7 Reasons Why AI Projects Fail

Just about every company today is looking to implement artificial intelligence (AI) to improve productivity, increase customer satisfaction, and accelerate revenue. But according to Gartner, “Through 2020, 80% of AI projects will remain alchemy, run by wizards whose talents will not scale in the organization.” And VentureBeat AI predicted that 87% of AI projects will never make it into production.

So how do we reconcile the transformative power of AI with the realities that a vast majority of AI projects will never produce results?

In our work with customers over the past few years, Ople has found a number of reasons why AI projects fail. This post outlines some of the most common reasons, and illustrates how the Ople Platform and automated machine learning (AutoML) helps organizations of all sizes overcome these obstacles to transform their business with AI.

Obstacle 1: Not Enough Data Scientists

It seems that today just about anyone who has ever worked in data analytics is labeling themselves a data scientist after taking a short course online. The reality is that experienced data scientists are needed to handle most machine learning and AI projects. Inexperienced “data scientists” often lead to false starts, bad models that look good, and lots of wasted time. 

But hiring data scientists is difficult – if not downright impossible – in today’s current economic environment. These highly-trained resources are scarce and very expensive. And for good reason… data science is a complex vocation that requires years of math, statistics, and programming skills to become proficient, let alone master.

The Ople Platform is a true AutoML solution that helps companies of all sizes overcome the data scientist shortage by automating the data science process, from data ingest all the way to deployment. With Ople, business analysts and other users who understand your business and your data simply load the data, ask business questions, and Ople delivers the right predictive model for your business challenge.  

Obstacle 2: Lack of a Data Strategy

One of the biggest issues in getting AI projects launched is the lack of a cohesive data strategy. Developing a solid data strategy before you start modeling is critical. You need to map out the data you have, calculate how much data you will need, strategize on how to bring all of the data from disparate sources together, and finally, plan out how to clean up and transform your data.

Many companies either start off without a plan or simply don’t begin an AI project because they feel they don’t have enough data, or that the data is “dirty.” But the biggest data-related obstacle to AI success is not developing a team-wide data plan before initiating an AI project. An effective AI data plan must encompass all of your data issues and provide a sure path to obtaining the best data possible for training and testing your models.

By automating much of the data process, from data preparation to feature engineering, the Ople Platform assists your organization in this critical step to AI success. Using its own advanced AI to analyze, organize, and transform the data you bring into the platform, Ople delivers the data needed to develop highly-accurate machine learning models automatically. 

Obstacle 3: Projects Are Too Complex

Enterprises understand that AI projects are extremely expensive, in both resources and time. The cost of AI creates a tendency to focus on hyper ambitious projects that will completely transform the company and provide an oversized return on investment. It makes sense – companies want the biggest for every investment, including AI.

Called “moonshot” projects, the effort and complexity of these projects often push data science teams to their limits, and in general, have the smallest chance of success while taking the longest to complete (if completed at all). And when moonshot AI projects fail, not only is the investment and time wasted; morale suffers.

At Ople, we recommend focusing on a single, achievable project to start. By targeting a discrete business challenge, your team learns how to work together, handle adversity, and in the end, has a successful AI project to deploy. This provides a blueprint for rolling out AI across the organization. Instead of focusing on a single moonshot that is likely to fail, you deliver quantifiable success across your organization in less time.  

Obstacle 4: No Confidence in the Models Built 

Data scientists and business leaders don’t always communicate effectively. This leads to confusion when data scientists try to explain how models work and why the models should be put into operation in critical production systems. When business leaders don’t have confidence in the models their data science teams are building, these models often go undeployed.

To improve communication and foster understanding, data scientists must do a better job of not only explaining how models work, but also the business benefits driven by the results. But how do data scientists get better at communicating highly-technical models to an audience unfamiliar with the math and statistics knowledge gained over years of study and experience?  

To ensure the models developed by the Ople Platform are trusted by business leaders, Ople has built model explainability into the platform. Not only does this allow data scientists to understand why a model performs as it does, but this transparency empowers business users to understand and utilize AI with ease. When business users can build AI that’s transparent and explainable, business leaders have the confidence to deploy these models. 

Obstacle 5: Unable to Deploy Models

One of the biggest secrets of machine learning and AI space is that a vast majority of machine learning models are not deployed. Analyst estimates range from 50-90% of the models that data science teams have spent months developing, testing, and verifying don’t make the leap from the data science team into operations.

For the most part, the reason why models aren’t deployed comes down to resources. Data scientists work in programming languages like R or Python that are not compatible with the programming languages used in production systems. This means for a model to be deployed, it must be passed to the software development team to be re-coded in Java or C++. This handoff can introduce errors and requires models to be thoroughly retested and verified before deployment. This process can take months, and by the time the model is ready for production, it could be irrelevant.

The Ople Platform has been built with deployment in mind. To provide immediate insights, Ople offers a “what-if” analysis feature that lets you see how changing different features affect the predicting outcome. And with Ople, you easily deploy any model to your analytical tools (like Tableau) or to your business applications, or you can export results to a CSV/XLS file to manipulate and analyze in a spreadsheet program. Ople makes the last mile, which is historically the most complex, extremely easy.

Obstacle 6: It’s Difficult to Update Models

As time passes AI models can get stale, meaning they are no longer the best model for the situation. This happens because new data sources become available, business conditions change, or better models have been created by your team or in open source. When this happens, companies need to test the existing model against new challenger models.

In a traditional data science process, updating existing models is as time-consuming as developing the original model. It’s a manual process that takes expertise, experience, knowledge of the business, and lots of time. The outcome is that many companies keep models in production well past the point where they provide the best results.

By accelerating and optimizing the entire data science process, the Ople Platform allows your team – including business analysts and other users – to quickly generate predictive models. This includes creating new models (called challenger models) to continually test against the deployed models to ensure you are always using the best model for the challenge at hand.

Obstacle 7: Senior Management Doesn’t Want To Invest

With the high cost of developing and delivering AI projects, many companies are hesitant to invest in the needed personnel and software to deliver on the promise of AI. And that’s assuming you can find the data scientists to fill the roles in the first place.

Even with the entry of new AutoML tools into the market, there is often a need to have data scientists available to manage and verify models that are developed by these automated systems since many don’t offer transparency into how models work. There’s also a need for additional resources – both people and software – when preparing data and deploying models.

Ople helps reduce the overall AI investment by delivering an automated solution that addresses the end-to-end data science process while offering an ease-of-use that empowers business analysts and other users to create highly-accurate AI solutions. The Ople Platform is the industry’s best, and most cost-effective, solution for scaling AI in your organization.

Delivering on AI projects may seem like a daunting task, but solutions like Ople provide the tools and intelligence to help your entire team, including business users, to deliver AI that will help transform your business. Let’s get started today!