Staying Ahead of AI's Acceleration
As AI capabilities advanced rapidly, it became harder for organizations to maintain a shared, realistic understanding of what was emerging, what was hype, and what would matter over the next 3–5 years. Without a clear narrative grounded in signals and evidence, teams risked becoming reactive, chasing new developments after they surfaced instead of building informed readiness over time.
The Tech Trends report was designed to help senior business and technology leaders and enterprise architects track where AI and other emerging technologies are heading, so the organization could make better long-range decisions about readiness, responsible adoption, and where to focus attention. Over time, AI increasingly touched almost every trend area, making it important to frame AI's evolution in a way that was actionable across business units, not just as a standalone topic.
Turning Signals into an Enterprise Point of View
We treated Tech Trends as an annual synthesis of signals, not a retrospective summary. I authored AI-focused sections by combining external research and industry reporting with perspectives from external ecosystem partners (including startups and industry operators), then translating what we observed into an enterprise lens: what was emerging, what was becoming practical, and what teams should pay attention to next.
The annual report was organized around seven enterprise themes from Amplified Intelligence, Client Experience, FinTech, Workplace Technology, and others. To make the publication actionable and consistent across contributors, each trend followed a standardized decision template with sections such as a Trend Brief, Why it Matters, Key Signals, and others. An Action Meter and Strategy Alignment were team-defined, and I contributed input during drafting and applied them within my trends and reviews.
-
AI-assisted research and editorial workflow
(human validated)
To support scale and consistency across a large research set, I designed lightweight automation in Copilot using system prompts:
- Auto-tagged research documents to the seven themes.
- Surfaced cross-document signals and candidate trends across the tagged library.
- Provided writing feedback on trend drafts (including my own) to improve clarity and consistency.
We treated automation as a directional accelerator, not a source of truth. Our team validated outputs for accuracy and used them to sift through 250+ research documents more efficiently while keeping human editorial judgment as the final gate.
I focused my primary contributions on Workplace Technology and my secondary contributions on Amplified Intelligence, applying an AI lens to what changes how people work and how leaders can evaluate applied capabilities and value over time.
Building Shared Context for Smarter Decisions
The AI narrative work helped leaders and teams interpret rapid shifts in generative AI and emerging capabilities through an enterprise lens, so the organization could be less reactive and more deliberate about readiness and responsible use.
I delivered a "State of AI" briefing within a business unit, translating Tech Trends research into implications, sharing follow-up resources, and facilitating stakeholder Q&A tailored to their near-term needs.
Beyond formal communications, I used the research and narrative assets in an embedded way during project work, translating external signals into practical implications that informed what to explore next and how to frame tradeoffs under enterprise constraints.
Related Initiatives
AI Vendor Evaluation Framework
Supported the investment group as AI use cases accelerated by evaluating vendor and platform options for orchestration and adjacent capabilities. I created an evaluation scorecard and comparison matrix, co-authored the recommendation readout with the senior product manager, and presented findings to stakeholders.
Evaluation criteria included developer experience, integration fit (including retrieval-augmented generation patterns), observability, agentic capabilities, and solution maturity.
I recommended using LangChain as an initial foundation for rapid prototyping because it was mature for the moment and had broad ecosystem support and examples, reducing new-vendor procurement friction and accelerating early experimentation within enterprise guardrails. The team used it for internal prototypes, including a secure internal chatbot-style experience and an investment research Q&A concept.
In discussions with lead engineers, we surfaced a key tradeoff: LangChain abstractions were acceptable for rapid prototyping but raised concerns for complex, production-grade workflows. The team later chose to build a custom orchestration layer rather than continue with LangChain in production.
Copilot Studio Architecture Strategy and Scale Readiness
Partnered with a solutions engineer to represent architecture considerations and facilitated cross-functional alignment with platform owners and information security to decide on Copilot Studio environment segmentation by risk level.
The segmentation approach covered development through production stages (with steps in between), accounting for the iterative, non-deterministic nature of generative AI workflows and the need to move back and forth during development. Data access constraints were a key factor in the decision, including production vs non-production and masked data considerations.
Copilot Studio Prototype to Support Tech Trends Research
Built a Copilot Studio prototype to extract structured data points from research documents, enabling a second agent to analyze abstractions across sources and surface candidate trends. This was used by our internal Tech Trends team to support the research process, and it also helped us evaluate Copilot Studio capabilities and limitations firsthand as part of the 2026 Tech Trends work referenced here as supporting context.