Q1. Could you start by giving us a brief overview of your professional background, particularly focusing on your expertise in the industry?

I am a results-driven technology and business leader with over 15 years of experience delivering strategic, innovative, and scalable IT solutions. My expertise lies in managing large programs, driving efficiencies, and leading the development and integration of AI-powered applications and digital platforms. I have a proven track record of successfully collaborating with stakeholders to define product visions, strategies, and roadmaps that align with business goals.

At Morgan Stanley, as an Executive Director - Technology Strategist, I have spearheaded the development of generative AI-integrated chatbots, prioritizing requirements based on business value, user needs, and technical feasibility. By collaborating closely with cross-functional teams and analyzing user data, I have continuously improved the chatbots' effectiveness and optimized their performance. Additionally, I have executed large-scale projects, delivering content publishing and experience platforms, search infrastructure, and virtual assistant capabilities, resulting in a significant increase in user satisfaction ratings.

My expertise extends to platform modernization, search optimization, and digital-centric product innovation. I have successfully integrated research and advice into knowledge management systems, revamped existing processes and platforms, and led the development of mobile and web-based tools for delivering market perspectives to financial advisors. With a focus on business intelligence and insights, I have implemented engaging digital capabilities and dashboards to track readership and determine advisor followership.

Throughout my career, I have demonstrated strong leadership skills by managing and developing high-performing operations teams across multiple locations. I have also ensured compliance with regulatory requirements, such as handling confidential data and implementing disclosure management systems.

 

Q2. What are some efficient strategies to reduce costs without compromising quality or service levels in generative AI-integrated chatbots?

Use Different Models: Handle simple questions with smaller, cheaper models, and save the powerful AI for complex queries.
Shorten Conversations: Make chatbot responses shorter and more direct to save on usage costs.
Pre-answer Common Questions: Have pre-written answers ready for frequently asked questions, so the AI doesn’t have to generate them every time.
Tune Existing Models: Instead of training new models from scratch, tweak the existing ones to suit your needs.
Schedule AI Tasks: Run heavy AI tasks during off-peak times when it's cheaper to process them.
Use a Knowledge Graph: For simple, factual questions, use a basic system that doesn’t need the full AI.
Monitor and Adjust: Keep an eye on the chatbot’s performance and make tweaks to improve efficiency over time.

 

Q3. How do you leverage your distribution network to improve overall supply chain efficiency and customer satisfaction in generative AI-integrated chatbots?

Use Local Servers: Place servers closer to users for faster responses.

Adjust Resources: Shift server power to where it’s needed most to keep things running smoothly.

Balance Requests: Spread user requests across multiple servers to prevent slowdowns.

Tailor for Regions: Customize the chatbot for different areas to make interactions feel more natural.

Store Common Answers Locally: Save popular responses on nearby servers for quicker replies.

Prioritize Urgent Queries: Make sure urgent issues get handled faster by focusing on location.

Collect Feedback: Use the network to gather data and improve the system over time.

 

Q4. Who are the main players in the industry, and what market share or position does each represent?

OpenAI: A leader with its GPT series, especially GPT-4, OpenAI is widely used across industries for various applications including chatbots and holds a significant market share.

Google DeepMind: Known for its powerful models like Gemini and its integration with Google’s vast infrastructure, Google is a top player, especially in search, AI research, and enterprise tools.

Microsoft (Azure AI): Through its partnership with OpenAI and its own AI research, Microsoft is deeply integrated into the market, especially with enterprise solutions via Azure.

Amazon Web Services (AWS): AWS offers a range of AI tools, including Lex, which powers conversational interfaces. AWS has a strong foothold in cloud services and AI integration.

Meta (Facebook AI): Meta focuses on AI for social platforms and conversational agents, pushing into the AI space with heavy research backing.

Kore.ai: Specializing in enterprise AI chatbots, Kore.ai is a key player in automating customer interactions, particularly in sectors like healthcare, banking, and retail.

IBM Watson: Known for its AI solutions in enterprise settings, IBM Watson provides strong tools for natural language processing and AI-driven customer engagement.

 

Q5. Is there opportunity for more price increases or are customers pushing back on pricing?

There's limited room for further price increases in the AI and chatbot space, as customers are starting to push back. Larger enterprises may still accept higher costs if they see clear value, but smaller businesses are more price-sensitive and may resist or seek alternatives. With increasing competition and market maturity, customers are becoming more aware of their options, making it harder to justify steep price hikes without offering significant improvements or benefits.

 

Q6. Are there any mergers and acquisitions/consolidation that are expected in the industry?

M&A are expected in the industry as companies look to grow and compete. Big tech firms like Microsoft and Google will likely keep acquiring AI startups to boost their platforms. Smaller AI companies might merge to stay competitive, and specific industries could buy AI firms to improve their services. Private equity firms are also interested in AI, which could lead to more buyouts.

 

Q7. If you were an investor looking at companies within the space, what critical question would you pose to their senior management?

As an investor, a critical question to ask senior management would be - How will you grow your technology and keep it effective and affordable?

This question addresses several key concerns:

Scalability: How the company will handle growth and increased demand?

Performance: Ensuring that technology remains effective and competitive as it scales?

Cost Management: Strategies for controlling costs and maintaining profitability while expanding?