Why Enterprise AI Products Fail: Training Data to Talent

Why do enterprise AI products fail? 

An estimated 70-80% of AI projects fail, with Gartner indicating that just 15% of use cases leveraging AI would be successful in 2022.

The good news is, the failure points of AI projects have now become reasonably predictable. Below are the top reasons AI products fail.

AI Challenges: Training data

In a Gartner CIO survey, 53% of organizations already rated their ability to mine and exploit data as “limited”. Worse still, training data is particularly difficult to manage, typically requiring tricky curation of largely unstructured data, tedious human oversight, and costly labeling and review. 

The knock-on effect? Poorly trained models, an agonizingly slow development process, and a final product that blends into the crowd. 

With this in mind, AI success stories share one thing in common: a rigorous data collection process that is unique to their use case, continuously updated, and sufficient in scope to produce accurate models. 

On training data, V7 Co-Founder Alberto Rizzoli states,

"AI is the process of compressing training data. This data distribution represents the "knowledge" of an AI model. If a task or user interaction isn't represented in the distribution, the AI will fail. Labeling the right amount of data to contribute to AI knowledge is an exhaustive process, yet the primary correlator of model accuracy. Without the right training data, there is no engineering process that can save you".

For those with a robust data pipeline, the challenges don’t stop there. Extracting value from your training data is often stifled by lacking or complex tooling, the requirement of expert annotators, and product development pipelines that drag on for months on end. Competitors race ahead, development slows, and team morale depletes with each and every hurdle.

So how do AI success stories overcome this challenge at scale? 

Overwhelmingly, businesses tackle the training data challenge with fit-for-purpose tooling, modular infrastructure, and integrations that ensure development remains at the precipice of AI advancements. Integrating tooling and best practices helps you extract value from your training data with superior accuracy, pace, and reliability. Increasingly, purpose-built training data platforms, that offer features like consensus-based labeling and configurable QA workflows, can be force multipliers to the value of your annotations, while dramatically reducing the cost of using them.

While the early days of AI development saw businesses build their own platforms - mostly out of necessity and a lack of better options - the AI Infrastructure Alliance recommends against building your own solution in 2023, citing the complexity, costs, and resource-intensive work that goes into home-grown platforms.

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Pro Tip: Preparing to build your own AI product? Sign up for a copy of our 2024 Guide to Building Enterprise AI Products.

Training data is the backbone of your AI product development, intrinsically defining the performance and functionality of your final model. Handled correctly, and you have a phenomenal opportunity ahead. Mismanaged - you risk spiraling costs, underperforming development, and a lackluster launch that renders your efforts wasted. 

Take it from us: as the likeliest point of failure for AI products, have a laser-focused approach to your training data. Your ability to compete relies on it.

Experienced V7 account executive Joey Blacks writes,

“Having worked with dozens of Enterprise Architecture teams looking to build their first enterprise-wide standard for training data management, as well as individual AI product teams left to 'figure it out' for themselves, it's clear that there's a still a stark difference in how enterprises treat training data compared to other data products.

Those that can successfully establish rigorous solutions and shared best practice for their training data pipelines will have an advantage when it comes to embedding AI in their business.”

Training data: Takeaways

• Quality data is crucial to the success of your product.

• Unique data will be your competitive edge as an enterprise.

• Develop a strategy to capture and structure quality data from the outset. 

• Be strategic about how you extra value from your data.

Solving the wrong problem

Another common reason for AI product failure is poor product market fit. While AI promises a host of benefits, it can be easy to get lost in the weeds. Rather than seeing your model as the final product, it’s crucial that you take a holistic approach to AI-product development, ensuring it directly benefits company-wide goals. 

It’s crucial to ask: What problem are you actually trying to solve? And, your follow-up question should be: Why is AI the best solution for that problem? Not only will this help you get stakeholder buy-in, but it will also help you to define what success looks like for your AI-powered product. 

It can be exciting to ideate the myriads of problems AI can solve within a business, however, few problems solvable by AI today are true “painkillers”. It’s important to focus on whether a problem at hand brings measurable value to the business, or is a “vitamin” (a nice-to-have). 

Countless AI products have failed on launch, as a result of decision-making that looked past a data-driven approach and instead favored the alluring promises of AI hype. 

AI can, and will, fuel your ability to deliver seismic shifts to your industry. However, to do this, you’ll need to truly interrogate the what, why, and how of your project. Countless companies get lost in the technological bubble, and fail to meet the interests of the market in the process - ensure you’re not one of them.

Training data operations consultant, Kevin Chang, weighs in,

“It’s crucial to think about the need and the use case. 

Let’s take context. The context in which your model is being consumed is the most important factor in determining what kind of model you’re training in the first place. 

If it’s being hosted on a compact edge device, you don’t have all the memory in the world to play with. If it's a consumer product, latency matters. If it's hosted on a supercomputer in a research center somewhere, you can take your time to focus on performance. 

Until you’ve defined this and communicated it between model development and model consumption, you’ve set yourself up for failure. Start with the need and the use case.”

Solving the wrong problem: Takeaways

• Secure product-market-fit. 

• Use platforms that allow you to rapidly create POCs/MVPs to test your hypothesis.

• Define the ROI for the business.

• Holistically treat the project like any other software project.

Time and costs

Countless projects fail thanks to hidden costs, missed deadlines, and an unwieldy product development process. In many cases, businesses underestimate the time and expense involved in creating an AI-powered solution and find themselves hemorrhaging cash due to an ineffective AI assembly line.

Worse yet, these businesses fail to earn market share, surpassed by competitors that beat them to the market with a solution that made better use of AI and was more cost-effective.

When building an AI-powered product pipeline, it is crucial that you address points at which time and resources can be lost. Fortunately, out-of-the-box solutions exist, with an express ability to monitor, control, and save time - saving costs in the process. 

Lacking resources: Takeaways

• Bypass the time and financial investment of building an AI solution, and opt for an out-of-the-box platform that prioritizes easy integration.

• Automate components of the annotation and QA process with reliable models.

• Ensure expert labelers are only assigned the most crucial of tasks. 
  
  

On cost savings, Co-Founder Alberto Rizzoli writes, “AI re-invents itself every 18 months with major breakthroughs. As such, time is your biggest expense. Building on a "previous generation" stack leads to worse products, and a loss of talent that wishes to push the bleeding edge. The two biggest time sinks in machine learning are data labeling and internal tool development, both of which can keep progress hostage for months. Outsource these whenever you can.”

Lack of talent

Another big cause for failure is an inability to secure or retain top AI talent. An enterprise AI project will invariably require exceptional AI, machine learning, and computer vision talent. Unsurprisingly, this particular candidate pool is hotly sought after, which means your ability to retain and reward your people will be paramount. 

In the context of product development, few things are so demoralizing as a failing development pipeline. Innovation grinds to a halt, teams waste weeks on ineffective tasks, and fail to realize the impact of their hard work through market realization. 

63% of businesses claim the largest skill shortages are in AI and machine learning.

The average income for an AI engineer in the US is between $78,000 to $150,000, but graduates from leading AI labs have starting salaries of $200,000 and above.

Fewer than 10,000 people have the skills necessary to become AI experts, which often come from building AI.

Talent takeaways

• Equip your people with the best tools for the job, ensuring they can collaborate, focus on quality work, and reap the rewards of their efforts.

• Set up safeguards to mitigate against model failure rates, product recalls, and plummeting morale.
  
  

On the crucial importance of computer vision talent, V7 Head of Sales, Matt Brown states, “Hiring quality ML talent is almost impossible, but when world-class developers have been identified, it is every leader's responsibility to equip them with best-in-class tooling that ultimately keeps them motivated and successful, and drives up staff retention rates.”

Poor adoption and lack of commitment

AI adoption can be an uphill battle, intensified by disparate approaches to development, sprawling tools, and silos of development. Worse yet, developing an AI product pipeline is expensive, so it’s crucial that you squeeze every last ounce of value out of your tech stack. 

AI products fail time and time again, thanks to poor buy-in, needlessly complex systems, and no repeatable process for success. With this in mind, it’s crucial that you implement systems that are inviting to use, easy to investigate, and promote a unified approach to development.

Adoption takeaways

• Take a look at your existing tech stack - how can it be integrated into your AI development process? 

• Consider AI platforms that manage multiple stages of the process. Crucially, you need to ensure these platforms will easily integrate with a broader AI ecosystem.

• Consider the usability of your process - does it require a lot of training to get started? Does it provide clear oversight from a strategic and performance level? Does it make the day-to-day tasks of your AI team worthwhile?