Across eight essays this year documenting our deeper exploration of the generative AI shift in today’s innovation landscape, we identify 32 hard truths when it comes to generative AI transformation in 2024.
Check out last year’s 23 insights, which covered more of generative AI’s impact on various industries:
(1) Competitive advantages in generative AI are defined by a new kind of PMF: product-model fit.
(2) Capital spend in driving operational efficiency of model deployment will define a new kind of CAC: compute acquisition costs.
(3) Capacity, compliance, and costs (3Cs) before applications (A). As with most deep / frontier technologies, these 3C’s need to be made more accessible first before Gen AI’s application layer gains more widespread adoption.
Read more about these insights:
(4) AI integration and experimentation is internal process transformation as much as it is introducing new types of interactions to the external market.
(5) AI capabilities are ultimately built on data and in the fintech world, access to data also means having trusted relationships, with customers, regulators, and industry players.
Read more about these insights from our case study on Vietnamese fintech Finhay’s Gen AI journey:
AI Notes #17: What We Learned about leveraging AI/ML for a smart investment platform from Finhay
(6) For generative AI in the region, the most immediate impact from localized solutions will be seen in custom enterprise use cases, like the ones WIZ.AI is supporting with their enterprise LLM solutions and AI agent solutions built on top of their proprietary Bahasa and Thai language models, among others.
(7) Perceived value to customers is an important aspect of developing a viable product with generative AI. Gen AI images and videos are cool and all, but what matters at the end of the day for the generative AI ecosystem to flourish in an emerging market region like Southeast Asia is whether or not solutions make a difference to the status quo that they would pay for.
(8) AI is like water, but that is not enough for Southeast Asia. The main ingredients to build generative AI in Southeast Asia are still in short or unusable supply at this point in time. Even if a local generative AI company develops distribution and finds some meaningful perceived value to customers, there is still the matter of costs of scale and competition. The opportunity cost for adoption can also tend to be higher in markets where labor costs are lower (the perennial issue with AI commercialization in emerging markets even before gen AI applications). This makes the path to commercialization and mass adoption a lot less straightforward than launching on HuggingFace or getting an app viral on TikTok.
Read more about these insights in this commentary on an NFX piece on generative AI:
AI Notes #18: If AI is like water, then Southeast Asia needs to make its own
(9) Generative AI integration can only become as intuitive as how comprehensive its accessibility is across touchpoints, interfaces, and languages.
(10) Generative AI pivots go beyond having proprietary data to work with, but also fit with what the product needs to make the customer experience far more superior.
(11) Development, deployment, and maintenance of generative AI pivots is not just an engineering workflow.
(12) Having the right development partners can make generative AI pivots more cost-effective and sustainable.
(13) Generative AI pivots need AI-oriented leadership.
Read more about these insights from generative AI transformation case studies:
AI Notes #19: 5 Learnings on making a Generative AI pivot for startups
(14) Cooling technologies enable sustainable server and data centre development across the region.
(15) Diversification of AI chip supply chain will impact costs and funding spend.
(16) Business finance and corporate secretary providers drive entrepreneurial talent flows for generative AI development.
(17) Data processing automation speeds up AI use case development.
(18) Enterprise SaaS drives the commercial opportunity for AI.
Read more about these insights on developing AI-adjacent industries:
5 Industries Driving Generative AI Development in Southeast Asia | AI Notes #20
(19) Develop solid use cases by focusing on the user problem and developing generative AI solutions to address top pain points: First, substituting services with software. Second, focusing on work with high value, volume, or labor shortages.
(20) Develop a solution and tap into proprietary data. First, target pattern-based workflows with significant engagement and usage. Second, tap into proprietary data.
(21) Leverage zero marginal cost creation. Generative AI enables unlimited scaling of written content, images, and potentially even software and products, facilitating creation possibilities across various enterprise functions, from cold sales outreach to personalized marketing and compelling visual assets.
(22) Develop and innovate where the dominant players aren’t, can’t, or won’t. Identify areas where Generative AI can introduce new capabilities, incumbents are slow to adapt, or the dominant player lacks foresight regarding AI possibilities.
(23) Excel with compound AI rather than models. The most successful AI applications add value at the data and infrastructure levels, leveraging innovative architectural approaches such as chain-of-thought, tool usage, agents, and appropriate model selection for specific functions, rather than relying solely on a single monolithic model.
Read more about these insights:
AI-native enterprise applications is the future of enterprise SaaS
(24) Going back to the AI / data value chain, AI players from markets in Southeast Asia are more likely to have a competitive edge in the application layer where native applications are able to target highly localized knowledge and backoffice work and non-native apps are able to embed Gen AI to scale acquisition and retention strategies.
(25) A competitive edge also exists for Southeast Asia players in the infrastructure layer, where there is demand for localized data engineering foundations for businesses.
(26) Looking at it from a go-to-market standpoint, B2C is a lot more challenging than B2B given how tech barrier to entry is not high. The threat comes from non-native players with established traffic like TikTok for example, already developing or launching in-house, consumer and business customer-facing generative AI solutions.
(27) Even if an app were to capture new traffic and improve localized user experience, there are still the risks of feasibility (Can we make this (at scale)?) and market fit (will people use this?)
(27) While there is more opportunity to monetize on top of localized B2B generative AI solutions, there is still the typical SaaS challenge of going from SMB to enterprise customers and navigating long sales cycles.
Read more about these insights:
(28) Bridge knowledge gaps in generative AI use cases with a guided, internal hackathon.
(29) Bring clarity into the ROI of Gen AI use cases with management-driven criteria.
(30) Develop processes for the right people to optimize the specific demands of generative AI development and deployment workflows.
(31) Develop alignment between the operating model and user interface.
(32) Leverage a development platform able to adapt to different foundation models over time.
Read more about these insights:
5 Roadblocks in Your Gen AI Capabilities Development, and How to Solve Them | AI Notes #22
Paulo Joquiño is a writer and content producer for tech companies, and co-author of the book Navigating ASEANnovation. He is currently Editor of Insignia Business Review, the official publication of Insignia Ventures Partners, and senior content strategist for the venture capital firm, where he started right after graduation. As a university student, he took up multiple work opportunities in content and marketing for startups in Asia. These included interning as an associate at G3 Partners, a Seoul-based marketing agency for tech startups, running tech community engagements at coworking space and business community, ASPACE Philippines, and interning at workspace marketplace FlySpaces. He graduated with a BS Management Engineering at Ateneo de Manila University in 2019.
Home
Publications 
Podcasts 
Search