Boobesh Ramadurai welcomed the confluence of technology experts, innovators, and thought leaders as he kickstarted the event.
In 18 short months, Generative AI has evolved from a novel, futuristic idea to a real driver of business strategy. Early adopters are navigating the roadblocks of their initial investments, and enterprise leaders are asking themselves, “How do we move beyond the noise to create solutions that generate revenue and drive measurable efficiency?”
LatentView has had a banner year so far, where we celebrated our 18th birthday and acquired CPG-focused analytics firm, Decision Point, strengthening our CPG and Revenue Growth Management (RGM) expertise and expanding our impact in new markets, including LATAM. Additionally, LatentView was recognized as Partner of the Year by the International Myeloma Foundation and was named an ISG Supply Chain Analytics Leader.
On the heels of this incredible success and momentum, LatentView led a meaningful discussion about the reality of GenAI investments and a roadmap to move away from experimentation and into value creation.
Chief Client Officer,
LatentView Analytics
For those in the industry, the rise and fall of technology trends is all too familiar. Like a moth to a flame, media buzz, and capital investments flow toward the shiny new tech. Remember Snapchat Spectacles, which cost Snapchat nearly $40 million in unsold units, or that time you thought you might one day have a 3D printer in your house?
According to keynote speaker Krishnan Venkata, Chief Client Officer at LatentView, moving from an overhyped disruptor to a technology mainstay means meeting the 3 C’s of consumer adoption: context, cost, and convenience.
Context—is the solution relevant, and does it integrate naturally into consumers’ daily lives? Cost—is the price affordable relative to the value it provides? For example, it’s easier to justify the cost of a Tesla, whose base model retails for $35K, because of its long-term value and the money you’ll save on gas than the original “Microsoft Surface,” a 30-inch tablet that doubled as a coffee table and retailed for $10,000 in 2008. And finally, Convenience—how easy is the tech to get?
So, what makes GenAI different, or is it doomed to arrive at the same fate as other overhyped solutions? Krishnan doesn’t think so, but transitioning from hype to hypergrowth requires meticulous planning, alignment with larger business objectives, and a strong data core.
Krishnan cautions the audience to prioritize outcomes over use cases, meaning if you’re going to apply GenAI to a problem, make it make sense. Align initiatives to your business outcomes, fail fast, and learn faster.
Joining Krishnan on stage to illustrate GenAI’s success was Zoher Karu, VP and Chief Data and Analytics Officer of Blue Shield of California. LatentView Analytics and Blue Shield of California are working together to build and implement AI solutions that transform patient care.
“Healthcare is an environment rich in data and poor in organization,” said Zoher. He went on to emphasize that 95% of companies are investing in AI, but few are successful; why? According to Zoher, success in AI implementation is contingent on these 6 key steps:
Krishnan and Zoher left the audience with this: 8 in 10 GenAI proof of concepts will fail. Of those that fail, 35% will fail because of poor data quality. Without quality data, you’ve failed before you can even begin.
Senior Vice President of FP&A,
MyEyeDr.
Business Head,
LatentView Analytics
Gartner has made several compelling predictions that illustrate the future impact of AI on the enterprise. According to Gartner:
The outcomes are clear, but the path to get there is yet to be determined. Investment in new technologies comes with the expectation of a return—both in profitability, customer satisfaction, and business growth. To get there, panelists Kaushik Subramanian, Tudor Floru, and Abhilash Edathil gave the audience a look at what’s happening inside each of their organizations.
Tudor Floru is the Senior Vice President of financial planning and analysis at MyEyeDr., a global healthcare practice with more than 150 locations across the US. He doubled down on Zoher’s statement that healthcare, as an industry, lacks the critical organization to capitalize on GenAI’s full potential.
For Tudor, a large part of his role is supporting the acquisition and integration of regional practices into the larger global brand. He employs AI in shared dashboards that uniform data and interpret financial information in context, like a P&L, that may be challenging to understand for practitioners without a background in finance.
At ezCater, Chief Revenue Officer Kaushik Subramanian and the team use GenAI to improve the customer experience. Currently, it uses a robust recommendation engine to filter results based on need. The company is exploring natural language processing as a way for customers to search with greater specificity intuitively.
But no AI implementation is without its challenges. At Alludo, teams are undergoing a significant transformation that necessitates buy-in from several key stakeholders. One challenge the team faces, according to Abhilash Edathil, Alludo’s Senior Vice President of Global Online Sales & Marketing, is translating product information into different languages for different markets. This is exponentially faster using AI, but AI operating autonomously can lead to mistakes and even security breaches if not vetted properly. To mitigate this, Alludo takes a hybrid approach to AI implementation and pairs human expertise with AI efficiency to reap the most value.
All three panelists agree that AI goes farther with human intervention.
In the pursuit of value-driven solutions, Boobesh Ramadurai took the stage once again to define where we are on the journey toward maximum AI adoption. With more than 200 million ChatGPT users daily, it’s safe to say we have crossed the threshold into comfortability with GenAI. 85% of executives believe GenAI will bring a competitive advantage, but nearly 70% of companies have moved 30% or fewer POCs into production. What can we do to bridge this gap?
To illustrate the enterprise journey, Boobesh presented data from OpenAI that outlines the 5 phases of GenAI evolution. Those phases are:
Currently, we are in the “Agents” phase, and the enterprise is exploring ways to turn that decision-making into something profitable. The key to bridge the GenAI vision-reality gap is finding where GenAI fits within your enterprise. You need to identify use cases and then deploy customized GenAI solutions. And that’s where LatentView’s RAISE framework comes in—designed to help businesses not just implement GenAI but scale it for real impact across different business functions.
1. Retrieve: Used to enhance knowledge discovery and data retrieval.
LatentView’s Solutions:
2. Analyze: Used to summarize and for insight generation.
LatentView’s Solutions:
3. Implement: Used for automation with AI Agents.
LatentView’s Solutions:
4. Sync & Execute: Used to manage and execute complex workflows with multi-agent collaboration.
LatentView’s Solutions:
The move towards GenAI success takes dedication and diligence. And though the equation may be simple: a clear business vision and a unified data strategy equal profitable GenAI solutions, it’s not easy.
Six weeks ago, Gartner released its 2024 Hype Cycle, a measure of the maturity and adoption of new tech. Many companies die in the “trough of disillusionment”—their overinflated potential is never fulfilled. Because AI and GenAI are still in their relative infancy, the graph is front-loaded with numerous companies hiking towards the peak of inflated expectations.
Of the Generative AI initiatives mapped in this year’s Hype Cycle, the lion’s share of solutions aims to improve customer experience and retention (38%). Trailing behind are tools for revenue growth (26%), cost optimization (17%), and business continuity (7%).
Questions about transparency, bias, accuracy, intellectual property and copyright, cybersecurity, sustainability, and so on may limit the widespread expansion and adoption of these technologies.
On stage with Ayushman Mathur, Associate Director – Analytics at LatentView, panelists unpacked a reality where these solutions move beyond the peak of inflated expectations, slide through the trough of disillusionment, and settle into the plateau of productivity. Specifically, they addressed how they’ve battled challenges first-hand and shared how to move beyond the hype and into action.
To break the ice, panelists offered their favorite unique GenAI application. One panelist, when asked to write a recommendation for a colleague that they didn’t work closely with, turned to CoPilot. CoPilot was able to crawl the company’s internal database to gather the history of the colleagues’ work, their email communication, and the general sentiments of other team members. Not only did it make the writing process easier, it was arguably a more comprehensive analysis of their skills.
One major challenge for any enterprise working to implement GenAI solutions is freeing up enough bandwidth for experimentation. Shikha Agarwal, Group Project Marketing Manager on YouTube’s Subscriptions Growth team, says freeing up 20% of her team’s time for GenAI projects has allowed time to embrace new solutions relatively quickly. Speaking on AI’s potential, she imagines a future where the YouTube user experience looks unique to everyone based on their preferences.
Steve Metz, Vice President and Interim Head of Global Analytics at eBay, also stressed that organization is key to overcoming implementation challenges. He emphasized that progress requires organizing disjointed, siloed proof-of-concepts into systematic, organization-wide initiatives. Data democratization lends itself to more creativity and faster innovation.
Further along in the journey, different challenges emerge. Rachel Krall, Senior Director of Strategy and Operations at LinkedIn, shared what can happen when you have an overcomplicated model. Her team, which sells to enterprises in the Bay Area, uses a model to identify personas most likely to invest in LinkedIn’s solutions. As the market declined last year, the model doubled down on a small set of Silicon Valley targets who were, in fact, not in a position to invest in LinkedIn at all. The model, renowned for its complicated algorithm, failed to look at potentially beneficial outliers because the data it was ingesting was too targeted.
To wrap up the event, Kishore Kotturu, General Manager at Microsoft, left the audience with a profound thought about the future. “AI will continue to democratize information,” he shared. “Blurring traditional job roles so much that our teams won’t look at all like they do today. To stay ready for the next wave of enterprise innovation, upskilling and reskilling are critical.”
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