ABOUT

Don't Guess, by relying on experts who have not made the mistakes before.

​

With 10 years of AI academic research and building commercial AI systems, and having served​ in most operational business functions, including product management, product marketing, channel and partner development, sales, pre-sales, engineering, IT, and support, I bring an informed, experience to product and systems strategy, development and go to market.   All with a heavy dose of pragmatics and emphasis on measurable business value returns.

​

I have been fortunate to work with some great people, companies, technologies in the enterprise space, including Sun Microsystems, Oracle, Sybase, GE, Salesforce, and hundreds of others as customers and partners to develop and deliver extraordinary business value  (and a few duds along the way, that are more often than not more informative).

​

These experiences inform how I continue to engage with customers and colleagues and help create measurable, sustainable business value.   

​

Please drop me a line, share your thoughts and experiences on my blog, or connect with me on LinkedIn, review sample services I provide, and let me know how I can help you to achieve substantial, responsible business value.

​

A Bit More Background: AI and commercial enterprise systems

​

In university, I was fortunate to engage in early research in neural networks, building a parallel simulator and building and testing language production systems, modeling human language production experiments (more here).  For someone studying Cognitive Science, emulating neural networks made sense, since they are literally how human intelligence occurred.

​

The first wave of commercial AI in the 1980’s were powered by specialized LISP machines, which were supplanted by general purpose UNIX systems running LISP (my first job out of university was developing expert systems running on LISP machines; my second job was as LISP Product Manager at Sun Microsystems).    While LISP machines were ultimately relegated to the heap of commercial experiments, their legacy includes popularizing the graphical UI and mouse, and object-oriented programming, which are today prolific.  The accepted truth of the day was that intelligence is in the software, not specialized hardware.    Yet, fast forward to today.  Nvidia's meteoric rise selling GPU's and parallel software for ML.  History apparently does repeat itself.   Let's see what we can learn from it.

​

The second part of my career was in product management, marketing with mainstream commercial technologies, including at Oracle (RDBMS, enterprise apps, middleware), Sybase (RDBMS, client/server architecture, middleware), followed by multiple middleware and web app startups.   

​

The third part of my career has been in the enterprise app ecosystem, designing, building, evolving enterprise apps and architecture, particularly in the Salesforce ecosystem.  Having led product engineering teams, helped to run a Fortune 10 company Salesforce CoE, provided hands-on-keyboard and advisory consulting to enterprises, having served in product management teams at enterprise technology companies, I have learned a few lessons what distinguishes between technology hype and demonstrated commercial business value, including the new AI ML hype cycle we now find ourselves figuring out.

​

The most important lesson I learn is that no one has all the answers and can predict the future.  However, history does have a way of repeating itself.  I engage through this lens, gravitating towards empirical discovery and experimentation, and not believing everything I think.