Why treating AI as individual speedup fails

Businesses are spending millions on AI tools hoping to accelerate time-to-market but aren't seeing organizational-level results. Laura Tacho (CTO @ DX) explains why an "individual productivity" mindset fails and how AI merely accelerates the condition of the system it enters. She provides a framework for leaders to shift to a systems-level approach, find high-leverage ROI by looking outside the 20% of time spent coding, and understand what sets high-ROI orgs apart. Plus Laura shares data literacy tools to cut through the "whiplash" of conflicting AI reports and provides key considerations for 2026 budgeting, detailing where and how companies are planning to strategically invest.

ABOUT LAURA TACHO

Laura Tacho is CTO at DX, a developer experience company. She previously led teams at companies like CloudBees, Aula Education, and Nova Credit. She’s an expert in building world-class engineering organisations that consistently deliver outstanding results. Laura has coached CTOs and other engineering leaders from startups to the Fortune 500, and also facilitates a popular course on metrics and engineering team performance.

SHOW NOTES:

• Downsides to approaching organizational outcomes from an individual task level (2:59)
• Why individual product gains don’t always equate to systems-level improvements (4:56)
• How the quality of existing systems impacts the improvements AI can foster (7:26)
• Strategies for shifting mental models from the individual to systems level (9:09)
• Implement training & enablement techniques as an organizational lever (11:22)
• Common workflows that can unlock new problem-solving methods (14:46)
• Understanding what impact you want to see / getting the most ROI from AI (18:40)
• How to interpret the data when it comes to AI & its true ROI (21:22)
• AI data literacy for engineering leaders (23:06)
• Interpreting the meter study & what it means for engineers using AI (25:49)
• Quality vs. quantity when it comes to AI implementation on the org level (28:43)
• Characteristics that high-ROI companies possess when it comes to AI (30:35)
• Strategies to invest in that may lead to higher ROI (32:29)
• Laura’s observations on time & money budgeting / investments for 2026 (35:28)
• Embracing cost savings & opportunity generation as an eng org (38:08)
• Tackling fear / uncertainty when it comes to AI adoption, budgeting, & ROI (40:01)

LINKS AND RESOURCES

• Previous Episode with Laura Tacho
• Introducing the AI Measurement Framework from DX
• Atlassian State of DevEx Report
• METR Study
• DORA Report (2025)

This episode wouldn’t have been possible without the help of our incredible production team:

Patrick Gallagher - Producer & Co-Host

Jerry Li - Co-Host

Noah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/

Dan Overheim - Audio Engineer, Dan’s also an avid 3D printer - https://www.bnd3d.com/

Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/

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