By using artificial intelligence (AI) and machine learning (ML), brands can analyze data objectively, improving insights and generating actionable reports that can be used to make data-driven decisions. With AI and ML, these insights are more sophisticated and valuable to brands, allowing them to enhance and improve the customer journey.

The use of AI and ML continues to exponentially increase across a wide variety of industries. A 2021 O'Reilly report  revealed that of those using AI, computer, electronics and technology industries were at the top of the list (17%), while financial services (15%), healthcare (9%) and education (8%) are the industries with the next-most significant use of AI. Let's take a look at the ways that AI and ML can enhance a brand’s data strategy and improve the customer experience.

AI and ML Can Help to Overcome Data Challenges

There are many challenges that brands face today when it comes to efficiently accessing and using data to derive actionable insights. In fact, Dell’s 2020 Digital Transformation Index revealed that data overload and “[the inability] to extract insights from data” is a rising barrier to digital transformation. Fortunately, AI and ML can help brands tame the data beast.

Siloed data is another challenge for brands today, especially when it comes to data analytics. A data silo is a repository of data that is controlled by one department and is isolated from the rest of the departments in a business. Although AI and ML are not magic pills that will eliminate the challenges of siloed data, they can provide brands with ways to minimize the effects of data silos.

“Siloed data is indeed one of the challenges of most organizations as AI depends on known data,” said David Geffen, vice president at Glassbox, a digital experience analytics firm. “Naturally, AI’s insights are much more detailed and actionable the more it has a holistic view of data — siloed data is a perpetual hurdle to this. This is why 45% of companies say the more they invest in data collection the less benefit they see — this is because it’s not about more data, it’s about ‘non-siloed’ data,” said Geffen.

Although many brands try to solve the challenge of siloed data by aggregating even more data, this is not a viable solution. “Companies have the inclination to combat silos with higher volumes of data, hoping that it creates a 360-degree profile of the customer,” said Geffen. “This is never achievable. Instead, you must account for siloes and provide AI/ML systems with the least-needed amount of data to make a decision. Research has found that we see diminishing gains after we provide AI systems with three-to-four data dimensions—beyond that, more data pieces achieve no greater benefit.”

CF Su, vice president of machine learning at Hyperscience, an intelligent document processing platform (IDP) provider, told CMSWire that the symbiotic relationship between data, AI and ML is a force to be reckoned with in the enterprise ecosystem. “Increasingly, these algorithms are generating more accurate insights as the influx of data is digested. When thinking about data strategies, AI/ML can effectively process and improve the data fed into an organization’s system,” explained Su, who gave the example of IDP, which, backed by AI/ML, can interpret characters from unstructured data like PDFs, emails and messy handwriting, effectively turning unstructured information into actionable data that can be used for downstream decision-making. “Enterprises must tap into AI/ML tools that enable the easy processing of data without burdening employees, turning static numbers and information into usable insights,” suggested Su.

Related Article: What's Next for Artificial Intelligence in Customer Experience? 

AI Adds Speed, Convenience to Complex Processes

Leandro DalleMule, general manager at Planck, North America, an AI data platform provider, told CMSWire that his business has seen the most successful and powerful uses of AI in the insurance and finance industries where they need to be able to siphon large amounts of data into relevant insights not only quickly, but also accurately.

"These industries are complex but have customers that are demanding a better, faster experience," DalleMule said. "AI can take traditional long forms and applications that a customer would need to complete and shorten them to two questions, for example, a name and an address.” DalleMule added that with answers to these two questions, AI generates the information that is needed to underwrite and price policies, which expedites not only internal processes but also the time the customer has to wait.

Although AI and ML have the ability to reduce the time a customer spends making a purchase, it’s still a new technology that many brands have not yet fully accomplished. “With AI and ML, commercial insurance has become a real-time purchase — although many insurers are not there yet," said DalleMule. “Using only a business name and address, carriers can access a complete risk profile assembled and extrapolated from the company’s digital footprint.” DalleMule said these automated insights shorten application forms, eliminate human error, supplement omissions, and reduce additional customer back-and-forth.

The process of collecting and analyzing data accurately in a timely manner is often outside the scope of what humans are capable of — but AI and ML are options that are fully capable of such processes. “Outside of these industries, AI is the next-generation tool that is helping remove human error and aiding the decision-making process through actionable insights,” said DalleMule. “What AI brings to the table is the efficient collection and analysis of multiple data sources which focuses the user’s time on how to use that data to drive everything from customer experience to profitability growth.”

With fears of a recession and a focus on reducing spend, many brands are turning to AI as a cost-effective solution. Sean Sollitto, co-founder of Relish, an enterprise application development firm, told CMSWire that AI and ML are an economical way for business leaders to assess and stop their spending to become more efficient with current inflation rates and an impending recession.

“Businesses must turn to quick-to-deploy, low-investment solutions to fill functionality gaps and maximize the value of enterprise IT investments,” said Sollitto. His Relish InvoiceAI, for example, identifies errors and alerts the supplier to fix and resubmit the invoice, all without involvement from the AP team. “Using AI and machine learning through enterprise software solutions helps companies boost efficiency and maximize investment without the need for additional spending.”

Related Article: Do Your Customers Trust Your AI?

Learning Opportunities

AI Improves the Customer Experience

Rather than looking at the individual interactions that a customer has with a brand, many brands are looking at an overview of the entire customer journey — a process that AI has simplified and enhanced. “AI has been a tremendous help in changing our entire structure of looking at customer behavior and the way we approach decision-making,” said Geffen. “We now look at customers’ digital experiences as a journey rather than a collection of pages they visited, which until AI/ML/NLP came into wide use was the only real indication you had of customer preferences and behavior.”

As Geffen explained, AI points to how an experience on one page impacts a customer’s behavior on the next — and on the next visits even days or months later. “It bridges gaps and helps us create more robust digital profiles.”

Kathy Stares, executive vice president of Americas at Provenir, an AI-powered risk decision engine provider, told CMSWire that for customers, the name of the game is speed. “Consumers expect instant decisions, personalized offers and automated digital experiences. This requires deeper insights from more data sources to power a new level of decisioning, speed and accuracy,” said Stares. 

“If a business cannot onboard a customer quickly and easily, there’s a good chance that customer will move on to a competitor.” Stares believes that automation capabilities can minimize customer effort by leaning on technology to do the heavy lifting. “Ideally, this approach leverages automation to augment customer data with the additional information needed to perform robust compliance checks, identity verification and risk decisioning, all in real time.”

Related Article: How AI Can Impact Your Marketing, Customer Experience 

The Challenges of AI/ML

Although AI and ML can speed up many processes within the customer journey, too much data can cause a slowdown that is detrimental to the process. “Even with the least-viable amount of data points inputted to AI/ML, the sheer volume of insights can overwhelm entire systems and impacts performance,” said Geffen. “It also impacts analysis time — the more data you’re processing, the more time for analysis. Processing three customer journeys versus 800-plus customer journeys requires more time, and if insights are needed ASAP or pressure is coming from above in your organization, this may not be the time you budgeted for.”

Skilled illiteracy shortfalls are also a big problem for brands that wish to use AI and ML as part of their data strategy. “Most business analysts are not familiar with using AI, interpreting its insight, and making those insights actionable. There are two schools of thought in this situation.

In one, it should be a requirement for analysts to upskill and reskill regularly, so they are not only able to solve data problems in front of them, but think long-term,” said Geffen. “In another, many believe that the natural progression of AI is that it will simply do everything for analysts, like providing insights in a digestible way and clearly making them actionable. The truth is, both are important.” Geffen reiterated that analysts need to have the baseline skills to interpret data and draw their own conclusions, while AI should also make its findings as clear as possible so that analysts can quickly implement or elaborate on them.

Final Thoughts on AI and ML

AI and ML can play an effective role in a brand’s data strategy, providing a way to help manage vast amounts of data, enhancing the speed and convenience of customer interactions, providing a deeper understanding of where the customer is in their journey, and producing actionable insights that improve personalization and marketing suggestions.

By being aware of overstuffing AI with too much data, and upskilling and reskilling employees to bridge skilled illiteracy shortfalls, brands can improve and enhance their data strategy and the customer experience.