CIOs, CDOs, innovators, and IT leaders have many technology investment options to consider. How should you prioritize dollars, agile teams, and transformation programs, given how all businesses need better technology and data capabilities?

I have been advising Digital Trailblazers to prioritize force multiplying platform investments that drive multiple strategic benefits. For many businesses, the benefits should include growing revenue, improving customer and employee experiences, reducing costs, improving productivity, and addressing data governance and other risks.

I believe that one of these key investments is AI-Search, and in my recent paper, It’s Time To Revisit The Importance Of SearchFor Your Enterprise Tech Strategy, I share the rationale and a playbook for innovation leaders.

What is an AI-Search platform?

Here’s how many enterprises implemented search experiences in the past:

• Using out-of-the-box search capabilities in content management systems, CRMs, and other SaaS
• Dedicating a development team to building search using an unstructured data indexing engine
• Purchasing a search appliance or other search middleware, then spending development effort to code integrations and optimize search relevancy

The net is that 62 percent report managing multiple indexes for different applications, and 99 percent report having one or multiple issues delivering relevant search results, according to the state of enterprise search report.

An AI-Search platform delivers a unifying platform for customer-facing applications, customer support functions, employee workflows, and analytics on unstructured data sources. They provide out-of-the-box SaaS integrations, low-code and pro-code development options, and built-in and configurable machine learning capabilities.

In other words, they take much of the heavy lifting out of delivering a key business capability – helping end users find relevant information with fewer clicks and frustrations – while ensuring consistent and real-time information is available to a full experience connecting suppliers, customers, and employees.

How AI-Search is a force multiplier in digital transformation

In my paper, I share details on identifying KPIs, financial benefits, and executive partners to create an agile program for delivering search experiences across multiple business units and products. I group the benefits into three different investment rationales:

1. Deliver a consistent search experience for customers and employees

Search isn’t just a tool for ecommerce, media websites, and intranet portals. Consider these five experiences:

• Banks want to make it easy for small business owners to match financial services to their needs while enabling their reps to make smarter, proactive recommendations.
• Insurance companies need portals for brokers, agents, and partners to access consistent information.
• Hospitals want people to know what to expect before coming in for a procedure and share consistent information with patients, their doctors, and other healthcare workers.
• SaaS companies must supplement their products with self-service knowledgebases, chatbots, and customer support workflows that help solve problems faster.
• Manufacturers need to simplify product discovery, provide efficient customer portals, and enable more efficient customer service.

You may have different tools and apps to support customer, supplier, and employee experiences, but each technology can only provide access to the information in its silo. AI-Search with easy-to-implement integrations enables innovators to embed a tailored experience to a consistent and holistic information source.

 2. Increase revenue and create new growth opportunities

Ask this question, can your customer support team easily turn around a customer inquiry or incident into a sales opportunity?

To achieve this objective, the rep must have easy access to relevant information, respond accurately and quickly to the issue, and know enough about the customer to make the best product and service recommendations. Chances are, your customer service ticketing system doesn’t have all the information for reps to take on this greater responsibility, and embedding an AI-Search experience can be a game changer.

AI-Search can also help data science teams identify new growth opportunities by providing a way to query unstructured content and data sources. Using natural language processing to extract information from customer support tickets,  insurance claims, patient records, and other documents can provide insights for developing new products and services.

3. Lower costs, improve productivity, and reduce technical debt

IT also benefits when enterprises centralize search and shut down legacy search engines, and in the paper, I share several reasons why enterprises have accumulated significant technical debt in managing search and unstructured data.

Platform reductions are one source of cost reduction, and productivity improvements are another efficiency benefit when employees spend less time hunting down information. Centralizing can also help address data security and privacy by enabling data governance and compliance leaders one search platform for implementing data access policies.

Driving culture change with AI-Search

While I share these three strategic benefits of AI-Search platforms, there’s one other cultural benefit worth calling out.

Implementing search was once a source of conflict between business leaders, subject matter experts, and technologists on optimizing search relevancy. It wasn’t easy gaining agreement on taxonomy, knowledge extraction rules, and search results ranking – all algorithms now aided by machine learning capabilities.

It means CIOs and IT leaders can quickly release search capabilities and then establish a search center of excellence to extend implementations to multiple business units, product areas, and content sources.

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The views and opinions expressed herein are those of the author and do not necessarily represent the views and opinions of Coveo.