Where does AI deliver the greatest value? We asked more than 500 CIOs to find out
November 13, 2024
Co-Founder and Chief Product Officer at Gong
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Sales Leadership
Co-Founder and Chief Product Officer at Gong
Eilon Reshef is the Co-Founder and Chief Product Officer at Gong, the leading platform in the revenue intelligence space. Since co-founding Gong in 2015, Eilon has spearheaded its product and engineering efforts, transforming how sales teams harness data to drive success. Gong uses AI to analyze sales interactions, offering actionable insights that help businesses grow revenue. Prior to Gong, Eilon co-founded Webcollage, a SaaS platform for e-commerce infrastructure. With deep expertise in product strategy and AI, Eilon is a key figure in advancing sales technology and operations.
The hype around artificial intelligence has reached a fever pitch in the last few years. Now that AI-powered applications have become firmly entrenched in the enterprise for everyday use, leaders are expected to quantify exactly how much value they are actually delivering.
However, it appears that that’s easier said than done. So far, the verifiable return on investment (ROI) of AI investments has been murky. There isn’t a currently accepted standard for how to measure the true impact of AI. Should we be asking how much time it’s helping save various business functions by automating processes? Is it about how much revenue it’s adding to the bottom line? Or what about just driving worker satisfaction?
At Gong, the AI opportunity for revenue teams is clear. We’ve seen the technology deliver on all these points, and more. But we wanted to find out how IT leaders are evaluating these investments and justifying them to the c-suite and board. So we conducted a survey of 573 CIOs and IT leaders in the United States and United Kingdom to gauge their perceptions of which AI projects are most likely to return value.
The survey’s findings illustrate the challenges many businesses are facing when it comes to really understanding where this value lies.
Not all AI projects are evaluated equally
The excitement around AI’s potential has spurred billions in investment and made it impossible to shy away from. But uncertainty remains around how to calculate the real value of those investments.
Over half — 55 percent of respondents — focus on increased productivity. However, consensus was rare. Nearly the same proportion of IT leaders put these priorities among their top metrics:
- Time savings (53 percent)
- Boosted revenue (53 percent)
- Employee satisfaction (46 percent)
Encouragingly, though, two out of five (39 percent) said increased revenue alone would not be enough to justify the costs of an AI project and 40 percent said the same about time savings. Yet, only 32 percent said they were measuring both. UK CIOs were more likely to consider both (44 percent) than US CIOs (20 percent).
This suggests that many CIOs are bought into the technology’s long-term potential despite not having a decisive way to measure its true impact. It’s great news that leaders understand the vast potential of AI, but closing the gap between perceived and actual value will be the next frontier for the enterprise.
CIOs are conscious of key barriers to implementation
At Gong, we’re proponents of equipping you with the right data and then using it as a foundation for your AI applications. Fortunately, many CIOs and heads of IT agreed.
For them, these were the primary aspects of implementation:
- Security and privacy (68 percent)
- Transferring and integrating their data (44 percent)
- Integration with other solutions (43 percent)
Security has clearly taken center stage in the AI conversation as regulations loom and businesses increasingly look to the technology as a way to increase efficiency. But companies struggle to make the most of their AI projects if they cannot trust the technology with their sensitive and confidential data. When asked where projects were most commonly falling short, security was the most frequently cited factor, according to 28 percent of tech leaders we surveyed.
Your business value may depend on the model you implement
After ensuring an AI application meets enterprise security standards, IT leaders next need to prove its suitability for their specific business. In this case, the large language model (LLM) you choose is critical. How do IT leaders select one model over another?
When asked, they reflected on the kinds of AI products and tools they were investing in:
- Nearly three-quarters (73 percent) of CIOs said they depended on off-the-shelf LLMs
- 58 percent are already working with domain-specific solutions trained on industry-specific data and capable of providing more measurable results
- This gap may be due to more than one-third (36 percent) of CIOs telling us they’d likely stop a project where data integration was too complicated
This suggests ease of implementation could be a top priority for many leaders, potentially hindering greater value from AI investments.
Get more value out of your AI investments
Over the last two years, the AI hype has led virtually every company to offer an AI solution or eagerly adopt one. But the tides are shifting, and leaders want to know if AI can deliver value today. How they will go about confirming that is still uncertain.
As these use cases mature and we see whether – and how – they provide value, the benefits of AI in the enterprise will become clearer. Global CIOs can then effectively implement the technology to achieve meaningful business outcomes.
Make no mistake: the AI opportunity is very real, but it must be done strategically, in a way that prioritizes solutions for critical business needs. As CIOs are seeing, the key to value is implementing the right AI applications to address their broader company goals.
