Top Challenges of AI Deployment–And How to Overcome Them

It’s not surprising that artificial intelligence (AI) and its potential impact on businesses is all over the news. AI and machine learning (ML) have been two of the most talked about technologies in recent years. And yes, these technologies have the potential to bring about a massive change in a number of industries.

But what does this mean for customer data platforms (CDPs)? As martech decision makers consider more advanced use cases of CDPs, they expect to benefit from advanced insights via machine learning, rely less on IT to manage customer data, and have improved first-party data ownership.

That was one of the key findings from a research report we jointly published with Advertiser Perceptions. The report is based on a survey of 100 martech decision makers who use CDPs. It  explores how CDPs drive value today and how decision makers expect to get greater value out of CDPs in the near future.

The report found that AI and ML are not as important at the beginning of the CDP journey, but they become more important along the CDP maturity curve. In this article, we’ll explore how your organization can address the challenges of AI deployment to have successful outcomes.

Top Four Challenges of AI Deployment and How to Overcome Them

We also recently published a white paper from CDP Institute titled “Managing Data for AI: Role of the CDP.” While the benefits of AI are too great to ignore, the white paper noted several challenges to its successful deployment.

According to the paper, just 12% of companies have a mature AI strategy and only 9% are confident in their governance of AI. Concerns around AI strategy and governance include explainability, biased results, unintended consequences, loss of skills, privacy violations, security risks, and future regulations.

The list of challenges to implementing and managing AI  projects can seem intimidating. By understanding why these challenges exist, we can better create solutions to overcome them. Let’s explore some of the challenges detailed in the white paper.

Data

Data quality and accuracy are one of the biggest challenges to deploying AI and ML technology. AI needs large amounts of well-structured data to learn, so if your datasets are unreliable or inconsistent, the accuracy of results may be affected.

Just 20% of organizations report that data accuracy is 80% or higher, and data sourcing and preparation take up more than half of model development time. Other potential issues include contextual understanding, scalability, security, and privacy concerns.

Just 20% of organizations report that data accuracy is 80% or higher…

To overcome these data management challenges, it’s important to focus on data quality, monitoring, and availability. This can be done by tagging data with attributes that make it easier for AI systems to analyze. Systems should monitor changes and regularly test the impact of such changes on existing AI models, so companies can quickly react when a model needs to be rebuilt or redesigned.

In addition, using data enrichment can create detailed and accurate unified profiles of your customers by gathering data from a variety of external sources, then combining that with your own first-party customer data. The enriched profiles can be updated with new data about customers and used to drive more meaningful personalization and customer engagement.

Education

AI requires a fundamental change in how workers do their jobs. While making any big change is difficult, AI faces the additional challenge that many people don’t understand how it works—or, they don’t trust it to work correctly. This explains why 63% of people believe education and training, along with awareness (52%) are the top barriers to AI deployment. Education can also address fears of job loss due to AI.

To educate, it’s important to spread AI competency across the organization, rather than just relying on a few specialists. Building relationships with external experts can supplement the company’s own resources and allow for expanded education across all departments. Most importantly, AI must become part of the larger corporate culture, with recognition and rewards to encourage AI projects.

…AI must become part of the larger corporate culture, with recognition and rewards to encourage AI projects…

Technology

With a sufficient budget, companies can purchase AI tools and assemble their customer data. Modernizing existing infrastructure may be a challenge, but the pandemic has accelerated this process, as many organizations are already making strides in digital transformation.

Technology infrastructure to support AI goes beyond the AI tools themselves. It also includes deployment, performance feedback, and self-service throughout. By properly deploying AI applications built with a comprehensive way to collect feedback through every stage, companies can maximize the benefits of AI.

Governance

The final set of obstacles relates to AI governance. This is more pressing for AI than most other projects because AI presents unique governance issues. These include explainability, bias, undetected errors, unintended consequences, privacy, security, and compliance.

To combat these challenges, it’s important to create and follow a defined process for AI implementation. Be sure to include privacy, security, and compliance reviews in the standard development process. Once implemented, there needs to be a focus on data governance and managing the quality of data inputs, model accuracy, and model outputs. Additionally, it’s important to identify a company-wide AI leader with overall responsibility for coordination and all other types of governance.

…The risks created by AI make powerful governance programs essential…

How Treasure Data Can Help Address the Obstacles of AI

Deploying AI successfully requires organizations to treat it as part of their core business operations. By focusing on data quality, educating employees, and modernizing technology infrastructures, businesses can set themselves up for success when it comes to AI deployment.  With a comprehensive approach to managing customer data, companies can make the most of their data and use AI to gain insights into customer behavior.

Treasure Data’s Customer Data Cloud makes it easy to collect quality customer data in one place and leverage that data for valuable insights. Using Treasure Data solutions, businesses can gather all types of customer data from both internal and external sources in a unified way, making it easier to uncover new insights and drive better customer experiences. With an integrated approach to AI governance, companies can ensure that the data they are collecting complies with all relevant regulations, ensuring that their customers’ privacy remains protected.

To ensure the success of your AI program, download this white paper, “Managing Data for AI: Role of the CDP.”