5 tips for better business value from gen AI

CIOs have been able to ride the AI hype cycle to bolster investment in their gen AI strategies, but the AI honeymoon may soon be over, as Gartner recently placed gen AI at the peak of inflated expectations, with the “trough of disillusionment” not far behind.

That doesn’t mean investments will dry up overnight. According to AI at Warton’s report on navigating gen AI’s early years, 72% of enterprises predict gen AI budget growth over the next 12 months but slower increases over the next two to five years. That gives CIOs breathing room, but not unlimited tether, to prove the value of their gen AI investments.

Proving the ROI of AI can be elusive, but rushing to achieve it can prove costly. Still, CIOs have reason to drive AI capabilities and employee adoption, as only 16% of companies are “reinvention ready” with fully modernized data foundations and end-to-end platform integration to support automation across most business processes, according to Accenture. These “reinvention-ready” organizations have 2.5 times higher revenue growth and 2.4 times greater productivity improvements than their peers, Accenture notes, which should motivate CIOs to continue investing in AI strategies.

Many early gen AI wins have centered around productivity improvements. For example, developers using GitHub Copilot’s code-generating capabilities have experienced a 26% increase in completed tasks, according to a report combining the results from studies by Microsoft, Accenture, and a large manufacturing company.

But if all gen AI does is improve productivity, CIOs may be challenged long term to justify budget increases and experiments with new capabilities. Instead, CIOs must partner with CMOs and other business leaders to help quantify where gen AI can drive other strategic impacts — especially those directly connected to the bottom line. Below are five examples of where to start.

Specify metrics that align with key business objectives

Every department has operating metrics that are key to increasing revenue, improving customer satisfaction, and delivering other strategic objectives. CIOs should return to basics, zero in on metrics that will improve through gen AI investments, and estimate targets and timeframes.

“Set clear, measurable metrics around what you want to improve with generative AI, including the pain points and the opportunities,” says Shaown Nandi, director of technology at AWS. “What success looks like can vary widely and range from reducing a call center’s escalation rates, a food distributor’s sales order processing time, or a professional services company’s new employee onboarding time, to an airline that personalizes customer communications or a media company that provides real-time language translation.”

Bogdan Raduta, head of AI at FlowX.AI, says, “Gen AI holds big potential for efficiency, insight, and innovation, but it’s also absolutely important to pinpoint and measure its true benefits.”

Raduta recommends several metrics to consider:

• Cost savings and production increases when gen AI targets efficiencies and automation;
• Faster, more accurate decision-making when gen AI is used to analyze large datasets;
• Time-to-market and revenue when gen AI drives product innovation by generating new ideas and prototypes.

Collaborate with sales teams to drive revenue-related efficiencies

CIOs should collaborate with their frontline sales teams to see where gen AI can help increase revenue. For example, inside sales reps using AI to increase call volume and target ideal prospects can improve deal close rates.

Abhi Maheshwari, CEO of AI software vendor Aisera, says, “Gen AI provides many benefits for sales, and key metrics for assessing its impact include conversion rate, sales cycle length, average deal size, win rate, and lead volume.”

Here, agentic AI hold promise, with CRM vendors releasing AI agents and assistants for sales teams and reps, many of which drive efficiencies and promote data-driven practices. Salesforce recently released two autonomous AI sales agents, one that engages with inbound prospects and another that coaches sales reps. Gong’s Call Spotlight analyzes customer conversations to extract pain points, outcomes, and next steps for sales reps. Zia, Zoho’s sales assistant, predicts deal-win probability, recommends who should sell what products, and improves customer communications.

CIOs should speak to sales leaders to identify areas where sales metrics are underperforming and where gen AI-driven improvements can drive revenue. “Successful selling has always been about volume and quality,” says Jonathan Lister, COO of Vidyard.

Gen AI holds the potential to facilitate that.

Align data strategies to unlock gen AI value for marketing initiatives

Using AI to improve sales metrics is a good starting point for ensuring productivity improvements have near-term financial impact. A second area is improving data quality and integrating systems for marketing departments, then tracking how these changes impact marketing metrics.

“When considering the breadth of martech available today, data is key to modern marketing,” says Michelle Suzuki, CMO of Glassbox. “The CIO and CMO partnership must ensure seamless system integration and data sharing, enhancing insights and decision-making.”

To drive gen-AI top-line revenue impacts, CIOs should review their data governance priorities and consider proactive data governance and dataops practices that go beyond risk management objectives. Improving data quality and integrating new data sources to enrich customer and prospect data are vital for applying AI in marketing and sales.

For example, many organizations have been centralizing customer data for some time, but gen AI can greatly enhance the ability to find patterns and signals in unstructured data sources.   

“AI and large language models can process millions of data points from various channels like social media and reviews to analyze feedback,” says Jacqueline Woods, CMO of Teradata. “Beyond identifying complaints, AI can help marketing teams classify customer sentiment as positive, negative, or neutral and uncover trends, recurring themes, and seasonal patterns. These insights can help companies address common issues or innovate new solutions to boost customer loyalty and can even extend to consumers’ views on competitors.”

Paul Boynton, co-founder and COO of Company Search Inc., says AI can greatly enhance marketing by combining unstructured data from various sources, such as customer preference reports, current news, legal records, and data connecting ownership with affiliated companies. “Compounding these data segments results in smarter recommendations with lead scoring, sales forecasting, churn prediction, and better analytics.”

To support sales and marketing in deriving business value from gen AI, CIOs should do the following:

• Centralize and improve data quality around customer interactions to enhance the accuracy, completeness, and timeliness of data insights;
• Improve customer retention and prospect conversion rates by developing gen AI use cases aimed at personalizing marketing content campaigns;
• Facilitate change management in marketing and sales by gaining adoption in a few winning approaches and sharing best practices rather than serially experimenting with many capabilities.

Target call center and service operations

Call centers, customer service departments, IT service desks, and other support services have significant amounts of data in the form of service tickets, knowledge bases, and user profile information from CRM and HCMS platforms. Gen AI applied in these areas can have a force-multiplying impact by improving customer or employee satisfaction scores, reducing costs, and improving job satisfaction for service desk employees.

“In support functions, gen AI expedites call center operations by generating rapid, context-aware responses to intelligently route queries, reduce average handling time, and improve resolution rate,” says Ram Ramamoorthy, director of AI research at ManageEngine. “In IT service management, AI-driven knowledge graphs provide issue diagnosis and proactive resolution, decreasing downtime.”

Ashwin Rajeeva, co-founder and CTO of Acceldata, recommends CIOs collaborate with department leaders on gen AI use cases and “track Net Promoter Scores and resolution times in customer support to quantify AI’s impact on loyalty and efficiency. In HR, measure time-to-hire and candidate quality to ensure AI-driven recruitment aligns with business goals. Observability metrics such as data quality, freshness, and consistency provide essential insights that enhance the reliability and precision of these AI-driven outcomes.”

Gen AI capabilities that can improve service desk efficiencies and user satisfaction include:

• AI agents and chatbots that help users solve their problems without making a service request;
• AI agents and assistants that service desk workers can use to analyze tickets and provide faster, more accurate responses to end-user inquiries;
• Embedded summarization, content generation, and language translation tools can improve end-user communications. 

Appian, Atlassian, Cisco Collaboration, Forethought, IBM, Ivanti, Pega, Salesforce, SAP, ServiceNow, Workday, Zoho, and others launched service-oriented AI agents in 2024. I reviewed some of these agents, and found several AI capabilities that can become competitive differentiators. For example, Webex AI Agent is voice-driven for call centers, listens to customer issues, and provides natural voice responses. Workday Recruiter Agent proactively sources passive candidates, automates outreach, and recommends internal top talent for open positions. CIOs should encourage department leaders to craft measurable ways to capture the business value of competitively differentiating capabilities that play out over longer timeframes.

Measure employee experience as AI transforms work

CIOs should also look inward at how gen AI impacts employee job satisfaction and overall well-being. Deloitte’s State of Generative AI in the Enterprise found that only 20% of organizations are highly prepared for talent considerations in adopting gen AI compared to 45% in technology infrastructure and 41% in data management.

This data suggests change management efforts are lagging technology efforts at many organizations. CIOs should adopt digital transformation techniques that ease adoption, such as setting realistic expectations, rolling capabilities out incrementally, and educating teams implementing AI on developing active listening skills. CIOs should then measure the impact of employee experience across multiple roles, personas, departments, and geographies and adjust change management programs accordingly. 

While driving business value may be a priority, Assaf Melochna, president and co-founder of Aquant, is concerned about how AI impacts employee well-being. “While gen AI enables employees to increase their output significantly, it often leads to heightened workloads and overwhelmed workforces. As AI technologies revolutionize workflows across industries, we see AI beginning to integrate into employee mental health initiatives, counteracting burnout created by AI-driven efficiency.”

Perhaps, with the hype of AI behind us, it’s the right time for CIOs to identify where gen AI is providing short- and longer-term business value and to partner with department leaders and employees on smart adoption and measuring impacts.