ASQ Boulder Section 28 May 2026 Virtual Meeting video, slides
6:00 pm Presentation
· - Speaker: Melissa Tondi, AI Ops, Delivery Ops Strategist https://www.linkedin.com/in/melissa-tondi-186551/
· Topic: Operational AI - practical strategies
· NOTE: Topic changed as CO State updated Law (See Refences below)
· NOTE: Recording (75 MB) only access via ASQ National Membership
· NOTE: Will update with slides when available
· Presentation Recording and slides at https://my.asq.org/viewdocument/2026-05meeting-operational-ai-practical-strategies
· Non-ASQ Members Webex AI Summary at https://my.asq.org/blogs/arnold-miller/2026/06/04/boulder-may-2026-meeting-operational-ai-practical
7:15 pm Presentation Ended
7:20 pm meeting over
References:
· Original CO Government Bill SB24-205 “Consumer Protections for Artificial Intelligence
· LinkedIn "CO AI Act - Are You Ready Part 1 of 3"
· LinkedIn "CO AI Act - Are You Ready- Part 2 of 3"
· LinkedIn "CO AI Act - Are You Ready - Part 3 of 3"
· Updated CO Government Bill SB26-189 “Automated Decision-Making Technology
· LinkedIn "CO AI Act Are You Ready - May 2026 update"
AI Notes
Overview
The meeting featured Melissa, an experienced technology and AI enablement leader, who provided an in-depth discussion on the evolving landscape of AI regulation and operationalization. Initially set to focus on the Colorado AI Act, Melissa explained that recent legislative changes have significantly altered the act, removing compliance deadlines and many provisions, thus reducing its immediate impact. In response, she pivoted the discussion to practical strategies for operationalizing AI within organizations.
Drawing on over 25 years of experience, Melissa contextualized AI as the latest in a series of technology disruptors, including Y2K, Agile/DevOps, mobile/digital transformation, and cloud computing. She highlighted common patterns in how organizations respond to such disruptors, emphasizing the importance of learning from past experiences to avoid knee-jerk reactions like drastic layoffs or budget reallocations.
Melissa outlined the AI disruption cycle, noting that most organizations currently face challenges scaling AI adoption due to issues like lack of integration into workflows, unclear ownership, and insufficient governance. She shared her organization's journey in building an AI enablement practice, stressing the need for clear ownership models, proactive governance, AI literacy training, and measuring adoption based on meaningful value rather than mere deployment.
The discussion also underscored the critical role of internal and external AI usage policies to ensure ethical and transparent AI use, aligned with industry standards and legal frameworks. Melissa provided examples of how AI impact was measured across different functions, from technology to finance and sales, tailoring metrics to each area's goals.
Arnold, the meeting organizer, committed to updating materials and sharing resources, including Melissa's articles and policy templates, to support attendees' ongoing AI efforts. Future meetings will continue exploring AI governance topics, including relevant ISO standards, with plans for hybrid formats to accommodate diverse participants. Overall, the session offered valuable insights into navigating AI's operational challenges and regulatory uncertainties with a pragmatic, experience-informed approach.
Detailed Summary
Colorado AI Act Changes
Melissa discussed the significant changes to the Colorado AI Act, which was initially set to enforce compliance by June 30, 2026. The law, which focused on automated decision-making technologies affecting humans, was substantially gutted during the recent Colorado legislative session, removing the original deadlines and many of its provisions. This shift means the urgency and applicability of the act have diminished, with future developments dependent on upcoming legislative sessions.
· Melissa explained that the Colorado AI Act originally targeted consequential decisions made by AI affecting humans.
· Melissa noted the compliance deadline was delayed from February to June 30, 2026, but recent legislative changes have effectively nullified the act.
· Melissa highlighted that the act's repeal reduces immediate compliance urgency for companies.
· Arnold committed to updating meeting materials and sharing the revised articles and summaries with attendees.
Pivot to AI Operationalization
Given the changes to the Colorado AI Act, Melissa shifted the focus of the discussion to operationalizing AI within organizations. She emphasized the importance of understanding AI as a disruptor and integrating it thoughtfully into business operations rather than reacting impulsively. Drawing from her 25+ years in technology and recent experience leading AI enablement practices, she outlined how companies can approach AI adoption pragmatically to avoid negative impacts such as workforce reductions.
· Melissa shared her career background in software testing and AI enablement over the past three years.
· She stressed that AI should be operationalized as a mindset and cultural change, not just a technology feature.
· Melissa cautioned against knee-jerk reactions like drastic budget reallocations or layoffs due to AI.
· She proposed learning from past technology disruptors to guide AI integration effectively.
Historical Technology Disruptors
Melissa reviewed several major technology disruptors over the past 25 years, including Y2K, Agile/DevOps, mobile/digital transformation, and cloud computing. She identified common patterns in how organizations respond to these disruptors, such as initial hype, early adoption, scaling challenges, and eventual operationalization. These lessons provide a framework for understanding and managing the current AI disruption.
· Melissa described Y2K as a crisis that forced unprecedented cross-functional collaboration.
· She noted Agile and DevOps transformations often failed to fully adopt the necessary cultural mindset.
· Mobile and digital shifts introduced shadow IT and the need for infrastructure to support edge speed.
· Cloud migration sometimes resulted in 'lift and shift' without changing operational models.
· Melissa positioned AI as the latest disruptor following similar patterns.
AI Disruption Cycle and Challenges
Melissa outlined the AI disruption cycle stages: hype, early adopters, scaling attempts, stall, and sustainable operationalization. She observed that most organizations are currently in the stall phase, struggling to scale AI adoption effectively. Key challenges include lack of integration of AI tools into workflows, diffuse ownership and accountability, and insufficient governance structures. Addressing these issues is critical for successful AI operationalization.
· Melissa identified that AI tools often exist outside core workflows, limiting effectiveness.
· She highlighted that no clear ownership or support models for AI usage hinder scaling.
· Governance and accountability were often reactive and bolted on after adoption.
· Melissa emphasized the need to design operational models that anticipate and address these challenges.
Lessons from AI Enablement Practice
Melissa shared her organization's journey in building an AI enablement practice, starting with a single advocate and evolving into a company-wide AI operational model. Early mistakes included treating AI as a feature rather than an operating shift and lacking clear ownership. Success came from partnering with leadership across departments, establishing governance proactively, measuring adoption and value (not just deployment), and borrowing lessons from previous transformations like Agile and DevOps.
· Melissa recounted the initial assumption that technology alone should own AI enablement, which proved insufficient.
· They created a 'lab and crowd' model to balance R&D and broad adoption with representation across the company.
· They developed AI literacy training and established internal and external AI usage policies.
· Adoption metrics focused on meaningful value aligned with role-specific goals rather than blanket productivity targets.
Governance and Policy Importance
The discussion emphasized the foundational role of clear AI usage policies, both internal and external, in guiding ethical, safe, and productive AI adoption. Melissa recommended aligning these policies with industry standards and legal frameworks like the Colorado AI Act. She also stressed the importance of transparency with customers about AI usage. Governance structures must be proactive and continuously updated to remain effective.
· Melissa advocated for a definitive external usage policy to communicate AI use to customers.
· Internal policies should align with external commitments and govern employee AI use.
· Policies should be living documents, reviewed regularly for relevance and accuracy.
· Melissa offered to share example policies to assist organizations in developing their own.
Measuring AI Impact Across Functions
In response to questions, Melissa explained how productivity and value from AI adoption were measured differently across organizational functions. In technology, AI reduced lead and cycle times, increasing delivery speed and quality. In finance and HR, AI enabled time and cost savings through tool consolidation and process efficiencies. Sales and marketing benefited from new revenue opportunities identified via AI. Metrics were tailored to each function's goals and outcomes.
· Melissa reported technology teams halved their delivery cycle times using AI tools.
· Finance and HR saw measurable time and cost savings from AI-enabled process improvements.
· Sales and marketing leveraged AI to uncover new revenue opportunities.
· Metrics focused on time savings, cost reduction, and growth rather than uniform productivity targets.
Future Meeting Plans and Resources
Arnold committed to updating meeting materials to reflect the pivot from the Colorado AI Act to AI operationalization topics. Upcoming meetings will continue exploring AI governance and standards, including ISO standards related to AI model creation. Resources such as Melissa's LinkedIn articles and AI usage policies will be shared with attendees to support ongoing learning and implementation efforts.
· Arnold will update meeting topics and materials to align with current AI operationalization focus.
· Future sessions will cover AI governance and ISO standards for AI models.
· Melissa's articles and policy examples will be distributed to attendees.
· Plans include hybrid meetings to accommodate remote and local participants.
DONE