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

I have a deep passion for technology and extensive experience as both practitioner and seller. I leverage customer insights to guide strategy and foster trust among stakeholders. My leadership at major companies like Cisco, AWS, and Microsoft is rooted in open communication, strong ethics, and a commitment to creating inclusive, high-performing teams. I have experience managing global business operations, love sharing expertise as a blogger and speaker, and driving success in both large enterprises and startups. My approach combines thoughtful strategy with disciplined execution, always focused on making a meaningful impact for my team and business.

Q2. What is the current global and regional market size for AI-powered customer success platforms, and how is this market segmented by industry vertical and customer size?

The customer success platform market size is currently about $ 1.8 B USD and is projected to grow to about $6B USD by 2030 (CAGR of about 22%). The North American market accounts for about 1/3 of the total market, and financial services and professional services are the two largest industries, each accounting for about 17% of the market.

Advancements in artificial intelligence and machine learning are transforming customer success platforms by enabling more sophisticated predictive analytics and automation. These technologies allow businesses to automate routine tasks, personalize customer interactions at scale, and proactively address potential issues before they escalate. As AI evolves, customer success platforms are expected to become even more integral to businesses looking to optimize customer engagement and retention strategies. New AI features are helping add context to interactions by aggregating essential customer information, engagement history, and activities into a single, succinct, useful summary at the click of a button. New tools like generative AI assistants have now been integrated directly into a Customer Success platform. These tools offer an unprecedented blend of qualitative and quantitative insights, providing users with the most current and accurate context for any form of customer engagement, whether it's meetings, re

Q3. From your point, what unique, high-impact market opportunities exist for new entrants and incumbent players to capture disproportionate market share—consider vertical specialization, geographic expansion, or strategic partnerships?

The practical application of new technologies and tools is always a good opportunity to capture rapid growth. These days, opportunities in the implementation of artificial intelligence for specific use cases (beyond the regular use of the now-common LLMs), the application of advanced cybersecurity, and the adoption of dev-ops tools and methodologies represent significant growth opportunities. These areas have broad horizontal impact across different vertical markets and geographies and are driving fast growth for the companies that offer them, as they directly address real business problems that organizations of all sizes face today.

Q4. How fragmented is the competitive landscape, and who are the dominant incumbents versus innovative disruptors shaping market dynamics?

The market of these technologies and tools, particularly AI and Cybersecurity, is very fragmented. Many new offerings are available, making competitive differentiation difficult, particularly now that AI is infused into Cybersecurity products and services. Many point products are being augmented with AI capabilities and features, further confusing and fragmenting the organizations that need them.

In the AI arena, the most prominent developers and model builders in the market (such as OpenAI, Antrhopic, Alphabet, Deepseek, NVIDIA, AWS, Microsoft, Meta, NVIDIA, Palantir, etc.) have now agreements with several AI infrastructure and services providers (Microsoft, AWS, Google, Oracle, IBM, etc.). This creates even more fragmentation and large barriers to entry for smaller, focused AI developers trying to bring innovation and disruption to the market, but lacking the global presence, resources, and funding required to introduce new services, models, and focused solutions.

Very innovative, but smaller model builders and developers such as Cohere, MistralAI, Hugging Face, Perplexity, and others have to rely on partnerships with the big cloud hyperscalers to bring their solutions to the market. At the same time, they compete with the hyperscalers' own solutions and a large menu of other options offered by their largest competitors on the same platform.

Q5. What emerging regulatory or ethical considerations might affect AI adoption rates in customer success platforms, and how are leaders building compliance and trust models?

Regulation (or the lack of) is an ongoing issue that is still preventing mass adoption in many industries, particularly those with complex legal and compliance requirements, as well as governments that require a higher standard of control and scope of work that is not fully framed or corrected just yet.

AI regulation varies worldwide, with major approaches including the European Union's comprehensive risk-based AI Act, the United States' sector-specific guidelines and state-level laws, and China's national strategy-focused approach. Many countries are developing national AI strategies and policies to balance innovation with risks such as data privacy, safety, and accountability, though a universal standard for AI governance has not yet been established.

Q6. Which emerging AI innovations will unlock the next wave of alpha in the next 3–5 years, and what indicators show early winners emerging in these segments?

This is hard to define. From a broad perspective, the emergence of Small Language Models is showing that for specific industry applications, the large models are not always better.

Vertical markets and industry-specific use cases are driving the creation of machine learning models with specialized capabilities, trained on well-defined datasets to deliver speed and accuracy for high-value, low-risk use cases. AI in the end is software, so many new developments on reasoning and deep learning capabilities are making the models faster, more efficient and cheaper to train and use.

Finally, the acceleration of Quantum Computing capabilities will also drive the next wave of growth and adoption of AI. Some leaders talk about a 20-30 year horizon for this new computer paradigm to arrive (mainly the current generation of chip makers), while others talk about 5-7 years. I am in this latter group perspective, and I see the value of having more, better, faster computing power with lower energy consumption as the bridge to the widespread adoption of AI in how we work, play, and live.

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

As investors, we are looking for innovation in practical applications, leveraging existing models in focused use cases with real ROI and value. Developing, training, and then running inference on new models, particularly LLMs, takes a lot of time, resources, money, and effort. Funding these initiatives is hard when the market is as fragmented as we described, and so many new options become available every day. I invest 50% in the quality and viability of the idea and 50% in the founding team behind the company.