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Start 2026 With a Real AI Execution Plan
The Shift Brief | Week of January 5th, 2026
Ryan Erickson
January 06, 2026
Most financial firms do not have an AI problem. They have an execution problem.
After years of pilots, proofs of concept, and experimentation, the question in 2026 is no longer whether AI matters. It is whether it delivers measurable outcomes. The gap between activity and ROI usually comes down to focus, ownership, and integration.
If this is the year AI creates real value, it starts with a simple operating plan. Not a vision deck. Not another tool rollout. A short list of decisions that align teams, data, and workflows.
Here is a practical way to kick off the year.
1. Pick One Workflow That Actually Matters
Do not start with an AI strategy. Start with friction.
Choose a single workflow that is repetitive, document-heavy, and tied directly to decision quality, speed, or risk. Research synthesis, earnings preparation, investment committee materials, compliance review, or internal reporting are common starting points.
If the workflow lacks a clear owner, it is not a good candidate. Ownership drives adoption.
2. Make the Data AI-Ready First
Most AI initiatives stall because models are layered on top of fragmented data.
Before deploying anything, identify the core documents and data sources that power the workflow. Standardize access, structure, and ownership. Decide what is authoritative and what is reference only.
This step is unglamorous, but it is where most of the ROI is unlocked.
3. Embed AI Into Existing Tools
Adoption drops quickly when AI lives in a separate interface.
The highest-impact deployments occur within the systems teams already use. Research platforms, document workflows, and internal portals. The goal is fewer steps and less manual work, not a new destination.
4. Measure Outcomes, Not Usage
Usage metrics are misleading. Value shows up in outputs.
Define success in terms of time saved, reduction in manual steps, faster decision cycles, or improved consistency and risk detection. If you cannot articulate what improves when AI is added, pause the project.
5. Scale Only After One Clear Win
One proven use case creates internal credibility.
Once a workflow shows measurable improvement, expand to adjacent teams using the same data foundation and governance model. This is how AI compounds without creating chaos.
Ryan Erickson, Founding Executive
TLDR: What you need to know about AI in Finance
JPMorgan Shows What Operational AI Looks Like
JPMorgan continues to be one of the clearest examples of turning AI into real operational leverage. Rather than positioning AI as a front-office replacement, the firm is embedding it directly into internal workflows across research, risk, compliance, and operations. The focus is on document-heavy processes, signal extraction, and decision support where incremental gains compound at scale.
Global Competition for AI and Finance Infrastructure Is Accelerating
India’s Yamuna Expressway Industrial Development Authority recently announced plans to develop a 500-acre AI and finance hub designed to attract fintech firms, AI startups, and institutional capital.
This reflects a broader trend. Regions are competing to build AI ecosystems that combine talent, infrastructure, and capital. Financial innovation will increasingly emerge from a wider set of geographies, not just traditional financial centers.
The Bottom Line
2026 is not about doing more with AI. It is about doing fewer things better.
Firms that win this year will narrow their focus, fix their data foundations, embed AI into real workflows, and measure outcomes instead of hype.
AI is no longer the differentiator. Execution is.