With generative AI reaching the peak of its hype cycle, expectation from the technology is skyrocketing. This puts pressure on IT leaders to deliver value from their AI initiatives. Sadly, this is not easier said than done. Even though most organizations are still bullish about the prospect of generative AI’s future and almost three quarters of them expect AI budget to increase in the next year, most organizations are still skeptical about whether generative AI technology can deliver tangible results or not.

Proving return on investment for generative AI is challenging because many benefits are indirect and evolve over time. According to Accenture, companies that achieve “reinvention readiness” through modernized data systems and platform integration see 2.5 times higher revenue growth.

These reinvention-ready organizations demonstrate the transformative potential of artificial intelligence, motivating IT leaders to focus on building strategic impacts.

5 Ways To Generate Business Value From Generative AI

Here are five ways you can use to get better business value from generative AI initiatives.

Align Metrics With Business Goals

To sustain artificial intelligence investments, IT leaders must specify clear metrics tied to business goals. Shaown Nandi, director of technology at Amazon Web Services, highlights the importance of identifying pain points and setting measurable targets for improvement such as reducing call center escalation rates or accelerating onboarding processes.

According to Shawn Nandi, “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.”

Metrics for evaluating generative AI’s impact include:

• Cost Savings and Efficiency: Streamlining operations to reduce costs.
• Improved Decision-Making: Using artificial intelligence to analyze complex datasets for faster, more accurate insights.
• Time-to-Market Innovations: Leveraging artificial intelligence to develop new products and prototypes more quickly.

Collaboration with business leaders is crucial. Bogdan Raduta, head of artificial intelligence at FlowX.AI, emphasizes the need for cross-functional alignment to measure true benefits and ensuring Generative AI Initiatives efforts support long-term goals. You can buy a VPS to achieve better scalability at a lower cost.

Closely Collaborate With Sales

Generative AI Initiatives ability to enhance sales processes provides another opportunity for IT leaders to showcase return on investment.

AI-powered tools like Salesforce’s autonomous sales agents and Gong’s Call Spotlight optimize sales strategies by identifying customer pain points, improving lead targeting as well as increasing deal close rates.

Abhi Maheshwari, CEO of Aisera, recommends tracking metrics such as conversion rates, sales cycle length and win rates to assess generative AI’s impact. Sales teams can particularly benefit from agentic AI, which automates repetitive tasks, freeing up time for high-value activities.

“Successful selling has always been about volume and quality,” says Jonathan Lister, Chief Operating Officer of Vidyard. Generative AI enhances both by improving lead quality and enabling data-driven decision-making.

Enabling Marketing with Data-Driven Insights

Generative AI Initiatives can also revolutionize marketing by improving data quality and integration. Many organizations struggle with fragmented data systems, limiting their ability to extract actionable insights.

Jacqueline Woods, Chief Marketing Officer of Teradata, points out that generative AI can analyze unstructured data such as social media feedback and customer reviews.

Paul Boynton, Chief Operating Officer of Company Search Inc thinks that combining diverse data sources can yield smarter recommendations from lead scoring to churn prediction.

To unlock generative AI’s potential, IT leaders should focus on:

• Centralizing and enriching customer data for precise insights.
• Personalizing marketing campaigns using AI-driven insights.
• Promoting change management by highlighting successful use cases.

4. Transforming Call Centers and Service Operations

Service functions such as call centers and IT service desks are ripe for generative AI adoption.

By applying artificial intelligence to analyze service tickets, generate context-aware responses and streamline workflows, organizations can improve customer satisfaction while reducing costs.

Ram Ramamoorthy, director of artificial intelligence research at ManageEngine, highlights generative AI’s ability to decrease average handling times and improve resolution rates.

Meanwhile, Ashwin Rajeeva, Chief Technology Officer of Acceldata, emphasizes tracking metrics like Net Promoter Scores and resolution times to quantify artificial intelligence’s impact on loyalty and efficiency.

Several leading vendors, including Salesforce, ServiceNow and Zoho have introduced AI agents to enhance service operations. For example, Webex AI Agent provides natural language responses in call centers and Workday Recruiter Agent automates candidate sourcing and outreach.

Enhancing Employee Experience

Artificial intelligence influence extends beyond business outcomes to the workforce itself.

While productivity gains are a common early win for generative AI just like with dedicated server hosting, organizations must also address employee experience.

Deloitte’s research reveals that only 20% of companies are well-prepared for the talent implications of generative AI adoption.

Assaf Melochna, co-founder of Aquant, warns of potential burnout as artificial intelligence increases workloads and output expectations. IT leaders can mitigate these risks by implementing change management techniques.

Here are some of the techniques they can use:

• Gradual rollouts of artificial intelligence capabilities to reduce disruption.
• Educating employees on artificial intelligence tools to build confidence and ease adoption.
• Measuring employee satisfaction across roles and geographies.

Artificial intelligence’s role in mental health initiatives such as monitoring workloads and mitigating burnout.

This dual emphasis on productivity and well-being ensures that artificial intelligence adoption enhances the workplace holistically.

From Hype to Sustained Value

As the artificial intelligence hype cycle progresses, IT leaders must shift from experimentation to strategic execution. This requires collaboration with business leaders, alignment with key objectives and a commitment to measuring impact.

By focusing on revenue-driving initiatives, data integration and employee experience, IT leaders can demonstrate the sustained value of generative AI investments, ensuring they remain a priority long after the honeymoon period fades.

What steps do you take to get the best out of your generative AI initiatives? Share it with us in the comments section below.

A server is a computer or system that provides resources, data, services, or programs to other computers, known as clients, over a network. In theory, whenever computers share resources with client machines they are considered servers. There are many types of servers, including web servers, mail servers, and virtual servers.

An individual system can provide resources and use them from another system at the same time. This means that a device could be both a server and a client at the same time.

Some of the first servers were mainframe computers or minicomputers. Minicomputers were much smaller than mainframe computers, hence the name.

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Initially, such servers were connected to clients known as terminals that did not do any actual computing.

These terminals, referred to as dumb terminals, existed simply to accept input via a keyboard or card reader and to return the results of any computations to a display screen or printer. The actual computing was done on the server.

Later, servers were often single, powerful computers connected over a network to a set of less-powerful client computers.

This network architecture is often referred to as the client-server model, in which both the client computer and the server possess computing power, but certain tasks are delegated to servers. In previous computing models, such as the mainframe-terminal model, the mainframe did act as a server even though it wasn’t referred to by that name.