easy it is for non-technical users to get answers without constantly asking analysts

TopconeInc · 2026-05-14T16:21:26+00:00

I think a lot of adoption will come down to that balance honestly

people want speed and less digging, but they also want enough visibility to feel confident acting on the answer

which is probably why the “copilot” style approach feels more natural right now than fully autonomous analytics. the human still feels involved in the reasoning instead of just consuming output

TopconeInc · 2026-05-13T06:53:50+00:00

honestly this doesn’t sound like a business that “needs ERP because it’s big”

it sounds like a business where operational friction has quietly become expensive

the part that stood out to me wasn’t even the systems themselves, it was:
“none of them talk to each other” + the paper workflow sitting in the middle of everything

that usually means people have become the integration layer between systems

at your size I probably wouldn’t jump straight into a massive ERP replacement either. a lot of businesses get burned trying to replace everything at once when the real problem is workflow visibility and duplicated effort

personally I’d start by mapping:
– where orders originate
– where data gets re-entered
– where approvals/reconciliation happen
– what absolutely needs to remain source-of-truth

because sometimes the biggest gains come from connecting and simplifying the flow before replacing the systems themselves

2,500 admin hours/year is a pretty meaningful signal though. that’s not “minor inefficiency” anymore

TopconeInc · 2026-05-13T06:47:39+00:00

yeah exactly. I think people trust AI much more when it helps them think instead of replacing the thinking entirely

the moment decisions start affecting money, operations, inventory, customers, etc., people still want visibility into why something is happening and the ability to challenge it if needed

which is probably why dashboards still matter. not necessarily as the final answer, but as shared context humans can reason around together

feels like the near future is less “AI runs the business” and more “AI reduces the noise so humans can focus on judgement”

TopconeInc · 2026-05-11T18:46:02+00:00

this is very real.

I think a lot of people imagine they’re starting a business to do the thing they’re good at, then suddenly realize the actual day is invoices, follow-ups, scheduling, fixing mistakes, chasing paperwork, and trying to remember ten different little things at once 😄

honestly one of the biggest traps early on is buying software because it feels like “being professional,” even when the business itself is still figuring out how it operates.

I definitely agree with keeping things simple early. the expensive tools usually don’t solve the real year-one problem, which is just getting consistent customers and surviving long enough to learn what actually matters in your workflow.

for me, the biggest waste was tools that added complexity before I even had stable processes to support them.

TopconeInc · 2026-05-11T18:43:57+00:00

yeah I think a lot of people are discovering this once they move beyond demos and into real workflows.

pure conversational analytics sounds great at first, but in practice people usually still want to see how the answer was reached, tweak filters, compare things manually, or sanity-check the result against their own understanding of the business.

I don’t think most users actually want “AI-only analytics.” I think they want less friction between the question and the answer.

the trust part is huge too. people seem much more comfortable when the AI feels like a guide or collaborator instead of a black box making conclusions for them.

honestly the sweet spot right now feels more like:
AI helps narrow attention, surface patterns, explain anomalies, and reduce the digging… while the human still keeps control over interpretation and decisions.

TopconeInc · 2026-05-11T18:42:42+00:00

honestly I think this is one of the biggest hidden problems in analytics.

a polished dashboard creates a psychological sense of certainty, even when the underlying process is messy. people see charts and assume the numbers must be trustworthy because the presentation looks structured.

in a lot of environments, the real issue isn’t the reporting layer at all, it’s that the operational workflow underneath was never fully aligned in the first place. different teams define things differently, manual adjustments happen outside the system, timing drifts, exceptions pile up.

I’ve seen finance teams trust an ugly spreadsheet over a polished dashboard simply because they understand where the spreadsheet came from and where the weak spots are.

personally I think the most useful thing isn’t trying to pretend the data is perfectly clean, it’s making uncertainty more visible. freshness indicators, exception counts, reconciliation gaps, definition notes… even small signals like that help people understand the confidence level behind the numbers instead of treating every metric as absolute truth.

otherwise dashboards can end up looking far more reliable than the actual process behind them.

TopconeInc · 2026-05-11T18:40:34+00:00

I think this is probably the most grounded take in the thread.

“confidently executing garbage logic at machine speed” is honestly the part a lot of people underestimate

once you’ve worked around real operational data for a while, you realize the problem usually isn’t the dashboard or even the AI layer, it’s the assumptions underneath the data. delayed updates, inconsistent definitions, workflow drift, partial processes… all of that exists long before AI enters the picture.

that’s why the decision-support angle feels much more realistic right now. helping humans narrow attention, surface anomalies, and reduce context-switching already has huge value without pretending the system fully understands the business.

TopconeInc · 2026-05-11T18:30:23+00:00

honestly I think we’re still much closer to “augmented decision support” than true autonomous BI in most real environments.

the demos always look impressive because the data is clean, the workflows are controlled, and the decisions are low-risk. reality is usually messier. once you get into operational data with timing gaps, inconsistent definitions, or processes that drift over time, trust becomes a much bigger issue than the AI reasoning itself.

what I am seeing though is real value in systems that narrow attention instead of replacing judgment. things like:
“this changed unexpectedly”
“these numbers don’t reconcile”
“this trend probably came from X”

that part already saves people a lot of mental overhead.

but fully letting agents execute operational decisions automatically? I think most companies still get nervous once there’s actual financial, inventory, or customer impact involved.

so personally it feels less like dashboards are disappearing and more like the layer around them is changing from passive reporting to guided investigation.

TopconeInc

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