Why Your AI Automation Will Fail (And How to Fix It First)

Why Your AI Automation Will Fail (And How to Fix It First)

Your expensive, high-profile AI automation project has a one-in-four chance of being a complete failure. Let that sink in. A full quarter of organizations report that up to 50% of their AI projects fail to deliver. That’s not a rounding error; it’s a systemic problem that costs millions and kills careers.

The hype machine tells you AI is a magic wand that prints money. The reality is that most companies are just buying expensive new ways to make the same old mistakes. They get mesmerized by impressive demos and vendor promises, rush to implement a “solution,” and end up with nothing but a bigger budget deficit and a confused, frustrated team. If you’re looking for help implementing this correctly from the start, talk to our team.

The problem isn’t the technology. It’s the strategy—or the lack of one. You’re failing before you even write a single line of code because you’re asking the wrong questions and skipping the boring, essential work upfront. This isn’t about being smarter; it’s about being more disciplined.

TL;DR: The 5 Reasons Your AI Project Is Doomed

• You’re automating a broken process. AI can’t fix a workflow that’s already a mess. It just makes the mess happen faster.
• You’re solving the wrong problem. You got distracted by a cool piece of tech instead of focusing on a painful, high-value business need.
• Your data is garbage. AI models are only as good as the data they’re trained on. If your data is a disaster, your AI’s output will be, too.
• You think AI means “no humans.” Trying to fully remove people from the loop is a recipe for catastrophic, high-speed errors.
• You forgot about your people. You can build the world’s best tool, but if your team doesn’t trust it, understand it, or want to use it, it’s worthless.

Mistake #1: Automating a Broken Process (You’re Just Speeding Up Your Problems)

A lot of people like to think of AI as a band aid – throw it on and the underlying workflow problems will magically resolve themselves. But they won’t. If your current workflow is a huge collection of ad-hoc and unsustainable workarounds, ambiguous decision points and constantly handled exceptions, attempting to implement AI on top is going to result in a faster and more expensive wreckage.

Imagine for a second your standard accounts payable workflow. You receive a dozen different types of invoices on a daily basis – from emails, through an online portal and even in paper form. Invoices have varying levels of approval required such as from a manager down to a department head and no-one seems to agree on what needs to be signed off and by whom. The end result is late payments to suppliers, vendor discontent and endless hours spent manually keying invoice information.

So, you try to “solve” the problem of manually logging vendor invoices using an AI enabled invoice extraction tool. This looks something like this: Awesome that the invoice is now automatically matched to the correct vendor and all the info is now captured, but let’s think about where the next bottleneck of human work actually goes. When the new, “auto-filled” entries hit your approval flow, guess what? The whole back-log is now injected at the exact same, very human speed it always has been. No improvement in throughput.

The Fix: Map and Fix the Process First

Getting the Processes in Order Before we can start exploring potential technology answers to our “I wish” lists we need to understand the processes that we’re looking to make change to. Simply, we need to document our workflow in a process map. Not very sexy but essentially getting all the folks involved in a particular workflow from the entry level data analyst who enters information into an application to the director who is authorizing budget expenditures to agree on the steps they complete to execute their tasks. Putting this workflow on a whiteboard makes the process visible and helps identify challenges that must be addressed.

Step 1: Document Everything

In This Step, We Recommend Documenting Out Every Single Step In The Process, No Matter How Insignificant. Questions To Ask When Documenting Each Step:

• Where does the invoice originate?
• Who is responsible for opening it?
• Where and to whom is the data contained within the invoice entered and for what purpose?
• Who reviews and who approves the invoice?
• Where does the invoice go post approval?
• Be Truthful And Transparent!

Process maps show where activities happen, but do not show where the work stacks up, where mistakes are made, or where clarification is required. An illustration of this is shown to the right. To get a real understanding of how work is flowing requires four additional steps.

Step 2: Identify the Bottlenecks and Breakpoints.

Identify where work accumulates, where errors occur most frequently, and where users need to halt flow and seek clarification. These are the points that truly reflect the source of pain in the process.

Step 3: Simplify and Standardize

As we continue through the process outlined in the prior post, we’ve now reached Step 3 of the journey. Step 3 calls for us to Simplify and Standardize the workflow that we’ve documented. So, how can we simplify the business process? Are there any steps that are redundant and therefore unnecessary? Are there any automated steps that we can put in place to make things more efficient? An example of the latter would be to establish a rule for expedited approvals for all procurements valued at $5,000 or less. Another might be to require all vendors to send their invoices to a single email address. We’ll discuss these items in more detail as we proceed.

You should only think about which activities or steps can be automated after you have automated the manual workflows. It is often quite a challenge to achieve a streamlined workflow and there are a lot of manual worksteps and activities before you can start to ask the question on which can be automated. We cover this during the process audit procedure.

Mistake #2: Solving the Wrong Problem (Focusing on ‘Cool’ Tech, Not Core Business Needs)

My warning flags have just turned red. A C suite recently had a demo of a marketing gen AI tool. Sales have recently heard a demo of the AI “predictive lead tool” that determines which leads are more likely to convert and now they both want both. This is shiny object syndrome in action. In short we are all set to burn a fortune of budget on un tested un validated useless tools.

The trap here is that you’re focusing on the technology first and then trying to work backwards to figure out what problem it solves. This is a solution in search of a problem which results in some technically impressive but otherwise completely unimportant projects. Does your company really need an AI which comes up with a few slightly different subject lines for the weekly newsletter? No, your company doesn’t really need that. Your company needs to make money.

First, put technology out of your mind. Then, make a list of your business’s most costly, most annoying, and most intractable problems. What are you losing money on? What is it that your customers are yelling at you about? What is that drudge work that is bottlenecking your operations? Write down as many as you can think of.

The “Boring” Problems are the Profitable Problems

The most valuable projects are usually the most mundane. They do not make for an interesting demo but they make a huge impact on efficiency, cost and revenue.

Consider these two potential projects:

Project A (The “Cool” Project): An AI chatbot for your website that answers basic FAQs. Although it can be quite good at keeping customers from bothering you with easy questions and it is fairly trendy. Its impact: You will see a small reduction in the number of customer service requests but this method is entirely inadequate for dealing with even remotely complicated issues. The net result is a very low ROI on this venture.

Project B (The “Boring” Project): Our inventory prediction AI discovers trends and patterns in your supply chain data to predict shortages 3 weeks ahead of time. This reduces the risk of stockout on your best selling products thereby reducing lost sales and customer dissatisfaction. The result is revenue growth and a reduction in operational fire fights, leading to a high ROI.

The Fix: Find the Pain, Then Find the Tool

Challenge number one is often the hardest one to acknowledge! It requires leadership to truly seek it out. Start by having a series of conversations with the department heads and then move on to talk with front-line employees. I suggest you ask one very simple question of everyone. Here it is: If you could wave a magic wand and fix one broken, repetitive, or time-consuming part of your job what would it be?

Listen for answers that involve phrases like:

• “A spreadsheet where they copy and paste information that later should be sent to several other applications or systems. They describe the task as very time-consuming because they need to copy the information from one application to another.”
• “We have no idea which customers are at risk of leaving until they’ve already canceled.”
• “Our forecasting is just a wild guess based on last year’s numbers.”

These are the problems worth solving. Once you have a list of your key business problems (your actual pains, not just where technology could be used), you can start to figure out if or how AI plays a role. Often the answer is “a script” or “an improved spreadsheet”, but when the answer is “machine learning”, you can be confident you have your focus on something truly important. See our custom AI solutions for some examples of how we actually approach real business problems, as opposed to just tech demos.

Mistake #3: Trusting Bad Data (The ‘Garbage In, Gospel Out’ Trap)

The maxim “Garbage In, Garbage Out” is old news to most of us. And while that’s still true, in the world of AI the Garbage In, Gospel Out problem is WAY more dangerous. What happens with an artificial intelligence system is this: a bunch of code, which is essentially a black box and completely unintelligible to a layperson (meaning the average non-technical person) produces some language, and THAT LANGUAGE BECOMES FACT because the “machine” said so. If the source information was poorly populated, lacking, or even poorly or biasedly collected, then you’re making material and very consequential decisions on unverified AI “drivel.” In other words, you’re making a ton of potentially terrible, maybe even ruinous business decisions, based on nothing more than something a digital camera would have invented as real.

Bad data isn’t just a technical issue. It’s an economic disaster. Studies have pegged the annual cost of poor data quality to the U.S. economy at as much as $3.1 trillion. Keep in mind, this isn’t about the costs of the latest AI systems behaving badly. It’s more basic than that. It’s about the fact that entire businesses are run on bad information all the time.

What does “bad data” look like in practice?

• Incomplete Data: You currently have a CRM system filled with contacts, but about 50% of them are missing phone numbers or company information.
• Inconsistent Data: Sales reps entered information with varying levels of detail. For example, some would enter “CA”, while others would enter “California”, or “Cali”. Instead of being the same value it showed up as 3 different locations. These type of problems may occur for several other data sources that needed to be used with geo-analysis for information to accurately analyze patterns at location specific levels. Even when the information was correct for a particular use, a key area for review was how data flowed between systems and what was being passed.
• Outdated Data: Inventory shows 100 units of a product that was discontinued 6 months ago.
• Biased Data: Consider a loan approval model that is created with only a historical dataset about white Americans. Now, when other demographics such as black Americans attempt to obtain a loan and their characteristics do not match up with the prior distribution, the model will heavily penalize them to avoid making the same past mistakes.

This is the data that you might use to train a sales forecasting AI. Imagine having to train a model that differentiates between “CA” and “California” and recognizes that leads generated from the former close at a dramatically higher rate than leads generated from the latter. Similarly, you’d have to train the model to disregard “OH” or “Ohio” if leads generated from those didn’t vary materially from leads generated from “Ohio” (and so on). You might also have to train the model to simply ignore entire classes of leads or customers if their data is inadequate. Your resulting forecast will be not only wrong but confusing and hard to even talk about.

The Fix: Become a Data Realist

It all starts with a data audit. And don’t even think about starting to build a model before you do it! This is a team effort too – you can’t just leave it to the data scientist to get it right. Domain experts will know their data far better than you ever will, and will be far more aware of all the weirdness and anomalies that they’ve grown used to over time. You’ve got to bring all that knowledge to the surface, so you have a clear picture of what you’re actually dealing with.

1. Know where to get the information you need. Data can live in a variety of places including a well normalized database like MySQL, or as much as we may disagree with the format, in sheets of Excel or retrieved via an API. Knowing this makes it much easier to plan out how to pull the information needed.
2. Profile your data. For each data source, try to understand the quality of the data in that source. Fields with no data, inconsistent date or address formats, and the time period that the data covers.
3. Create a Cleaning and Governance Plan. It is simply no good saying “we’ll clean it later”. One of the most important things you can do for the success of a project is to get a clear cleaning and governance plan in place for the data, from initial formatting and handling of missing values to ongoing quality control of the data throughout the life of the project.

Maintenance work is very time-consuming and very detailed. You could consider it similar to getting ready to cook, say, cleaning and preparing your kitchen. In most applications it is considered more than worthwhile to get the basics right, in order to have a good product or “meal”. With IT you risk serving a very bad “meal” if you don’t spend enough time on getting your kitchen ready.

Mistake #4: Removing Humans Entirely (The Myth of ‘Set It and Forget It’ AI)

One of the biggest and most dangerous myths surrounding AI and automation is the idea that you can fully automate a business process or function to the point where you have “lights out” — no humans needed. This is simply a myth. In the real world, nearly every business automation initiative is designed to augment human capabilities, and therefore is focused on achieving success at the human/AI intersection. In most cases, the goal of business AI is to achieve about 99% of the needed automation, leaving about 1% to be done by the humans in order to act on the work that was created by the automation. This is very different than a mythical “lights out” scenario and is therefore much more achievable.

Trying to take the humans out of the loop completely will give you one of two options: a system that is so rigid it collapses at the slightest deviation from expected input, or a system that creates havoc at a speed that makes understanding the nature of the harm almost impossible. An AI system does not have common sense. An AI does not understand context or subtle implications that are not directly trained into it.

One of the most prevalent examples of this phenomenon comes up again and again: the common “content moderation” scenario of a company deploying an AI to remove certain types of content from a website, in this case a social media platform. They train a learning model to scrub all mentions of words in a predefined dictionary of swear words and, voila, all their problems seem to go away. What they don’t realize is that all they’ve done is make the headlines this time next year infinitely worse when the media discovers that the censorship software has “accidentally” removed perfectly innocuous historical quotes, explanations of certain medical conditions that have clinical terms as part of them, and news articles about ongoing trials and court cases.

A better approach is a Human-in-the-Loop (HITL) system.

How Human-in-the-Loop AI Works

The AI does not yet make decisions. Instead, the system is designed as a filter and tool to assist and ease the work of the human expert.

• The AI does the heavy lifting: It can scan millions of transactions per second to flag the 0.1% that look potentially fraudulent.
• The AI suggests possible answers: It analyzes the content of the support ticket and offers up to three possible solutions based on the existing database of matching solutions.
• The human makes the final call: Fraud analysts examine tagged transactions more closely to determine whether they are a legitimate or malicious transaction. Support agents choose the best solution to the questions that customers send in, and they give an answer that is as relevant as possible to the individual customer.

An innovation that brings together the velocity and volume capability of machines with the unique strengths of human professionals. Experts and knowledge workers are freed from the mundane activity of wading through large datasets in search of meaningful information to focus on those few cases which are typically characterised by higher levels of ambiguity, uncertainty and criticality.

The Fix: Design for Collaboration, Not Replacement

When designing your AI automation, constantly ask:

• Where is human judgment absolutely critical?
• When could a machine error possibly cause serious financial consequences to us or severe embarrassment to us and our companies?

Decision support systems (DSSs) use the power of AI to give people who make important decisions, such as managers and executives, rapid access to the information they need to get the job done quickly and effectively.

Your goal is to establish a partnership between your workers and your system. The work related to repetitive and heavy data processing should be carried out by the AI. Your staff can then act as engineers to manage and supervise the system; quality controllers of the final product and interventions, and decision makers that use the system’s analytical capabilities to optimize the productivity of the plant.

Mistake #5: Skipping the ‘People’ Part (Ignoring Change Management and Team Buy-In)

You’ve got a great process, a high value problem, good quality data, and an excellent human-in-the-loop design. And yet your project fails spectacularly. Why? — Bec

If you’re facing these challenges and want to ensure your AI project succeeds, the first step is a solid strategy. Contact our team today for a consultation.