SShortSingh.
Back to feed

AI agents shift software switching costs from tools to trained business processes

0
·3 views

AI agents are changing how businesses evaluate software renewals by moving switching costs. Previously, switching tools required exporting data and retraining teams, creating friction. With capable agents that can learn new tools quickly, workflows can live in agent instructions rather than app configurations. This makes the trained agent containing business knowledge the valuable asset, not the software subscription itself. Businesses should now assess renewals based on whether software serves as essential systems of record for agents.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

Data Freshness, Not Real-Time, Key for Market Insights

A blog post on DEV Community argues that most businesses do not require true real-time market data. Instead, teams should ensure data is fresh enough to support the specific decisions it informs. The key is to define a maximum acceptable age for data before acting on it becomes risky, establishing a freshness service-level objective. This approach reduces false reactions and makes systems easier to debug by focusing on the relevance of data to the required action.

0
ProgrammingDEV Community ·

Django 6.1 increases default password hashing iterations, affecting login speed

Django 6.1, released in August, increased the default PBKDF2 password hashing iteration count from 1.2 million to 1.5 million. This change increases CPU time for password verification by approximately 25%, adding 50-90 milliseconds per login. Benchmark tests confirmed the performance impact but showed the hashing process scales across multiple CPU cores. The framework automatically updates existing password hashes to the new standard when users next log in.

0
ProgrammingDEV Community ·

Study finds AI assistant recommendations for local businesses vary significantly by language

A researcher tested seven major AI models in late September 2026 across four cities. The study asked identical buyer questions in different languages, such as French, Dutch, German, and English, to see which local businesses the AIs recommended. Results showed dramatic differences, with leading businesses in one language often absent from recommendations in another language within the same city. For example, an insurance broker in Montreal was named 16 times in English queries but zero times in French. The findings indicate that businesses serving multilingual clients cannot rely on testing AI recommendations in just one language.