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Jesse Shiah
Jesse Shiah is a technology entrepreneur and business leader based in Silicon Valley. He is located in Mountain View, California, and is actively involved in the technology sector.2
Professional Background:
- Founder, President & CEO of AgilePoint1
- Pioneered low-code/no-code innovation in 2003 and established AgilePoint in Silicon Valley in 20094
Technology and Leadership: He serves as the Conference Co-chair and former Program Co-chair of the US Taiwan High-Tech Forum (UTHF), an annual technology conference in Silicon Valley.2 His primary mission is to deliver disruptive digital advantage through integrating composable model-driven technologies. Since 2003, he has been a pioneer in low-code innovations, helping organizations from startups to Fortune 100 companies achieve process excellence and digital transformation.3
Professional Interests: His core passion is to "change the world" through his entrepreneurial efforts.5 He focuses on enabling citizen development, which he sees as a critical approach to democratizing and decentralizing application development.3
Community Involvement:
- President of NATEA Silicon Valley in 2019 and Vice President in 20186
Highlights
Operationalizing AI is imperative for accelerating returns on AI investments.
Last week, I attended the Forrester webinar "AI Strategy Across The Enterprise," delivered by Sudha Maheshwari, Michele Goetz, and Rowan Curran.
https://lnkd.in/g5HpZ-UA
Some survey results shared during the webinar were enlightening and depicted most organizations' challenges and realities when adopting AI.
1) “Who is Primarily responsible for each enterprise AI strategy at your organization?”
It is pretty intriguing to see the high level of involvement from CEOs. CEOs are not at peace with AI adoption because they know AI could make or break their business if they don't act.
Does this also explain why CEOs placed 'Efficiency and Productivity' last in Gartner's CEO Business Priorities survey in Q4 2023, while 'Technology Related' ranked second?
CEOs want AI to achieve strategic business goals rather than just incremental efficiency and productivity at the task level or within silos.
2) “Over what time period would your organization need to show a positive return on your AI investment to consider them successful?”
A massive 44% of organizations do not expect a positive return on their AI investment until after three years, which is alarming.
Lacking AI-ready data may still be a top reason.
Could it also be that, due to their deterministic nature, the existing IT systems and software applications cannot take maximum advantage of the unpredictable probabilistic nature of AI/GenAI? Therefore, the use of AI is limited to the task level or within silos that limit value creation.
Net; Net:
Due to the high cost of investing in AI, the best way to operationalize AI is through processes, especially end-to-end orchestrations that drive strategic KPIs and business outcomes that CEOs care about.
Using AI in conjunction with processes, AI can continuously guide processes to achieve business goals, including closed-loop optimization.
Yet many process solutions can leverage AI/GenAI at the task or activity level; they cannot support operationalizing AI at the goal-driven process or end-to-end orchestration level.
To accelerate ROI for AI investments and deliver strategic impacts, organizations must select process automation solutions that not only provide efficiency and productivity gains but also: 1) Generate AI-ready data 2) Respond to AI/GenAI's unpredictable probabilistic nature with the highest degree of agility
With an abstraction-enabled, codeless composable platform, the existing IT stack can be reused, and end-to-end orchestrations can be rapidly composed to generate AI-ready data - effectively future-proofing an organization to operationalize AI when ready.
#OperationalAI #AI #GenerativeAI #GenAI #Automation #Composable #ProcessAutomation #BPM #DPA #workflow #LowCode #Orchestration #Deterministic #Probabilistic

Why AI Needs a New Era of Probabilistic Adaptability Much attention these days has been focused on how AI will disrupt the software development paradigm, specifically Gen AI’s ability to generate code and render low-code, no-code tools obsolete.
The assumption fails to recognize that AI will change the characteristics of every application we build, disrupting the paradigm of software development as we know it.
The reason is simple. Almost all IT systems and software applications developed today are deterministic, i.e., you gather requirements and develop applications accordingly. But in the Age of AI, AI will continually uncover unpredictable new findings and insights, so we must prepare for entering a new era of probabilistic operations.
For process, this class of problem can't be easily pre-defined into structured automation and orchestrations that represent the mainstream implementation today.
As a result, deterministic computing is no longer adequate for the Age of AI. Technologies that only speed up the development of deterministic applications will not be adequate either.
For organizations to thrive and reshape their industries in the Age of AI, they must be able to create probabilistic-ready applications.
Therefore, when organizations begin to rethink their application strategies, selecting a next-generation application platform that supports probabilistic use case scenarios is a critical criterion.
One such proven architecture is a metadata-abstraction and model-driven composition-enabled codeless composability architecture free of code generation and compilation and without the need for virtual machines.
AgilePoint was designed and architected in 2003 to deliver such an architecture that enables probabilistic automation and end-to-end business orchestration.
AgilePoint's ultimate vision is to enable the creation of natively AI-ready applications and to provide AI with the ability to dynamically adapt running applications based on continually uncovered real-time findings and insights without modifying code.
The architecture naturally supports business process management and low-code use cases, but it is also more scalable, sustainable, and adaptable without incurring technical debt.
In 2007, Gartner discussed AgilePoint for the first time in its Cool Vendor in Business Process Management Report:
“For processes where the sequence of work is not well understood or that must change dynamically based on the context of the transaction in real-time . . . This class of process problem is not easily pre-defined into structured automation. AgilePoint’s internal design enables users to design and deploy processes that can be dynamically changed in real-time.”
The discovery implied that AgilePoint was architected in 2003 to be probabilistic-ready for the Age of AI.
#AI #GenAI #OperationalizeAI #OperationalAI #LowCode #ProcessAutomation #ProcessOrchestration #Deterministic #Probabilistic #Adaptability #ApplicationInnovation
