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Fact vs myth: Is there scope for investment in GenAI?

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Today, every business discussion leads to GenAI. We are, after all, reading about newer, bolder, and bigger investments in this innovative technology. Just last year, Microsoft reportedly invested $10bn in ChatGPT developer OpenAI for a ~35% stake. Again, in April, PE investors like Andreessen Horowitz and Sequoia Capital put an additional US$300 mn into the AI research company.

Other than these hyperscalers and investment companies, even smaller companies seem to be betting big on GenAI. A BCG survey reveals that global executives prioritize artificial intelligence (AI) and generative AI (GenAI) in their tech investments. While 71% plan to increase overall tech spending in 2024, a staggering 85% will specifically boost their AI and GenAI budgets.

Interestingly, businesses and PEs are not the only parties looking to invest in GenAI. Many governments tend to allocate a significant portion of their annual budget to GenAI research and studies. For instance, the Icelandic government has partnered with OpenAI to preserve its native language. Saudi Arabia has also recently revealed its plan to invest more than $40 bn in GenAI.

This fervor around GenAI falls perfectly in line with the Gartner Hype Cycle. We are at the steep log phase (incline) of the hype cycle supported by swift advancements of this technology.

Amid all these significant investments, the concern about tangible ROI is also becoming part of the boardroom discussions. Many stakeholders are now pondering about the ROI they can expect. However, to answer their question, we need to first look at the actual implementations of GenAI at scale. Even though big investments are being made into this technology, the latest reports suggest nearly 90% of GenAI POC pilots won’t be in production soon — and some might even get scrapped before they can take off. So, it is too early to paint a clear picture of the long-term ROI. There also seem to be many considerations beyond just the financial calculations. Let’s break it down.

You can’t predict what you can’t measure

There’s no cookie-cutter approach to determining the return on GenAI investments. Why? Various entities are currently investing in GenAI technology, and they differ from each other in their goals, business use cases, and positions in the GenAI adoption journey. Naturally, the ROI measurement also varies for these entities. Here’s what it means:

  1. Most organizations appear to be at the very first step of the GenAI journey. They’re still weighing on different use cases to see which one fits their operational needs. Despite their willingness to invest in GenAI, these businesses don’t have a clear business case in mind. And if you look at this cohort, return on investment is not even a question for them at this point.
  2. The next group is those who have done the due diligence and created a specific business case for applying GenAI. Now, two interrelated factors are at play for these groups. First, many of these initiatives are undertaken due to the fear of missing out since many competitors are investing in GenAI. These initiatives are often CEO-led instead of being a CFO- or CIO-led one. So, there is no apparent financial decision on which to base these use cases, leading to the second factor – weak business cases. As a result, organizations in this category often stop at small-scale GenAI implementations. And since the main objective of this cohort is to stay relevant, the ROI really doesn’t matter to them.
  3. In the third category, we have organizations that have not only dipped their toes in the GenAI frenzy but also created successful POCs based on strong use cases. They have invested significantly in human resources and technology to build prototype solutions that seamlessly fit into the intended use cases. These businesses are now looking at scaling their GenAI solutions and rolling those out at an enterprise level. At their current stage, the ROI is less financial. It is measured in terms of productivity or efficiency gain. The financial ROI will become clear once they scale the GenAI solution and implement it organizationally.
  4. Finally, we have companies that have scaled their GenAI solution and put it into larger production. These businesses are in a position to see a return on their GenAI investment. But there are some challenges for them, too. The topmost is cost optimization. As businesses scale out their GenAI application, they will use more resources from the available LLM platforms, burning their bottom line. If the cost spirals out of control, it might overshadow the intended benefit.

This situation is very similar to the whole cloud movement. The cloud journey had three parts. First, we had to decide which cloud to use through deep analysis and consulting. Then, we migrated to our preferred cloud services. Once the new cloud-based business model became the norm, we started looking at different strategies to optimize cost. The same applies to this whole GenAI situation. Your financial gains will become prominent as you mature in the journey, and cloud technology’s success is a testament to that.

Pitfalls

That said, there are some common pitfalls of GenAI implementation that many companies are losing momentum.

  • Lack of strategic vision: Some companies might rush into GenAI without clearly understanding how it aligns with their business goals. This can lead to poorly defined projects that don’t deliver the expected value.
  • Data silos and integration issues: If legacy systems are not well-integrated, they can create data silos that hinder the effectiveness of GenAI models relying on centralized and high-quality data.
  • Resistance to change: Legacy company employees might resist adopting new AI-powered processes, obstructing the success of GenAI implementations.
  • Focus on novelty over utility: Getting caught up in the GenAI hype and implementing flashy features that don’t solve real customer problems can lead to wasted resources.

Overcoming these bottlenecks will require careful consideration of various factors beyond just cost and revenue. A long-term strategic approach that goes beyond traditional metrics and focuses on both short- and long-term as well as indirect and direct benefits can give you a broader view. However, the specific value proposition will vary depending on the objectives and their unique goals.

Beyond the financial ROI

From a business point of view, you are bound to look at it from a monetary angle. But it is not only about the financial gain. Even when investing in GenAI to improve employee productivity, you ultimately free them up for more value-added tasks. This will indirectly add to your bottom line in the long run. Besides, GenAI is also opening up new avenues in research and academia. In drug development and disease prediction, GenAI plays a crucial role. The benefits of breakthroughs, innovation, and health improvements thus far outweigh the financial returns for research institutes and healthcare organizations using GenAI.

GenAI is also helping many businesses optimize their operation and reduce their carbon footprint or achieve other sustainability goals. These businesses might not see any direct monetary gain from their GenAI investments, but they can avoid any fines related to compliance breaches.

Due to these nuances, calculating the return from GenAI investment is not straightforward. It is better to consider GenAI a strategic investment. Even though it might take a while, we can expect to start seeing significant financial ROI in the coming years.

AUTHOR
SME Retail Expert Adnan Saulat

Adnan Saulat

Senior Vice President, SME Retail and Healthcare

SUBJECT TAGS

#ArtificialIntelligence

#GenerativeAI

#TechInnovation

#AIInBusiness

#TechROI

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