AI in Data and Analytics

AI in Data and Analytics

In today’s world, being data-driven is no longer enough—companies must embrace AI to thrive. To this end, TSIA’s 2023 ENVISION conference recently shed light on a roadmap for organizations that are eager to excel in the dynamic AI landscape.

In this blog, we’ll delve into key takeaways from these insightful conference sessions. The resounding message? To harness the transformative potential of AI, organizations must adopt a strategic and sustainable approach that seamlessly blends technology and human expertise to boost productivity.

Join us as we explore the future of AI integration!

Revolutionizing With AI: A Roadmap

Staying ahead of the competition is paramount. Jared Peterson, Senior Vice President of R&D Platform Engineering for SAS, believes that the key to success lies in harnessing the power of AI to supercharge human productivity. While remaining data-driven is essential, Peterson’s insights from his recent conference session emphasize that organizations must make the leap to becoming AI-driven to truly excel.

But what’s the secret sauce to this transformation? Peterson’s wisdom provides a valuable roadmap for those eager to embrace AI-driven change and surpass their rivals. He often hears a common lament: “I’ve got all the tools, the automation, a top-notch team, and I’m spending a fortune, but I’m not seeing results. Why?” According to him, it’s not just about technology; these organizations have inadvertently created friction in their models.

Success in the AI realm means outpacing competitors in identifying opportunities swiftly. The decision-making process has become increasingly complex amid today’s global disruptions. AI, on the other hand, boasts a well-defined life cycle. The motivation to go AI-driven is about moving from questioning to informed decision-making, as illustrated below. The speed at which an organization navigates this cycle is a testament to its resilience.

AI’s repeatable life cycle, from asking questions to making informed decisions.
AI’s repeatable life cycle, from asking questions to making informed decisions.

The Three Pillars of AI Transformation

Embarking on this AI-centric journey requires focusing on three critical pillars: productivity, performance, and trust.

  • Productivity: In AI, true productivity is measured by the decisions made by models in production. Everything else—experiments, flashy demos, or proofs of concept—are secondary, as Peterson explains.
  • Performance: While cloud adoption delivers quality, agility, and resilience, cost control can be a challenge, especially when it comes to training AI models.
  • Trust: Responsible AI is non-negotiable. AI systems rely on historical data to make predictions, and any bias or inaccuracy in this data will taint the quality of predictions.

Furthermore, to extract maximum value from data and thrive in AI, organizations must embrace open data formats—a crucial step highlighted by Peterson. The quest for the right talent is a common challenge, compounded by impending regulations that will have a global impact on AI usage. This makes the adoption of compliance tools truly imperative.

Real-World Use Cases: How AI Transforms Industries

AI isn’t just a buzzword—it’s reshaping industries and revolutionizing how businesses operate. In this section, we explore real-world applications of AI that are making waves and delivering tangible results.

Risk Management and Empathy at Scale

Moody’s Analytics, a front-runner in risk management, knows the importance of staying ahead in a rapidly evolving global landscape. Dominique Gribot-Carroz, the Global Head of Customer Experience, introduced a game-changing concept called “empathy at scale” during his recent ENVISION session. This innovative approach harnesses generative AI capabilities to capture customer sentiment on a massive scale.

Before the era of generative AI, parsing through customer comments was a Herculean task, consuming hundreds of hours on average. With generative AI, the process now takes as little as three hours, enabling real-time sentiment analysis. The distilled customer feedback turns into valuable insights that can be shared across departments, fostering alignment and enabling teams to create actionable plans.

The outcome? A highly focused, easily digestible dataset that informs cross-functional decisions. Moody’s newfound agility has broken down silos, leading to impressive growth. A recent survey reveals that 86% of customers view Moody’s as a trusted innovation partner.

Moody’s Analytics model for Empathy at Scale.
Moody’s Analytics model for Empathy at Scale.

Omni-Channel Customer Support

Customer support often feels like navigating a maze for customers and support agents. Erin Kurusz, Dell’s Vice President of Services Transformation, and Gautam Kaura, Senior Director of Services and Applied Data Science at Dell, introduced a game-changer: the Next Best Action capability powered by AI.

This tool equips agents with recommended solutions, allowing them to provide feedback for continuous improvement. Agents can also ask questions, connecting them to valuable resources. The results are staggering: since its implementation, Dell’s customer support has seen $50 million in cost savings, 37% fewer repeat work orders, and a 14% improvement in resolution times. Dell’s digital resolution journey is a resounding success, with plans to make data more accessible and streamline customer-agent connections.

Bridging the Trust Gap

Salesforce envisions AI as a co-pilot, enhancing human capabilities in customer success. They’re building networks of agents that combine AI language skills with human action-taking abilities, promising personalized and efficient service.

Salesforce’s Einstein for Service cloud integrates AI into customer service productivity with features like chat replies, recommendations, and AI-generated knowledge articles. However, a significant hurdle is the “AI trust gap,” with 52% of consumers doubting AI’s safety and security.

To bridge this gap, Nick Sweers, Senior Director of Digital Customer Success at Salesforce, introduced the Einstein Trust Layer. It ensures secure data handling and monitors AI-generated responses for toxicity. This approach fosters trust and transparency in AI-driven customer support.

AI and Quantum Computing: Shaping the Future

In AI, the journey ahead is far from linear; it’s a dynamic, multifaceted transformation that demands thoughtful navigation. Bryan Belmont, Microsoft’s Vice President for Customer Service and Support, emphasizes the need for a responsible and inclusive approach to AI. He believes that AI’s benefits should be accessible to all.

Belmont underscored the importance of addressing critical impact considerations during his conference presentation. This includes tackling societal and ethical concerns head-on, ensuring equitable access to technology and addressing the environmental impact of energy-hungry data centers. For AI to truly benefit humanity, these challenges must be met with innovative solutions.

Crafting a Robust AI Strategy

To navigate this evolving landscape, Belmont suggests that every company should craft a robust AI strategy. He highlights the pivotal roles of people, data, knowledge, and scale in effectively implementing AI. In this journey, humans and AI must collaborate seamlessly, much like pilots ensuring the safety of autonomous planes in aviation.

Quantum Computing: A Disruptive Force

Belmont’s presentation didn’t stop at AI; he foresees the disruptive potential of quantum computing on the horizon. With the power to revolutionize problem-solving and encryption methods, proactive preparation for this technology is essential. Quantum computing could reshape industries and drive innovation in unforeseen ways.

In closing, Bryan Belmont echoed Microsoft’s CEO in predicting a profound impact of AI in the near future, akin to the transformative influence of cloud computing over the past decade. The key takeaway? Early readiness is paramount. A balanced approach, considering both short-term implications and long-term paradigm shifts in the AI landscape, is the recipe for success.

The road ahead is exciting and filled with possibilities. AI and quantum computing are poised to shape the future, and organizations that embrace these technologies with responsibility and foresight will lead the way into this new era.

Sustainable Progress and the Future of AI: Insights from TSIA

Kevin Bowers, Director of Field Services Research at TSIA, led a panel with three industry experts from Salesforce, TSIA, and NCR Atleos on data science in practice. The panel represented a broad range of companies: both software and hardware of varying company sizes. Together, they explored various facets of AI, from development to governance, and offered invaluable insights.

Accessible AI Development

The landscape of AI development is undergoing a profound transformation. In the past, data scientists grappled with mountains of unstructured data, but AI has brought about positive changes. There’s less manual labor involved and the need for specialized skills is diminishing. Jeremy DalleTezze, Senior Vice President of Analytics and Software Engineering at TSIA, sums it up succinctly: “Data science literacy will not be too far out of reach for anyone.

Bill Girzone, Senior Vice President of Global Field Services at NCR Atleos, shared their strategic approach, maintaining a small in-house team while outsourcing development to partners on the cutting edge. Regardless of the approach, one thing is clear—the accessibility of data science has never been greater.

Centralized Strategy, Decentralized Innovation

The question of who steers the ship regarding AI innovation, development, and implementation arose. Salesforce follows a model of “centralized strategy and decentralized execution,” granting individuals the freedom to innovate within predefined guidelines. DalleTezze emphasized the importance of a central goal and oversight while recognizing the potential for AI gains in every department.

Bill Girzone highlighted a critical point: before delving into AI, you must master data management. Understanding how to leverage data is the foundation for effective AI utilization. Both Salesforce and TSIA stressed solid data governance and management processes as prerequisites for progress. The resounding message? Data science and AI are inseparable—there’s no AI without data.

Sustainable Progress in AI

Towards the end of the conference, a team from Microsoft presented a talk on sustainable progress in AI. In a world where AI is everywhere, Daniel Pickworth, Head of AI and Innovation at Microsoft Customer Service and Support, reminded the audience of the importance of sustainable development. While the hype around generative AI is peaking, he cautioned against a “deploy first, ask questions later” mentality. Rushed AI deployments could lead to bloated processes and siloed operations.

Instead, Pickworth advocated for a measured, deliberate approach. Start by understanding how AI is already being used across your organization. Speak with customer success teams, support agents, sellers, and customers to identify gaps and critical use cases. Sharing knowledge and building foundational processes that include the human element is crucial for sustainable AI deployment.

In this rapidly evolving field, sustainable progress is the key to harnessing AI’s potential. The journey ahead is exciting, and it’s all about finding the right balance between technology and human ingenuity.

AI’s Ongoing Evolution: Where Are We Heading?

The journey through the Life Cycle Approach to Data Science and AI track at TSIA’s ENVISION Conference was highly enlightening for conference attendees. From exploring the depths of generative AI to uncovering its real-world applications in the tech industry, we’ve witnessed the unfolding of a transformative era.

However, amid all the cutting-edge advancements, two overarching themes emerged. Firstly, companies are in the early stages of experimenting with these innovations, seeking to integrate them into their existing business models seamlessly. Secondly, the challenge lies in aligning these new breakthroughs with the people, processes, and technologies already in place.

Hirak Vora, Vice President of Customer Intelligence at Salesforce, captured the essence of the current landscape perfectly: “AI is not new, and we’ve all been using it in various forms… [but] the promise of AI has not been fulfilled yet.” It’s a reminder that despite our strides, we’re only scratching the surface of AI's potential.

As an industry, we’re on a collective journey of discovery. We have much to learn from one another as we navigate the uncharted waters of AI and data science. The road ahead is promising, and together, we’ll continue to unlock the true power of AI.

Where Do You Stand in the AI Capabilities Landscape?

The transformative journey of AI is constantly unfolding. Keeping up with its pace can be daunting. Cutting through the noise becomes vital, and resources like the TSIA AI Capabilities Landscape emerge as essential beacons, offering clarity, direction, and a grounded perspective, ensuring stakeholders can navigate the AI-driven future with informed confidence.

With AI’s undeniable ascent, where does your company stand? Are you already surfing the AI wave or gathering insights at the shoreline? Download the “TSIA AI Capabilities Landscape” report today to find out and join our Enterprise AI in Technology and Service Operations Research Journey to learn how technology companies can manage the deployment of AI capabilities today.

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