Becoming an AI-driven enterprise sounds amazing—leverage your organization’s data to optimize your processes, reduce extraneous costs, and maximize business value.
However, adopting AI and ML throughout your organization isn’t as easy as building a machine learning model and pushing it into production. Even getting models into production in the first place can be fraught with challenges.
With so many roadblocks in the way of AI adoption, enterprises often end up not getting the most out of emerging technologies—leaving cost savings and extra profits on the table.
The good news is that this doesn’t have to be the case. In this post, we’ll walk through common challenges businesses face when adopting AI and how you can set your enterprise up to overcome them.
Tips for Enterprises to Combat 5 AI Adoption Roadblocks
Are you facing obstacles when trying to get your AI projects off the runway? We’re here to help.
1. Lack of Data Science Skills
Most organizations don’t have a full team of full-time PhD data scientists at their disposal, so even if they have excellent use cases outlined, they don’t have the internal talent needed to put them into action.
Other enterprises might have sufficient data science talent to get started, but that team operates in a silo, and their lack of business understanding makes it difficult for them to produce impactful models.
How to Overcome
For those organizations who don’t think they have the internal talent, we’re here to prove you wrong. You don’t need a team of Python-coding ninjas to make an impact with data science. Instead, you can focus your resources on upskilling your existing talent, which can look like:
- Leveraging an SME’s deep business expertise to inform data science problems
- Providing non-data scientists with a platform that allows them to build ML models intuitively (read: without code)
- Investing in improving everyone’s data literacy and analytics know-how in the org
For those that do have in-house expert data scientists, we have news for you, too—upskilling goes both ways. In fact, it’s better to prioritize upskilling your data scientists, too. When your data scientists understand the business problems their models are supposed to solve for, they’re able to do more tailored work.
2. Disorganized Data
Most enterprises have mountains of data at their disposal, but oftentimes it’s disorganized, and an extract/transform/load (ETL) process, which involves verifying, qualifying, and validating data, hasn’t been put into place. Ideally, this high volume of data would be channeled to support AI adoption, but it all too often ends up hindering it.
How to Overcome
According to Harvard Business Review, “poor quality data is enemy number one to the widespread, profitable use of machine learning.” To ensure your AI implementation goes smoothly, you need to make sure you have the right data in place to solve your high-priority problems. If you don’t have the data, how are you going to get it? If you do, how are you going to prep it?
Your team needs tools in place that make cleaning and prepping data easy. You’ll also need to set up systematic ETL processes and prioritize data management. Once you have a system in place to make the process run smoother, ensure your team knows who’s in charge of ensuring data quality.
3. Lack of Data Governance
Data governance refers to a set of policies that guide how data is collected, stored, and accessed within a business. If your organization doesn’t have guidelines in place to protect your valuable proprietary data, it can have an avalanche effect on AI adoption.
Businesses who don’t exercise control over their data will spend more time tracking down misused data than improving their internal operations.
How to Overcome
Before you’re able to make an impact with your data, you need to know where it comes from, how it moves throughout the organization, and who can access it.
Implementing an effective data governance strategy starts with establishing a data architecture that makes data easily accessible, ensures your systems are scalable, and offers flexibility so your solutions can run in a variety of environments.
Another key aspect of data governance is adhering to compliance standards and procedures. These protocols are constantly changing, and it’s best to have dedicated data leadership involved to keep stakeholders informed and ensure compliant processes are implemented throughout the org.
4. Fear of Failure
Adopting AI throughout the enterprise has a high monetary barrier to entry as well (according to recent research, custom AI solutions can cost upwards of $300,000 to implement). Despite the huge investment, AI doesn’t always pay off in the way businesses hope.
In fact, less than 1% of machine learning models have their desired impact. For many, AI is a risky venture, and leaders are wary of taking the leap with no guaranteed results.
How to Overcome
The numbers don’t tell the full story. Sure, a custom enterprise AI solution can get expensive, but most data science platforms won’t set you back $300k. And, while 99% of ML models don’t have their intended impact, it’s not because AI is inherently prone to failure.
There’s a much higher success rate for enterprises that follow a few key guidelines for building and deploying effective models:
- Don’t overlook ModelOps: You need a streamlined way to manage, deploy, and deliver tangible insights from your models
- Put your use cases at the center of your models: Employing strategies like value-sensitive scoring allows you to determine your models’ financial impact before they’re deployed, so you can decide if they’re worth the cost
- Invest in transparent solutions: A data science platform should allow you to document your entire process so anyone can modify and understand your workflow
5. Lack of Trust
Let’s unpack that last point a little bit more—because when your team doesn’t have visibility into what your models are doing, it’s only going to exacerbate any lack of trust and organization-wide buy-in in AI initiatives.
Data science is capable of making incredible changes at organizations—from solving supply chain inefficiencies to providing tailored marketing campaigns to customers.
But, if stakeholders haven’t been exposed to AI, they might fear it taking over human jobs, adding more work than it’s worth, or being too difficult to understand.
How to Overcome
Whether you’re using explainable AI to break down black box models or using data visualization techniques to show stakeholders what your models can do, creating a basic level of understanding is key.
This takes us back to the first point about upskilling—when you enable more people to feel confident working with data, you eliminate the pervasive lack of trust in these solutions as well.
You’ll also need to ensure your models are avoiding bias and operating ethically, which can look like:
- Defining which datasets were used and why
- Ensuring that those datasets represent diverse scenarios
- Outlining your AI system’s logic and explaining its impact on various departments
- Describing how the models enhance efficiency and profitability
To Wrap Up
We never said adopting AI throughout your enterprise would be easy, but when it’s done right, you’ll reap the rewards for years to come. It can be daunting to embark on an AI implementation—especially if you don’t have the tools in place to support your team as they learn how to best integrate data science throughout the org.
To get a more in-depth look at what key platform criteria you should look for in a data science platform, check out our Buyer’s Guide to Enterprise Data Science.
