Tech Transformed

Takeaways

• #Customersuccess is about helping customers get value from products.
• The perception of customer success is shifting from a cost centre to a revenue generator.
• #AI can enhance customer success by providing predictive insights and automating processes.
• Customer success teams are uniquely positioned to identify upsell and cross-sell opportunities.
• Collaboration between customer success and sales teams is essential for maximising revenue.
• The integration of AI tools can streamline customer success operations.

Summary

Ever wonder how to transform existing customers into your company’s most powerful growth engine? According to Gainsight’s Chief Revenue Officer, Marilee Bear, it starts with one deceptively simple principle: “helping customers get value from your product.” 

This #TechTransformed episode features Christina Stathopoulos, Founder of Dare to Data, in conversation with Marilee about the dynamic role of customer success – a powerful avenue for building deeper customer bonds, boosting retention rates, and ultimately achieving significant revenue gains.

Tune in to learn how to unlock the potential of customer success as a catalyst for cross-selling and upselling opportunities. Explore how to equip your customer success teams with commercial acumen and harness their potential as a secret weapon for business growth.

Whether you're a #CRO looking to optimise revenue or a business leader navigating the AI revolution, this episode offers invaluable insights into the future of customer success.

For the latest tech insights visit: EM360Tech.com

Takeaways

• #AgenticAI systems can string tasks together for efficiency.
• Real-world applications include supply chain optimisation and knowledge worker augmentation.
• #Dataquality is crucial for effective AI implementation.
• Education and understanding of AI's potential are essential for organisations.
• Governance is key to ethical AI deployment.
• Leadership is critical in adopting AI technologies.
• AI will create new job opportunities, not just displace existing ones.

Summary

Jeff DeVerter, Field Chief Technology Officer at Pythian, describes agentic AI as “little workers that are going to go off and all do a job. You're now a manager of these AIs that are going to go off and do some work and come back and give you that work product.”

In this episode of the #TechTransformed podcast, Christina Stathopoulos, Founder at Dare to Data, and Jeff DeVerter explore this concept, revealing how agentic systems are re-shaping real-world business scenarios. 

Imagine these agentic AIs as powerful “personal assistants” when empowering leaders to manage data, streamline workflows and drive commercial acumen. 

The discussion goes beyond the possibilities addressing how to prepare your data infrastructure, navigate ethical considerations, and understand AI’s impact on employees, delivering crucial takeaways for CIOs.

For the latest tech insights visit: EM360Tech.com

From the integrities of the human workforce embracing enhancing soft skills over hard skills in the enterprise tech space to the adoption of artificial intelligence (AI) agents in customer service, this conversation covers it all. 

In this episode of the Tech Transformed podcast, Shubhangi Dua speaks with Nikhil Nandagopal, co-founder and CPO of Appsmith, about the metamorphological impact of AI agents in the workplace. He particularly emphasises the need for organisations to hone in on the advancing capabilities of agentic AI while still maintaining a focus on human collaboration and security. 

Takeaways

• AI agents are autonomous entities designed to achieve specific goals.
• The centralisation of data through AI agents simplifies workflows.
• Conversational interfaces are becoming the norm for accessing information.
• Humans remain integral to AI workflows, acting as moderators.
• Job roles will evolve, requiring new skills and adaptability.
• Critical thinking is essential when interacting with AI outputs.
• Cybersecurity is a major concern with centralised AI systems.
• Self-hosting AI solutions can mitigate cybersecurity risks.
• The future of work will reward soft skills over hard skills

Chapters

00:00 Introduction to AI Agents and Their Impact

03:34 The Shift Towards Conversational Interfaces

05:07 Assisted Workflows and Human-AI Collaboration

10:05 Job Market Evolution in the Age of AI

13:23 Critical Thinking in the Age of AI

15:29 Cybersecurity Concerns with AI

20:31 Preparing for Cyber Threats in AI Systems

22:51 The Future of AI Agents in the Workplace

In this episode of the Tech Transformed Podcast, Jon Arnold, Principal of J Arnold Associates speaks with Nikola Mrksic, CEO of PolyAI, discussing all things AI, specifically in contact centres. From the benefits of automation to the emergence of the most trending subject of the year – Agentic AI.

Mrksic particularly spotlights some underutilised capabilities of AI such as how it can manage up to 90% of repetitive duties, allowing human agents to concentrate on other complex tasks. The conversation also explores the transition from basic service to a broader, more holistic customer experience, necessitating the need for rapid adaptation and experimentation.

AI in contact centers isn't just about cutting costs. This conversation shows how it can truly make a difference – giving agents the tools to shine, providing customers with better, more quality experiences, and even letting AI take care of tasks behind the scenes securely, so humans can focus on what truly matters.

Takeaways

• AI is a dominant force shaping technology today.
• Contact centers have a high volume of repetitive tasks suitable for AI.
• AI can automate up to 90% of tasks in contact centers.
• The role of AI is not just cost-cutting but improving service quality.
• Agentic AI can perform tasks on behalf of users asynchronously.
• Customer experience is now a key focus beyond just service.
• Companies must adapt quickly to avoid falling behind competitors.
• Failing fast and experimenting is crucial for success with AI.
• AI can provide insights that traditional methods miss.
• Investing in AI should be about solving problems, not just keeping up with trends.

Chapters

00:00 Introduction to AI in Contact Centers

02:01 Benefits of AI in Contact Centers

07:37 Transforming Customer Experience with AI

15:42 Understanding Agentic AI

21:27 The Shift from Customer Service to Customer Experience

30:25 Advice for Business and CX Leaders

The world is changing faster than ever. Businesses are drowning in data, yet struggling to extract the insights they need to stay ahead. Artificial intelligence (AI) holds the key, but traditional AI models are too slow, too static, and too disconnected from the real world. This is where real-time AI comes in. 

Real-time AI empowers businesses to make decisions in milliseconds, reacting to changing conditions and seizing fleeting opportunities. It's about more than just analysing historical data; it's about understanding the present and predicting the future, all in the blink of an eye.

Imagine a world where customer service agents have access to the most up-to-the-minute information, resolving issues before they escalate. Envision supply chains that dynamically adjust to disruptions, ensuring products are always available. Envision marketing campaigns that personalise experiences in real time, maximising engagement and driving conversions. 

But real-time AI isn't just about speed; it's also about accuracy. The time to embrace real-time AI is now. Businesses that fail to adapt risk falling behind in an increasingly competitive world. By harnessing the power of real-time data and intelligent agents, enterprises can tap into new levels of performance, innovation, and growth. 

In this episode, Shubhangi Dua, an editor and tech journalist at EM360Tech, speaks to Madhukar Kumar, the Chief Marketing Officer at SingleStore, about the transformative potential of real-time AI for enterprises.

Takeaways

• Real-time AI is essential for modern enterprises.
• The evolution from generative AI to real-time AI is significant.
• Data accuracy and freshness are critical for AI success.
• AI agents will collaborate to enhance business processes.
• Enterprises must manage data silos to improve efficiency.
• Smaller companies can leverage AI to create innovative solutions.
• Data governance is crucial for protecting sensitive information.
• Real-time AI can significantly improve user experience.
• AI will enable professionals to focus on higher-value tasks.
• Harnessing data effectively will be a key differentiator for businesses.

Chapters

00:00 Introduction to Real-Time AI and Its Importance

03:03 The Evolution of AI: From Generative to Real-Time

05:54 Real-Time AI in Enterprises: Advantages and Examples

11:01 The Future of AI Agents and Their Collaboration

16:47 Preparing Enterprises for AI: Data Management and Security

20:47 Business Advantages of Real-Time AI and Future Opportunities

In this conversation, Ryan Worobel shares his extensive experience in the technology sector, discussing the evolution from traditional monitoring to observability. He highlights the cultural and technical challenges organizations face during this transition, emphasizing the importance of collaboration and data management. Ryan also explores the role of AI in enhancing IT operations, advocating for a balance between automation and human expertise. He provides insights on implementing AI in organizations, the risks and opportunities associated with it, and the necessity of understanding company culture for successful adoption.

Key Takeaways

• Observability requires a cultural shift towards collaboration.
• Data management is crucial to avoid overwhelming teams.
• AI is transforming IT from reactive to proactive approaches.
• Organizations must start small when implementing AI.
• Understanding company culture is key to AI adoption.
• Uptime is essential; downtime is no longer acceptable.
• AI should supplement human expertise, not replace it.
• Effective data sorting can reduce noise in decision-making.
• Innovation is necessary to maintain a competitive edge.
• Organizations need to establish governance around AI usage.

Chapters

00:00 Introduction to Ryan Worobel and His Journey

07:37 Proactive vs Reactive Approaches in IT

13:31 Implementing AI in Organizations

19:31 Conclusion and How to Connect with Ryan

In this conversation, Sam Page explores the evolving landscape of digital experiences, emphasizing the shift from the information age to the experience age, driven by advancements in AI.

Learn about importance of creating meaningful digital interactions, the challenges posed by biases in AI, and the need for transparency and education in navigating these technologies. Sam introduces a framework for brands to understand, connect, and serve their audiences effectively, highlighting the potential of AI to enhance consumer experiences while addressing concerns about job displacement and data biases.

Key Takeaways

• The digital experience is rapidly changing with AI at the forefront.
• Brands must prioritize meaningful digital experiences to connect with consumers.
• The shift from the information age to the experience age is significant.
• AI can create unique experiences tailored to individual needs.
• Concerns about AI include job displacement and biases in data.
• Transparency and trust are crucial in AI adoption.
• Education about AI should start from a young age.
• Brands can leverage AI to enhance customer connections.
• Spotify's DJ feature exemplifies effective AI use in consumer engagement.
• Understanding, connecting, and serving are key components for brands in the AI era.

Chapters

00:00 The Evolution of Digital Experience

04:01 Transitioning from Information Age to Experience Age

08:03 Addressing AI Concerns and Biases

14:11 Navigating AI: Understand, Connect, and Serve

In this conversation, Phillip Mortimer discusses the transformative impact of AI on private markets, emphasizing the unique challenges posed by non-standardized data and the importance of balancing quantitative and qualitative insights in investment decisions. He highlights the significance of data privacy in AI applications and the evolving role of generative AI in automating workflows. Mortimer also addresses concerns about the future of AI, arguing against the notion of reaching an inflection point in returns due to existing limitations.

Key Takeaways

• AI is essential for navigating the complexities of private markets.
• Data scarcity and non-standardization make AI a necessity in finance.
• Human intuition remains crucial in investment decision-making.
• AI can enhance productivity but should not replace human judgment.
• Generative AI poses new data privacy challenges that must be addressed.
• Last mile SaaS can still thrive despite the rise of generalized AI.
• The future of AI is promising, with ongoing advancements in technology.
• Type 2 thinking in AI is a key area for development.
• Investors must consider the return on intelligence, not just ROI.
• AI's role in finance is to augment human capabilities, not replace them.

Chapters

00:00 Introduction to AI in Private Markets

02:56 The Role of AI in Data Analysis

06:00 Balancing Quantitative and Qualitative Insights

08:50 Data Privacy Concerns in AI

11:54 Generative AI and Last Mile SaaS

14:59 Future of AI and Investment Returns

In this conversation, Michel Spruijt discusses the integration of AI and robotics in various industries, emphasizing the importance of balancing automation with human oversight. He highlights the challenges of designing multifunctional AI systems and the critical role of data interpretation in ensuring ethical use. Michel also shares insights on how organizations can adapt to AI, the significance of curiosity in career development, and the evolving job landscape due to technological advancements.

Key Takeaways

• AI and robotics are transforming industries, including cleaning.
• The balance between automation and human oversight is crucial.
• Data interpretation is more important than just data collection.
• Organizations should start small with AI investments.
• Curiosity is key to identifying unique opportunities in careers.
• AI will create new jobs while automating others.
• Human oversight is essential for ethical AI use.
• Staying curious can lead to career growth and innovation.
• AI should complement human work, not replace it.
• Understanding AI's capabilities can enhance productivity.

Chapters

00:00 Introduction to AI and Robotics in Industry

03:00 The Balance of Automation and Human Oversight

05:56 Challenges in Designing Multifunctional AI Systems

08:48 Data Interpretation and Ethical Considerations

12:07 Adapting Organizations to AI

16:58 Identifying Unique Opportunities in AI Roles

The rise of artificial intelligence (AI) is transforming industries, and customer success is no exception. Current trends show a rapid increase in AI adoption. This is driven by the potential to personalise interactions, automate routine tasks, and gain valuable insights from customer data among other solutions. 

However, the transition requires careful consideration of how AI can blend with existing customer success practices. The goal is to ultimately develop a combination of AI capabilities and human empathy, leading to more satisfying and effective customer experiences.

This is where natural language processing (NLP) comes in. The power of NLP can be leveraged to understand customer queries and sentiment analysis to determine their emotional state. 

In this episode, Kevin Petrie, VP of Research at BARC, speaks to Kate Neal, Senior Director of Customer Success at Gainsight, about the evolving role of AI in customer success. 

Takeaways

• AI adoption in customer success is accelerating despite some hesitations.
• Gainsight provides a comprehensive customer operating system, “powered by AI”.
• Natural language processing can significantly enhance customer sentiment analysis.
• Human oversight is crucial in AI applications to ensure accuracy.
• Data quality is essential for effective AI implementation.
• AI can help reduce the administrative burden on customer success teams.
• Collaboration between data and AI teams is necessary for success.
• Understanding AI's capabilities is key for customer success leaders.
• AI is not a replacement for human jobs but a tool to enhance them.

Chapters

00:00 Introduction to AI in Customer Success

03:44 Gainsight's Role in Customer Success

07:11 AI Adoption Trends in Customer Service

10:51 Use Cases of AI in Customer Success

15:12 Natural Language Processing and Customer Sentiment

19:48 Human Oversight in AI Applications

22:06 Collaboration Between Data and AI Teams

23:59 Getting Started with AI in Customer Service