While generative AI and machine learning tools have much to offer in terms of improving workflows, speed to market, and overall effectiveness across many applications, many organizations are wasting more time and resources chasing AI than actually executing it with meaningful results.
To succeed with AI, brands need to three major elements in place:
- The right strategies, people, and processes
- Capable technology and high-speed infrastructure
- Quality data
When one or more of those elements are lacking, it creates a significant barrier. In our work with clients and through our work with partners, we’ve seen five major types of problems come up again and again. In the 41-minute on-demand webinar below, we discuss those barriers, share lots of examples, and talk about solutions. However, in the remainder of this post, we spotlight those five barriers and summarize how to overcome them.
5 Common AI Barriers
Here are the five most common problems we see brands encounter when trying to use artificial intelligence.
1. Shiny Object Syndrome
The Problem occurs when organizations invest in AI tools before the team understands how models work and the best application for each model.
The Solution: Decision-makers must understand the proper applications of various AI models and the projected impact. Besides general confusion about the critical differences between GenAI models and machine learning models, the waters are muddied further by many tech vendors adding GenAI features to their products that deliver few benefits but help them appear on trend and innovative.
2. Order-Taker Mentality
The Problem occurs when organizations tell data scientists to create a certain model instead of explaining why a model is needed and what the goal of the model is. Conversely, it’s also when data scientists deliver solutions that business users don’t understand how to use properly.
The Solution: Business users need to understand the current strategy, define what they’re trying to achieve, and understand how the data connects to those goals. Data scientists should share insights into relationships between data and behaviors to inform business strategies, and should also work with domain experts when crafting models to avoid misfires.
3. Missing & Wrong Data
The Problem occurs when an organization understands the AI model and its application, but it doesn’t have enough data or accurate data to fuel the model. For instance, you may have data gaps or tainted data due to migrations, outages, capture failures, and other problems.
The Solution: Before deploying an AI model, understand the data you have available. Where gaps exist, explore ways to fill them. Where errors exist, replace or remove the bad data. Proper data hygiene and data capture is easier to solve up front than fix retroactively.
4. False Out-of-the-Box Expectations
The Problem occurs when buyers of out-of-the-box AI models assume that means they can plug it in and go. Often, some assembly is required.
The Solution: With any out-of-the-box model, you’ll likely need to:
- Solve for biases
- Provide missing inputs/features
- Collect enough historical data
- Tweak the model’s logic based on how your audience behaves
5. Setting & Forgetting
The Problem occurs when AI models are built with minimal documentation. Years later, when people have forgotten how it was built or left the company, it can be hard to maintain or even use the model. Relatedly, organizations can also fail to recognize the need to update their AI models as their businesses change and their audience changes.
The Solution: When leveraging AI, don’t neglect user training and documentation.
Also, recognize that AI models require maintenance and periodic retraining, especially when there are external shocks (e.g., COVID-19 pandemic, Apple MPP) and internal shocks (e.g., exiting/ entering product categories).
Artificial intelligence models are powerful, but these five barriers can cause costly deployment delays or, even worse, malfunctioning models that harm your results. Be cautious with these tools.
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