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Best Practices for Getting Company Data Ecosystems AI-Ready

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Read more about author Valentin Tsitlik.

Organizations invest considerable resources into collecting customer data to build digital footprints and profiles for enhancing the customer experience (CX). Previous technologies and toolsets limited businesses to simple, structured data, which included mainly transactional information as well as customer and call center conversations. Then, they would use a solution like sentiment analysis to determine if customers were satisfied with a product or service.

Today, with the emergence of artificial intelligence (AI) tools, companies can better utilize complex and unstructured datasets to deliver even more value to customers. For instance, large language models (LLMs) enable businesses to uncover and interpret the complexity and subtleties of human interactions and intentions at a high degree of sophistication. However, companies cannot simply plug AI into their data ecosystems and expect immediate results. They must make their data ecosystems AI-ready before they can reap such benefits.

Elucidating the Data Estate 

Data is the currency of the future, and for businesses to become AI-ready, they must gather and organize their data into a central repository. Unfortunately, most organizations do not understand their existing data estate. These companies should leverage the expertise of a partner with methods and solutions that can provide a comprehensive view of their data portfolio. While this data exploration process is complex and multifaceted, a skilled partner can help companies maximize their data through AI solutions, improving cost-efficiency and competitiveness.

For example, a unified generative AI orchestration platform can empower enterprises to accelerate experimentation and innovation across LLMs, AI-native applications, custom add-ons and – most pertinently – data stores. Acting as a secure, scalable and customizable AI workbench, this platform enables companies to understand their data ecosystem more deeply, streamlining and enhancing AI-driven business solutions. 

Additionally, by achieving a better understanding of one’s data estate, organizations can use AI more responsibly and in a way that safeguards the security of their data. Privacy and regulatory compliance will become increasingly critical as data becomes more detailed through AI-powered means. To be truly AI-ready, enterprises should utilize a partner’s expertise to ensure compliance with security requirements and adherence to responsible AI best practices.

Uplifting Data Processing Technologies and Data Governance 

Many organizations can build data technology tools and platforms but cannot incorporate and act upon heavily unstructured data within day-to-day customer interactions. Historically, most data processing occurred at the transactional level using relatively well-structured data. While businesses can finally take advantage of large quantities of messy or unorganized data through AI, their tech stack must be reinvented to support these complex datasets.

It is important to note that although AI demands considerably more advanced processing capabilities, once a company successfully infuses AI into its data ecosystem, it will streamline the processing of complex assets such as legal documents, contracts, call center interactions, etc. Ultimately, AI will become a more integral part of data platforms.

Today’s data governance playbooks will also require a facelift before companies’ data ecosystems are AI-ready. Organizations created data governance frameworks around more traditional data assets only recently. Now, businesses must use unstructured data like PII, emails, customer feedback, etc. As such, they should implement and adopt new data governance tools, approaches and methodologies.

Finding the Ideal Partner to Become AI-Ready 

Getting a data ecosystem AI-ready will require a high level of expertise that not many companies possess in-house. To that end, there are specific characteristics businesses should look for when choosing a partner to help prepare their data ecosystem for AI integration.

The ideal partner should have technical expertise across multiple disciplines outside AI. In the context of AI adoption, cloud, security, data, customer experience, etc., are not independent – in fact, these disciplines are highly interconnected. As such, for a partner to deliver on the promises of AI, they must have proven experience in multiple areas.  

Lastly, an ideal partner should understand the importance of agility. The pace of technological change is making the future murkier and more difficult to predict. Rather than trying to anticipate some future state, businesses should instead work with their partner to make their data ecosystems and human capital agile enough to adapt rapidly and continuously.