4mo ago

How can I politely phrase, "My company is full of old men in leadership who don't understand data so using AI effectively is next to impossible"

I work at a place where data quality is not on anyone's radar. We have a reporting team in our group so we do our best where we can, but combining any datasets with other groups (like marketing & sales) is next to impossible as each team is silo'd and do things their own way - think free-form text fields to tag content...

How can I politely and succinctly say the above? Also, anyone else in a similar boat?

31 comments

Implementation of AI requires strong unified data governance and data hygiene to produce company wide strategic solutions. The current company posture instead is focused on tactical level data collection and analysis which does not lend itself to consumption in relevant possible cross-department opportunities.
- And if you want to tank it without overtly tanking it.
  “We will need to establish a review and governance board to establish standard data structures and reporting that can be used to drive the initiative.
  It will need to be cross team and cross specialty so we should start by establishing a group to identify those people so we can proceed”
  A year later and you’ll be lucky if they’ve even picked out who can be part of the review process let alone agree on some convention and adjusting their tooling and processes to make that work.
- This is gold
- Somebody give this guy a promotion.
  
  I dunno. They forgot the hyphen between company and wide. 3/10.

We're unable to leverage some of the latest advances in AI due to our Leadership's abundance of caution and strategically allowable risk profile.
- You can effectively shorten the second part to “due to the company’s allowable risk profile”

They're not wanting to work better, they just want to be able to say they "use AI".
Get them an estimate of how much it would be to give everyone Copilot for Business licenses and they will start talking about the business having other priorities.

We need shared definitions to tell meaningful stories with our data. And then use a company specific example like how a customer's journey can not be understood with differing definition between marketing and sales. The marketing team can't measure the quality of the leads they're producing unless they can directly link a customer's whole journey from acquisition to churn. Otherwise it's just vanity metrics. But don't be too harsh, vanity metrics are really common in business. A company needs strong data leadership to create a culture of using data to justify decisions to a culture of using data to inform decisions.
- Definitely try to use examples to help them get a glimpse into the issue. I like to explain documentation errors by pointing out when what are supposed to be sequentially recorded timestamps are recorded out of order in my work’s database. Sometimes the data quality isn’t there.

Our team does its best to maintain consistency within our own processes, but collaborating with other groups like marketing and sales can be challenging due to siloed operations. Each team follows different approaches to data management, often using free-form fields and inconsistent tagging methods, making it difficult to combine datasets effectively.
You pretty much already had it.

"sure thing boss"
Starts looking for new job
- "yes sir"
  
  Yeah sure

Having been in a situation in which management could not quite grasp the concept of "what you're asking is literally impossible" the only answer I have to give is to leave and find someplace smarter to work.

This is what AI is for. Why don't you ask ChatGPT?

Resign, then offer them consulting on it for $300/hour.

Our legacy systems have served us well. Unfortunately, the facts of being market leaders and first movers mean we're not operationally equipped to leverage turnkey AI solutions. We will need significant buy-in from management in several stakeholder organizations, as well as significant time and resources for procurement (or development), implementation, and change management.
(optional depending on your GAF level) : With the right level-set and commitment, integrating our systems for AI could be transformative in the best ways.

www.goblin.tools is a godsend for this type of stuff.
Here's what it put out using the "Formalizer" tool set to "More professional":
Our leadership team primarily consists of individuals with extensive experience, but there appears to be a gap in understanding contemporary data practices. This challenge makes it difficult to fully leverage the potential of AI in our operations.
- Have been using this the last 2 days and love it, thank you!
  
  Hell yeah! Happy to help--it's definitely a game changer for me and plenty of others. I wish you the best luck moving forward!
  PS there is an app, and I think it's $1. Worth throwing the dev something for their efforts imo!

It is actually easier to just get a different job with better leadership. You can say exactly what you wrote in your exit interview.

Well there's a classic saying for this purpose: "Garbage in, garbage out".

Removing the unecessary adjective "old" would be an obvious start.
But what is your audience and what do you hope to accomplish? Unless you really have an audience who is likely to accept that the people in leadership are lacking an important competence and the power to go around them to get something done, then I would assume that leadership is your audience. In which case the part that blames them is entirely unhelpful and you should stick to explaining the problem and needed changes.

Why would you even bother?

“The org is extremely top-heavy”

Good luck. The company I work at has the exact same problem. Since each system tends to be owned by a different org, and the systems all meet the owning org's needs, you're going to be in for struggle.

I know you are asking for something different, but since there are already a few good answers, allow me to instead to reject the premise and give you a different.
It's not impossible to implement an AI solution within the context your provided. The problem is that it's going to be expensive. However, you can offer to deliver something smaller, focus on the smallest but valuable contribution you can make. While cleaning up the data is still going to be a hell of task, if the scope is small enough it can be achievable. Then, you can communicate the difficulty to scale due to data issues which can help management undestand the importance of prioritizing data quality.
If you have a bunch of sales data, maybe you can focus on deriving purchase patterns and build a simple recommendations engine. If you want to focus on marketing, you could try lead classification. Ideas depend on the domain of the company you work for.
- If you have a bunch of sales data, maybe you can focus on deriving purchase patterns and build a simple recommendations engine. If you want to focus on marketing, you could try lead classification. Ideas depend on the domain of the company you work for
  This is where we get the fun part of definitions. Depending on what people think AI is this aren't AI. Most people mean GEN-AI aka the new fancy shiny thing. These are boring old machine learning, data science, statistical learning, data mining etc. (depending on your definition)

AI can probably help with formatting issues, but what are you intending to use AI for?
Most of the companies that have used AI successfully have been ones with targeted goals for their AI. That your company has siloed information buckets means that each division seems to generally be operating by themselves. What is going to happen when a computer output says you need to tweak how the company is running?

31 comments