Demystifying AI’s growth generation capabilities

GenAI, agentic AI enable customer-focused improvements

Executive Summary

The emergence of generative AI and agentic AI has created a remarkable new opportunity for businesses to improve their customers’ experiences and deliver revenue growth.

Companies are capitalizing on GenAI’s ability to learn from existing data and create content and interactions that delight their customers. Meanwhile, businesses are using agentic AI’s capabilities to perform actions on behalf of employees, fielding customer inquiries and responding to requests in seconds.

When applied to augment commercial, operational and business intelligence capabilities, GenAI and agentic AI can improve customer retention, lifetime customer value and SG&A as a percentage of revenue.

To succeed, companies need to create an architecture that enables horizontal scaling, prioritize back-end system integration, and use the right data to feed the AI platforms. Responsible AI governance and employee-focused change management also are keys to success, and organizational silos must be broken down to enable effective data exchange and monetization of customer intelligence.

While incorporating GenAI and agentic AI for growth is just the next step in a technology enablement cycle that began years ago, it does require a shift in mindset that ultimately can pay off in substantial growth.

Introduction

GenAI and agentic AI have quickly become catalysts for advanced, customer-focused revenue generation on a scale that would have been impossible just a few years ago.

Companies are augmenting their CRM and marketing systems with AI-enriched insights that transform their ability to go to market, enable personalized outreach that improves customer experiences and ultimately turn those satisfied customers into a growth engine.

To gain an edge on their competitors, AI-powered companies are replacing manual processes with automation and upgrading legacy systems that they have found to be incompatible with GenAI and agentic AI architecture.

“While everybody else is figuring out how to adopt these technologies, you’re just going to be left in the dust if you don’t get on board,” said Grant Thornton Technology Modernization Services Senior Manager Kaitlyn Ramirez. “That’s the reality of this moment.”

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Enabling customer-focused growth

Deploying AI for customer-focused growth is a huge opportunity for most businesses, as our Digital Transformation Survey showed that 63% of organizations rank customer relationship management and customer experience as one of their top three priorities for tech enhancements. At the same time, 71% of executives responding to our survey say they are using GenAI.

Using the technology to benefit customers and drive growth is near the top of the C-suite to-do list. This AI-driven customer experience enhancement starts with taking inventory of the processes that shape a company’s customer journey, experience and expectations. Understanding the current state and ability to deliver on GenAI and agentic AI initiatives is important as improvements get underway.

A grasp of the potential capabilities also helps inform what’s possible. GenAI and agentic AI can enable personalized customer experience at scale, personalized content creation, predictive insights and operational benefits and process efficiencies.

Within the go-to-market function, GenAI and agentic AI uses include:

Targeting accounts dynamically, replacing manual list building
Replacing one-size-fits-all campaigns with personalized campaigns that respond to buyer behavior
Creating first-draft content, briefs and subject lines
Optimizing content development and distribution by identifying which assets influence late-stage pipeline movement
Redesigning customer journeys to include scalable, self-service pathways

Increased customer interaction and engagement are the first signs that a company is on the right track.

“If you’re able to deliver personalized and predictive experiences to satisfy and meet the needs of customers as they evolve, you’re going to have higher customer retention.”

Mark Owens

Managing Director, Business Consulting
Grant Thornton Advisors LLC

“Success really comes with adoption from your end user,” said Grant Thornton Technology Modernization Services Managing Director Supreet Singh. “Good things will happen when you focus on your customer experience and embed that focus into your design.”

Improvements in other metrics typically follow:

SG&A as a percentage of revenue can decrease significantly
Customer acquisition cost often is reduced
Customer retention improvements feed enhancement of other key metrics

“If you’re able to deliver personalized and predictive experiences to satisfy and meet the needs of customers as they evolve, you’re going to have higher customer retention,” said Grant Thornton Business Consulting Managing Director Mark Owens. “And ultimately, lifetime customer value is going to be dramatically higher.”

4 keys for go-to-market tech adoption

While enhancing customer experience is the critical goal that determines success in this tech implementation, companies need to consider four additional key objectives as they choose and implement GenAI and agentic AI for growth:

Creating modular, scalable architecture: GenAI agents can be linked together to form whatever architecture a business needs, enabling horizontal scaling that creates more agility than the traditional monolithic, vertically scaled infrastructure.
Back-end system integration: A go-to-market tech transformation is most successful when back-end data sources can be integrated so the right data can be incorporated. It’s also helpful when new data sets can be added using a unified integration model.
Effective AI governance practices: Responsible AI and human-in-the-loop objectives must continue to be prioritized to reduce risks. “If our clients don’t have formalized responsible AI use policies, they’re usually worried about the risks,” Ramirez said.
Change management: Training teams to take advantage of these tools and aligning adoption with company culture is critical. Meanwhile, keeping customers informed about how new customer-focused tech will improve their experience can prepare them for the changes and enhance their satisfaction.

Capitalize on the right data — and customer intelligence

Successful business ventures start with high-quality raw materials, and data is the raw material that feeds GenAI and agentic AI platforms.

Fortunately, organizations don’t need perfect data across the enterprise to take advantage of GenAI and agentic AI in their go-to-market campaigns. Many key go-to-market innovations start with small pilots that require a modest amount of data and can be scaled if they demonstrate an appropriate ROI.

Data priorities for these projects include:

Data platforms with strong, reliable ecosystems: Proven hubs of trusted data are preferred to newly acquired data.
Data readiness: Data needs to be clean, compliant, connected and integrated.
Cross-functional data sharing: Departmental silos prevent optimization of AI platforms.
Availability of data: Real-time access often is best.

“Data needs to be served, available and trusted with the appropriate cadence.”

Kaitlyn Ramirez

Senior Manager, Technology Modernization Services
Grant Thornton Advisors LLC

“Data needs to be served, available and trusted with the appropriate cadence,” Ramirez said. “It’s important to take a right-sized approach when determining what data availability needs to be.”

Breaking down departmental silos, meanwhile, will allow companies to truly take advantage of the go-to-market’s function’s role as a customer intelligence provider. Organizations often have marketing, sales and customer success functions walled off from the organization, with a mandate to sell whatever goods or services the company creates.

This separation from the rest of the business prevents organizations from taking advantage of the vast treasure trove of data that go-to-market possesses on customer preferences. Companies can maximize their growth when they use this customer intelligence to learn how to adjust their products and services — and ultimately their operations — for maximum effect and customer satisfaction.

An effective revenue operations function can help break down these silos to facilitate alignment, promote data-driven decisions, enhance the customer experience and ultimately drive revenue.

“A centralized RevOps function is ultimately responsible for driving that accountability cross-functionally within marketing, sales and customer success — in partnership with finance and enabled through technology in the IT domain,” Owens said.

Scaled capabilities: A 3-legged AI stool

Another way to view how GenAI and agentic AI can make an impact throughout an organization is to think of the organization’s capability domains as a three-legged stool. The three legs are represented by the commercial, operational, and business intelligence capabilities of the organization:

Commercial: In this external-facing leg of the organization, AI is used to attract, engage and retain customers with personalized, dynamic interactions. For example, GenAI can enable one-to-one hyper-personalized marketing content, and agentic AI can analyze customer-facing websites and dynamically arrange content to meet customer preferences, enabling a better experience and driving growth.

Operational: GenAI and agentic AI have a significant role to play in creating efficiencies in this internal-oriented leg. GenAI can speed up routine tasks such as report drafting, document summarizing and code writing. AI agents can save time with customer support troubleshooting.

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This chart shows how GenAI and agentic AI can be used in the commercial, operational and business intelligence functions of an organization to enhance revenue creation.

Business intelligence: This leg of the organization uses AI to gain insights on customers, markets and the business itself to drive better business decisions. AI can turn raw data into actionable intelligence. For example, GenAI can generate detailed customer personas for marketing teams based on an analysis of large data sets for customer segmentation. Agentic AI can replace static dashboards with intelligent dashboarding, dynamically updating reports and tailoring visualizations to different users based on their needs. This leads to better planning and forecasting — and ultimately improves business results.

GenAI and agentic AI have separate use cases in each of the three legs of the stool, and the technology drives growth best when it’s deployed effectively in each area — and when these functions work together in harmony.

An evolution, not a revolution

“It’s not really revolutionary. It’s just the evolution of what we did in the past with analytics, only with real-time data collection and interactions.”

Supreet Singh

Managing Director, Technology Modernization Services
Grant Thornton Advisors LLC

Although embedding GenAI and agentic AI across the commercial, operational and business intelligence aspects of the organization seems challenging, Singh reassures clients that this is just the logical next step in tech-enablement that has been taking place for many years.

For example, companies have been tracking click-throughs and other customer interactions on their websites for years. The next step is to apply AI to interact more effectively with customers, understand their preferences more fully and present them with a better experience.

“It’s not really revolutionary,” Singh said. “It’s just the evolution of what we did in the past with analytics, only with real-time data collection and interactions.”

At the same time, Ramirez acknowledges that the new capabilities require many companies to change their ways of working — while asking employees to alter their habits and behaviors. Helping clients navigate through these challenges often is the most rewarding experience of Ramirez’s day.

A strategic facilitator can help companies understand their options, spend on the right technology and assist employees in taking full advantage of AI.

“There is a big gap between how teams are working today and how they will work in the future,” Ramirez said. “Maximizing generative AI and agentic AI means using those technologies first instead of a search engine. It requires a very different mindset for gathering intelligence.”

But when the mindset shifts, the intelligence can be incredibly rewarding — for customers and businesses in their pursuit of growth.