AI is becoming part of more decisions across the business, which means the way organizations manage it is evolving too. There are different ways to approach that shift and we’re curious to hear what others are prioritizing. Cast your vote below 👇
Thoughtworks
Information Technology & Services
Chicago, IL 838,940 followers
Global tech consultancy blending design, engineering and AI to help businesses thrive through innovation.
About us
We are a global technology consultancy that delivers extraordinary impact by blending design, engineering and AI expertise. For 30 years, our commitment to design-led thinking, engineering excellence and innovation means we prioritize people, build teams with strong technical foundations and embed AI into every step of the process – not just as a tool but as a mindset. It’s this approach that sets us apart, sparks bold ideas and empowers us to drive real, lasting innovation. We’re not preparing for the future – we’re defining it.
- Website
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http://www.thoughtworks.com
External link for Thoughtworks
- Industry
- Information Technology & Services
- Company size
- 10,001+ employees
- Headquarters
- Chicago, IL
- Type
- Public Company
- Specialties
- Agile Development (Scrum, XP, Lean), Retail Customer Experience Consulting, Management Consulting, Data Science and Analytics, Product Innovation, Open Source Communities, Continuous Delivery, User Experience Design, Agile Development Tools, Mobile Strategy, AI, Data and Analytics, Cloud, Platforms, Customer Experience and Products, Software Defined Vehicles, and Software Engineering
Employees at Thoughtworks
View 14k employees at Thoughtworks
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Updates
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Meet the finalists of the AI/works™ Innovation Hackathon! After an exciting journey of ideas, experimentation and innovation, these teams have made it to the final stage. The finals are happening this week, where our finalists will bring their ideas to the spotlight and compete for the top spot. A huge congratulations to everyone who has made it this far! Stay tuned as we reveal the winners of the AI/works™ Innovation Hackathon.
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As AI reshapes how organizations use data, the fundamentals matter even more. How do you make data easy to discover, understand and use while keeping governance in place? In the latest Tech podcast episode, host Nate Schutta is joined by Thoughtworks’ Lorisa Perdoci and Microsoft’s Lauren Hartman mic to explore the evolution from data products to data agents. They discuss the role of semantic and governance layers, the evolution of Microsoft Fabric and what it takes to build a data foundation ready for the AI era. 🎧 Listen to the full episode: https://lnkd.in/dwZ76AQF
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An AI coding agent can write code that looks right, compiles cleanly and passes local tests, yet still be wrong for the system as a whole. 🤖 That’s where harness engineering comes in. In their latest article, Jaya Simha Reddy Nandyala and Prabina Pani explore a practical pattern for making coding agents more reliable by combining: 👉 Guides that steer agents before they act 👉 Sensors that automatically verify their work 👉 Selective human gates for high-impact decisions The goal isn’t to constrain agents at every step. It’s to create the right environment for bounded autonomy, allowing agents to move quickly while protecting system integrity. Explore how teams can engineer the harness around coding agents and build more reliable agentic software delivery. 👉 https://lnkd.in/dVQBTwnU
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Can generative AI really turn every engineer into a 10x developer? 💭 The promise of AI-assisted software development often centers on speed: more code, generated faster. But high-performing engineering has never been about how quickly developers can produce code. In his latest article, Ricardo Piccoli argues that over-reliance on AI-generated code risks creating something harder to measure: cognitive debt. When developers become passive reviewers rather than active participants in building software, they can lose the deep system knowledge, craftsmanship and shared mental models that make great teams effective. That doesn’t mean GenAI has no place in software development. The question is where it adds value without sacrificing autonomy, mastery and human judgment. Read Ricardo’s perspective on why the path to better software may not be about creating “10x developers” with AI. 👉 https://lnkd.in/duc7rJiu
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The people who build a product won’t always be the ones maintaining it. For Vietnam And Friends, rebuilding Open Road meant creating a platform that could keep working beyond its original developers. The result: 2,637 audiobooks recovered, access restored across iOS and Android, and a platform built to be easier to sustain.
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There’s more than one thing standing between AI ambition and AI value. For some organizations, it’s complex legacy technology that makes every change harder. For others, it’s the time it takes to turn a promising idea into something customers can actually use. At Amazon Web Services (AWS) re:Invent, we’re bringing an answer to both: AI-powered modernization with AI/works™ + AWS Transform and rapid product development with AWS Agentic Catalyst + 3/3/3: https://lnkd.in/d-hzFDhu AWS Partners
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AI can accelerate legacy modernization. But speed alone doesn’t solve the hard part. In this conversation with EM360Tech, Shodhan Sheth explores where AI can genuinely help, where human judgment still matters and what leaders need to consider before applying AI to complex legacy systems. Watch this excerpt, then listen to the full episode: https://lnkd.in/dvBcgziK
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If AI makes code easier to produce, what becomes harder to get right? Who decides what an agent should build, and how do we know the result works? Could AI change which software is worth building rather than buying? And if individual tasks get faster, where does the time go across the rest of delivery? The latest issue of Tech to know follows these questions across software engineering, delivery, data and the changing role of SaaS. Read now 👇
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As AI agents take on more responsibility, how do we make sure they remain aligned with human judgment? In his latest article, Zichuan Xiong breaks down five controllers that shape agent behavior: prompts, instructions, skills, recipes and loops, each playing a different role in defining boundaries, directing actions and correcting outcomes. But control is only part of the picture. As agent systems grow, they also need shared, persistent knowledge. That’s where the graph comes in, providing the institutional memory agents and their controllers can draw on. Five controls. One ground. Read the full article: https://lnkd.in/dw38C7Mz
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