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.
This post is brought to you by Coveo.
The views and opinions expressed herein are those of the author and do not
necessarily represent the views and opinions of Coveo.
