ReadingThe role of AI in data interpretation and decision-making

The role of AI in data interpretation and decision-making


Effortless'23 was centered on the role of AI in business, bringing together industry experts ranging from Scott Belsky to Vinod Khosla to discuss its impact on products and customers.

The speakers exchanged insights on how integrating AI can drive innovation, personalize customer experiences, and revolutionize the design and functionality of products.

The industry panel, in particular, focused primarily on the significance of AI in analytics. They spoke about how businesses can use AI to predict trends, customer behavior, and potential market shifts, enabling proactive strategies rather than reactive ones.

We aim to recap the engaging discussion led by Mollie Holland from DevRev's Customer Support & Success team, featuring insights from panelists Kshitij Gupta, Co-Founder & CEO of 100ms, Shikhar Agarwal, Founder & CTO of Spotnana, and Amit Prakash, Founder & CTO of ThoughtSpot, in this blog.

You can read more about Effortless’23 here.

Here's a quick gist about the speakers:

Shikhar Agarwal, Founder of Spotnana

Spotnana is a modern travel platform transforming the travel industry's infrastructure. Shikhar is passionate about making travel services more efficient and customer-centric. Having worked with Brex and other big names, he is also noted for his previous work at VMware, as a founding engineer at ThoughtSpot, and as a member of the Google Brain team.

Kshitij Gupta, Co-founder and CEO of 100ms

100ms powers real-time interactions in the virtual world. Kshitij has a history in live video technology, having developed video solutions for the first iPhone and 3G networks and the infrastructure for Facebook Live. He was also the VP of Engineering at Disney Hotstar, where he developed the world's largest live-streaming platform.

Amit Prakash, Founder & CTO of ThoughtSpot

ThoughtSpot is an AI-powered analytics company that uses natural language search to analyze data. Amit has a PhD in Computer Engineering and experience building large-scale analytics and AI systems, including work on Microsoft's Bing and AdSense at Google.

AI in analytics

In today's market, the companies that focus on using data and analytics are quickly moving ahead, while the ones that don't are falling behind or not growing. Data-driven decisions have become crucial in the digital economy, which is now a big part of the economy.

According to the latest Hubspot report on Marketing Trends, data-driven marketers will win in 2023.

This swift analysis paves the way for more timely and well-informed business decisions, ensuring companies can respond quickly to market changes and internal performance metrics.

The predictive capabilities of AI stand out as particularly transformative. Businesses can forecast trends, anticipate customer behaviors, and predict market fluctuations. Such predictive insights allow companies to adopt proactive strategies, staying ahead of industry curves and customer expectations rather than merely reacting to changes as they occur.

By utilizing AI analytics, businesses can:

  • Automate routine data analysis tasks, which frees human analysts to tackle more strategic, complex problems.
  • Gain predictive insights by identifying patterns and trends, helping businesses to anticipate market shifts or customer behaviors.
  • Improve decision-making by providing more accurate and faster insights, leading to smarter business strategies.
  • Enhance personalization by tailoring products and services to individual customer needs based on their behavior and preferences.
  • Increase operational efficiency and reduce costs by streamlining processes and reducing the potential for human error.
  • Manage risk better by detecting potential issues and anomalies that could indicate risks to the business.
  • Scale with the business as AI systems can handle increasing amounts of data without a corresponding increase in human resources.

Harnessing AI and analytics for advances in corporate travel

Kshitij Gupta captured the essence of the day's mood with his remark, "When you empower a system with AI, you're not just automating processes; you're endowing it with the capacity to learn, to predict, and to adapt in ways that are inherently human."

This set the stage for a discussion that delved into the nuances of how AI-driven analytics is not just reshaping existing business models but also spawning entirely new market dynamics. Shikhar Agarwal responded affirmingly, emphasizing the industry's keen interest in dynamic pricing strategies informed by such analytics.

"The ability to adjust prices dynamically, with the aid of deep analytics, is a game-changer," he noted. Agarwal pointed out that this approach allows the airline industry to respond more to market demand and optimize revenue.

"When you leverage data analytics in real-time, you're not just setting prices; you're adapting to the economic landscape and customer behavior instantaneously."

Agarwal further mentioned how this technology enables a more direct connection with travelers. This sentiment underscores the industry's shift towards a more data-driven, customer-centric approach, where every price point is an opportunity to meet the traveler's needs and preferences.

Amit Prakash, contributing to the depth of the discussion, shed light on an innovative idea poised to revolutionize the travel industry—a dedicated marketplace platform. This platform is conceived as a centralized hub where airlines, corporate entities, and travel agencies can converge to exchange and manage travel-related data with greater efficiency and transparency.

He stressed the potential of such a platform to streamline how travel data is accessed and utilized, leading to a more cohesive travel management ecosystem.

"This isn't just about buying and selling tickets," Amit elaborated. "It's about creating a shared space where the entire travel process is optimized—from price setting to policy compliance to enhancing the end-user experience."

The envisioned marketplace represents not just a tool for simplifying transactions but a strategic asset that can empower all parties involved to make more informed decisions, ultimately elevating the level of service provided to the modern traveler.

Key learnings:

  • Dynamic data analytics and AI are changing corporate travel management, leading to cost savings.
  • Airlines are interested in direct connections with travelers for dynamic pricing.
  • Breakthroughs in AI have been facilitated by team collaboration and customer feedback.
  • AI has improved the efficiency of meetings through advanced transcription and analysis.
  • Automation in customer service is seen as a way to reduce workloads and improve response times significantly.

The vision for an "agentless travel agency" is ambitious, reflecting the potential of AI to streamline operations. However, this also indicates a shift in the role of human agents, who may need to adapt to more complex tasks that AI cannot handle.

Adapting to AI: From traditional systems to innovative machine learning

In the fast-paced world of technological advancement, machine learning (ML) and artificial intelligence (AI) have emerged as groundbreaking forces, leading the charge toward a new era of digital innovation. These technologies are not just tools; they represent a fundamental shift in how we approach problem-solving and system development.

Whereas traditional system building is typically a linear process rooted in long-established protocols that offer predictability and a clear set of rules, ML, and AI introduce a dynamic, often unpredictable element that demands a different mindset. These modern methodologies are characterized by their ability to learn and adapt over time, becoming more sophisticated and accurate as they process more data.

The challenge for developers and businesses alike is to embrace this shift, moving away from rigid structures and towards more flexible, learning-oriented systems. This means letting go of the tried-and-true in favor of innovative solutions that improve and evolve autonomously.

In doing so, ML and AI disrupt the status quo, pushing boundaries and forcing industries to rethink how they operate, compete, and innovate.

Integrating AI into analytics further enhances this transformation, offering deeper insights and foresights that enable data-driven decisions to be more accurate, predictive, and effective.

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