AI STRATEGY & IMPLEMENTATION
AI is a wilderness into which you should not venture without a guide.
AI is a wilderness into which you should not venture without a guide.
How should we refer to the current state of AI affairs as impacted in particular by ChatGPT and other LLM models and applications? Chaos? Confusion?
Maybe the best description is "Wilderness."
This wilderness is a particularly treacherous place for smaller organizations that lack the engineering and security resources of their larger brethren.
Large organizations may be able to move aggressively to explore and extract the riches from this AI wilderness because they can absorb the cost of their missed bets. Smaller organizations simply cannot afford that luxury.
When evaluating whether to invest in an AI initiative consider the following:
Risk notwithstanding, there are clearly opportunities for smaller organizations to benefit from investments in AI. But they must understand and plan to maximize the value while mitigating the risks.
The Indieus® mission is to help small to medium size organizations benefit from integrating AI into their business operations and product offerings in a way that will have a positive impact on the bottom line and/or company valuation.
"If you don't know where you're going, you'll end up someplace else." Yogi Berra
Work with with Marketing, Sales, Operations and Engineering to identify and evaluate GenAI and other AI opportunities for AI integration within your business processes and workflows. Above all, focus on projects that contribute to the bottom line.
Work with security and engineering to perform a cybersecurity audit to assesses the effectiveness of existing security measures against potential threats, identifying vulnerabilities in systems and processes. Help identify and evaluate AI solutions to the challenges of identifying and preventing cyberattacks.
Collaborate with executives, product managers, data scientists and engineers. Collaboration between leadership, product managers, engineers and data scientists can be challenging, particularly with respect to AI projects. The Indieus® mission is to bring years of experience in all those roles to facilitate communication between these stakeholders so all parties can participate in project success.
Historically only one in ten AI projects make it into production. To improve the odds of AI project success, focus on prioritizing a clear, business-driven approach from the outset. This involves defining specific, measurable goals aligned with strategic needs and technical resources, rather than pursuing AI for its own sake.
Failure to assemble the right team to help evaluate and monitor proposed projects is, based on my experience, one of the leading causes contributing to the "nine out of ten" failure rate of AI projects. So what is the right team? I strongly recommend the team include, in addition to the obvious members, product management and data science, representatives from other groups you might not have considered.
Specifically:
The typical response from leadership (and the data scientists) when I share this proposal is "Legal, security? Why? Won't they just gum up the works and slow things down?" The short answer is, no, this team will speed things up.
Engineering needs to be at the table from day one. It's sadly common that, after a substantial investment has been made to develop a minimal viable product, engineering is tasked with productization and reports that it's going to take way more resources and way more time than anticipated.
Data operations needs to understand the production data needs and participate in refining the delivery road map. Here too, it's sadly common that, after a substantial investment has been made to develop a minimal viable product we learn that while the test data was easy to get, delivery of production data is going to take way more resources and way more time than anticipated.
As for security and legal, it's virtually certain, or it should be, that they will get involved at some point. I've seen projects delayed by months or cancelled altogether because of security and legal roadblocks.
The challenge here is communication between members of the group who are new to the AI table and the core data science team. Given my background in business, data science, and engineering, I can help. With a one-day engagement when the team first meets I can serve as moderator and help create a foundation for effective and productive communication among the team members. Please invest a few minutes to read this article I wrote on the subject.
According to CSO Online 2024 was a year of clever breaches, showing just how wide the gap is between user habits and security practices."The year 2024 saw some of the most devastating zero-day and N-day exploits in recent memory, with a few of them even picked up by high-profile attackers to breach critical systems and launch nation-state level persistence."
Many smaller companies are familiar with the cybersecurity risk landscape (new attacks are reported almost daily) but lack the resources and the time necessary to perform regular audits to assure compliance with frameworks such as the NIST Cybersecurity Framework (CSF) and the NIST Risk Management Framework (RMF). And, in additional to the opportunity cost (your teams are busy and audits take time), an audit performed entirely by the groups being audited may not be the most reliable measure of compliance with the frameworks.
I'll collaborate with security and engineering to perform an audit based on the frameworks mentioned above. Our goal will be to assess existing assets and complete a controls and compliance checklist to determine which controls and compliance best practices need to be implemented to improve your security posture.
Are you developing a new product or adding a feature to an existing product? Do you have a sales team or others within the organization who speak directly to prospects or existing customers? If so then ignore this last item at your peril.
The several risks associated with a poorly trained sales team:
I founded Indieus® AI with the objective of applying my experience as a machine learning engineer and data scientist with over ten years experience in AI and twenty years software engineering experience. I'm also a Certified Professional Google Cloud Machine Learning Engineer, and earned the Google Cybersecurity Certificate.
My education includes a Masters in Computer Information Systems from Boston University and an MBA from University of Nebraska. In addition to my engineering experience I spent ten years successfully developing new international country markets for a banking fintech company. I established and managed their Japan subsidiary where I lived with my family for seven years.
What this means for my clients? I'm comfortable and capable working with leadership, finance, engineering, and the data science team. Let's talk AI.
Gary Biggs