IA FORUM upcoming events

TECH THOUGHT LEADERSHIP EXCHANGE: IS AI MOVING TOO FAST - AND IS IT TIME TO HIT THE BRAKES? (Wednesday, September 23, 2026)

Wed, 23 Sep 2026 19:00:00 GMT

An Unfiltered Conversation with Enterprise Technology Leaders

Presented by Stephen Harris, in association with the IA FORUM.

An “Open Network” PUBLIC virtual event - for IA FORUM members, their guests, and anyone interested in the event topic - to then be shared via the IA FORUM “public” YouTube channel, website, LinkedIn, and other marketing channels - and to live FOREVER via said platforms.

About THIS Tech Thought Leadership Exchange Event…

The warnings are becoming increasingly difficult to ignore.

Recently, AI researcher Jacob Coxon resigned from Anthropic after previously working at OpenAI, publicly warning about the potentially catastrophic consequences of the race to build increasingly powerful artificial intelligence. His warning was extraordinary: people inside the companies developing the world's most advanced AI systems genuinely believe the technology could ultimately threaten humanity itself.

And he is far from alone.

Across the AI industry, researchers, technologists and leaders from some of the world's most influential technology companies have increasingly warned that AI capabilities may be advancing faster than our ability to understand, govern, secure and control them. Calls to slow the development of frontier AI, strengthen independent oversight, establish meaningful safeguards and create mechanisms capable of stopping or pausing development when risks become unacceptable are moving from theoretical discussions into an urgent global debate.

Yet the race continues.

Technology companies are investing hundreds of billions of dollars. Models are becoming more autonomous and capable. AI is increasingly able to write code, conduct research, interact with external systems and perform work that previously required human intelligence. Companies, governments and nations are competing to lead the next generation of AI - creating enormous economic and geopolitical pressure to keep accelerating even when those building the technology themselves are warning that we may be moving too quickly.

So where is the line between innovation and recklessness?

During this special one-hour, IA FORUM raw & unfiltered conversation, we will move beyond the headlines and AI hype to examine one of the most consequential questions facing the technology industry and society today: Has the race to build increasingly powerful AI begun moving faster than our ability to make it safe?

Topics of discussion will include, yet will not be limited to:

The Anthropic whistleblower warning and what it reveals about concerns inside the world's leading AI companies
Why AI researchers and technology leaders are increasingly warning that AI development is moving too fast - and whether the industry should deliberately slow down
Whether competition between OpenAI, Anthropic, Google, Meta and other technology companies has created an AI arms race that no individual company can realistically stop
Whether today's AI safety, alignment, security and governance mechanisms can possibly keep pace with rapidly advancing AI capabilities
Where the line between innovation and recklessness should be drawn - and who ultimately decides when AI has become too powerful or dangerous to continue developing at its current pace

Joe Fuqua, Head of Enterprise Intelligent Automation Architecture & Governance, TRUIST

Anurag Voleti, Vice President of Data Science, XSOLIS & Former Chief Data Officer, BLUECROSS BLUESHIELD

Murali Kashaboina, Ph.D., Data & AI Strategies Senior Leader & Consulting Chief Data Officer, HEALTH NEW ENGLAND

Host: Stephen Harris, Founder, Chief AI & Data Officer, GENEYE & Former Corporate Vice President, Global Head of Data Science & AI Growth Analytics, MICROSOFT

PANEL DISCUSSION: POINT-SOLUTION AI VS. GENERATIVE AI - IS GENAI ALWAYS THE RIGHT ANSWER? (Thursday, September 24, 2026)

Thu, 24 Sep 2026 19:30:00 GMT

Over the past several years, Generative AI has rapidly moved to the center of enterprise AI strategies, investment priorities, and transformation initiatives. Organizations across industries are racing to identify GenAI use cases, deploy enterprise platforms, and integrate generative capabilities across business functions. Yet as adoption accelerates, a critical question is emerging: is Generative AI always the right solution for the business problem being addressed – or are organizations overlooking more targeted, purpose-built AI solutions that may deliver greater accuracy, control, efficiency, and measurable business value?

For many enterprise use cases, point-solution AI - designed to address a specific business process, operational challenge, or defined outcome - may provide advantages over broader Generative AI approaches. At the same time, GenAI offers unprecedented flexibility, scalability, and opportunities to transform how employees work, customers engage, and organizations create value. As enterprises move beyond experimentation and AI hype toward scaled implementation and measurable ROI, leaders must become increasingly disciplined about determining which AI approach best aligns with the problem, risk profile, data environment, operating model, and desired business outcome.

Topics of discussion will include, but not be limited to:

How enterprises are determining when Generative AI, point-solution AI, traditional AI/ML, or a combination of approaches provides the strongest fit for a specific business challenge and desired outcome
Where purpose-built AI solutions may outperform broader GenAI approaches in areas including accuracy, reliability, governance, security, cost, and operational effectiveness
Whether the rapid enterprise push toward GenAI is causing organizations to prioritize technology trends over clearly defined business problems, measurable value, and ROI
How business and technology leaders can establish more disciplined frameworks for evaluating AI use cases, selecting the appropriate technologies, and measuring whether AI investments are delivering meaningful business outcomes

Muhammad Mohsin, Lead Product Manager, Treasury Solutions, VISA

Sarvesh Gupta, Senior Principal Engineer & Data Architect, ORACLE

Praveen Cherakkara, Technical Program & Engineering Leader, WHATABURGER

Ruchi Gupta, Director, GRC, IT Services & Support, Business Resilience, AI Transformation, SPRING HEALTH

Moderator: Angela McKeirnan, Director, Global Product Lifecycle Management and Master Data, SOLENIS

PANEL DISCUSSION: THE HIDDEN COST OF AI - ARCHITECTING FOR THE NEW ECONOMICS OF TOKENS, COMPUTE & INFERENCE (Wednesday, September 30, 2026)

Wed, 30 Sep 2026 20:00:00 GMT

As enterprises rapidly scale AI from experimentation to production, a new challenge is emerging: the cost of AI is no longer simply an infrastructure or procurement issue - it’s becoming an architectural and engineering problem. AI workloads introduce highly variable consumption patterns across tokens, models, compute, inference, storage, and data movement, making traditional approaches to application architecture and technology cost management increasingly difficult to apply.

This is changing how AI systems must be designed. Decisions around model selection and routing, workload placement, caching, context and token usage, GPU utilization, inference optimization, and the use of smaller or specialized models can have a significant impact on both technical performance and operating economics. As AI adoption expands across the enterprise, organizations must architect systems that balance performance, reliability, scalability, and user experience with the growing requirement to control consumption and demonstrate measurable business value.

Topics of discussion will include, but not be limited to:

How to design AI systems to optimize the economics of tokens, compute, inference, storage, and other rapidly growing consumption-based costs
How model selection, model routing, caching, workload placement, context management, and smaller or specialized models are being used to balance performance, scalability, and cost
How enterprises are addressing unpredictable AI usage and demand while avoiding over-provisioning infrastructure, unnecessary model consumption, and escalating cloud and GPU costs
How organizations are moving beyond traditional technology cost metrics to measure AI economics at the application, workload, use-case, and business-outcome level

Jay Dhar, Director, Emerging Capabilities & AI Enablement, U.S. BANK

Shrikant Sonparote, Lead Engineer, LOWE’S COMPANIES

Navita Singh, Principal Data Scientist (Research Engineering), AUTODESK

Moderator: Gaurav Basra, Chief Executive Officer, BASRA CONSULTING SERVICES