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George Nychis
@gnychis
PhD graduate from Carnegie Mellon in 2013. Co-founder & CTO of Workfabric AI (
Boston MA
Joined July 2010
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222 Followers
3.2K Posts
@RohanMurty Learning from that organization's behavior to build a harness that adheres to their work was key to unlocking this level of value.
Bringing context across teams and continuously incorporating it to build a unique harness catered to them has been critical in what we've shown. That harness needs to adhere to their behavior, not general behavior learned to drive value.
Here is an actual implementation of what has been described, in a production environment, with $200M (and counting) to show the value of optimizing the human-AI context loop.
https://t.co/jkMnTPJMyG
After spending time inside real enterprise AI deployments, one thing becomes obvious: access to the latest model is not the differentiator. What @RohanMurty and @imravikumars describe is exactly the conversation happening inside companies everywhere. “We have access to the same tools as everyone else… so why aren’t we seeing the same impact?”
Across 200+ measured work patterns in 50+ large enterprises, the gap in who captured the most value had very little to do with the model or platform. It came down to how well the AI was grounded in how the organization actually works. Same technology. Very different outcomes.
Strongly recommend reading the article. It provides understanding around what many teams are already experiencing.
New in Harvard Business Review - from our founder @RohanMurty and @Cognizant CEO @imravikumars:
Context is Your Competitive Advantage.
The core insight from actual measurements in enterprises - when every company has access to the same AI models and platforms, the only remaining differentiator is how well you ground that AI in how your organization actually works.
200+ work patterns. 50+ large enterprises. One consistent finding: context, not technology, explains the performance gap.
This is a follow-up to an earlier HBR article, which was the first to show real-world proof that context-driven AI produces dramatically better outcomes. Same tools, radically different results.
At Workfabric AI, this is exactly what we do. We capture execution context, the decision patterns, coordination rhythms, and trade-offs that no system of record holds, and make it available to AI at the moment of decision.
Context compounds. And the companies that capture it first will be the ones that pull ahead.
@gnychis @guruprasad_r94 @NabeelQuryshi @juggy_17
https://t.co/MSboTkvLx5
The industry is building version control for AI-generated code, but the harder problem is version control for human work. Most enterprise decisions unfold across teams, systems, exceptions, and unwritten rules that leave no trace of how or why something actually happened. If we believe context matters for software, it matters even more for how organizations operate. The real opportunity is capturing the behavioral layer of execution and making it traceable, searchable, and eventually usable by agents.
https://t.co/6pKcMBBRL0
https://t.co/6pKcMBBRL0
The importance of a context layer is beginning to show up where enterprise priorities are set: earnings calls.
On @Cognizant's recent Q4 call, Ravi Kumar S spoke about Context Engineering as a strategic focus and named Workfabric AI as the platform partner behind it. That builds on Cognizant's earlier commitment to train and deploy 1,000 context engineers on ContextFabric to operationalize context across the enterprise.
Context has become a foundational layer. It signals AI maturing from models, to systems, to the layer where real work actually happens.
Looking forward to continuing the partnership with the Cognizant leadership team as they invest in making enterprise AI practical and operational globally.
Workfabric AI and Context Engineering are now being discussed on earnings calls!
On @Cognizant's (strong) Q4 earnings call, Ravi Kumar S (CEO, Cognizant) spoke about Context Engineering as a strategic focus and named WorkFabric AI as the platform partner behind it.
This builds on Cognizant Technology Solutions’s earlier commitment to train and deploy 1,000 context engineers on ContextFabric, our context engineering platform, to operationalize context across the enterprise.
Context is becoming infrastructure.
This is what it looks like when AI moves from models → systems → real work.
Proud to be partnering with @imravikumars and the Cognizant leadership team that’s building for where enterprise AI is actually going.
@RohanMurty @gnychis @NabeelQuryshi @guruprasad_r94
https://t.co/pXHSpQ4KFZ
Workfabric AI and Context Engineering are now being discussed on earnings calls!
On @Cognizant's (strong) Q4 earnings call, Ravi Kumar S (CEO, Cognizant) spoke about Context Engineering as a strategic focus and named WorkFabric AI as the platform partner behind it.
This builds on Cognizant Technology Solutions’s earlier commitment to train and deploy 1,000 context engineers on ContextFabric, our context engineering platform, to operationalize context across the enterprise.
Context is becoming infrastructure.
This is what it looks like when AI moves from models → systems → real work.
Proud to be partnering with @imravikumars and the Cognizant leadership team that’s building for where enterprise AI is actually going.
@RohanMurty @gnychis @NabeelQuryshi @guruprasad_r94
https://t.co/pXHSpQ4KFZ
One thing that consistently shows up in enterprise deployments is how much context exists beyond systems of record. Most organizations capture outcomes, but the real signal lives in the execution: the steps taken, systems consulted, approvals negotiated, exceptions made, and judgment applied as work happens. At scale, these digital work experiences number in the tens of billions per year inside a large enterprise, yet they have historically been ignored because there was no practical way to use them. AI changes that. This execution context is what agents need to operate with real situational awareness, and it is the missing layer that led us to build ContextFabric.
How much context is there really inside the enterprise?
The largest 2000 companies generate 50+ trillion digital work experiences per year.
After deploying real agentic workflows inside multiple Fortune 500 companies, one fact became clear: we are underestimating how much context exists inside the enterprise, and how little of it shows up in systems of record. The tribal knowledge of how teams work lives in digital experiences created as work happens. This is the context that matters most.
Here's what we've observed in deployments:
-Each person generates ~2,500 digital work experiences per day
-A 20-person team generates ~50,000/day
-Over ~250 working days, that’s ~12.5M digital work experiences per year for one small team
Scaled up, a 50,000-person company (median Fortune 500) generates ~31B digital work experiences per year. And this scale will only accelerate with more digitization of work.
For comparison, social media companies are considered data giants because they collect clicks, views, graphs, and engagement at global scale. Yet enterprises generate roughly 8x more data in digital work experiences than all social media interaction data combined.
This dataset was ignored for decades because there was no practical way to use it. Social media turned interaction data into trillion-dollar ad engines. Enterprises had no equivalent engine.
What are these “digital work experiences”?
Execution traces of how teams actually work: steps, sequence, systems touched, approvals, exceptions, dependencies, workarounds, and handoffs across email, documents, CRMs, ERPs, ticketing tools, browsers, spreadsheets, and legacy software.
This is the context that matters most because it’s where judgment lives.
Systems of record capture the outcome: a form, a finalized doc, a deal marked closed, an email sent. But there is often an order of magnitude more signal behind the outcome than in the outcome itself: the checks run, sources consulted, policies applied, exceptions allowed, approvals required, and the reasoning that made it correct here.
Example: a workflow that looks simple in the CRM, “generate an account renewal email,” is rarely simple. The real work involves pulling usage and billing from multiple systems, understanding prior concessions, applying pricing and approval policies, coordinating with finance and legal, and relying on tribal knowledge about similar renewals. None of that execution context is captured.
AI changes that.
These execution traces are precisely what you need to build a company’s context backbone, a living layer that reflects how work actually gets done: what mattered, which policies applied, where exceptions were made, who approved what, and why.
Context is not a feature. It is the missing substrate that allows agents to operate with real situational awareness inside a company.
That’s why we are building ContextFabric.
@gnychis @RohanMurty @NabeelQuryshi
How much context is there really inside the enterprise?
The largest 2000 companies generate 50+ trillion digital work experiences per year.
After deploying real agentic workflows inside multiple Fortune 500 companies, one fact became clear: we are underestimating how much context exists inside the enterprise, and how little of it shows up in systems of record. The tribal knowledge of how teams work lives in digital experiences created as work happens. This is the context that matters most.
Here's what we've observed in deployments:
-Each person generates ~2,500 digital work experiences per day
-A 20-person team generates ~50,000/day
-Over ~250 working days, that’s ~12.5M digital work experiences per year for one small team
Scaled up, a 50,000-person company (median Fortune 500) generates ~31B digital work experiences per year. And this scale will only accelerate with more digitization of work.
For comparison, social media companies are considered data giants because they collect clicks, views, graphs, and engagement at global scale. Yet enterprises generate roughly 8x more data in digital work experiences than all social media interaction data combined.
This dataset was ignored for decades because there was no practical way to use it. Social media turned interaction data into trillion-dollar ad engines. Enterprises had no equivalent engine.
What are these “digital work experiences”?
Execution traces of how teams actually work: steps, sequence, systems touched, approvals, exceptions, dependencies, workarounds, and handoffs across email, documents, CRMs, ERPs, ticketing tools, browsers, spreadsheets, and legacy software.
This is the context that matters most because it’s where judgment lives.
Systems of record capture the outcome: a form, a finalized doc, a deal marked closed, an email sent. But there is often an order of magnitude more signal behind the outcome than in the outcome itself: the checks run, sources consulted, policies applied, exceptions allowed, approvals required, and the reasoning that made it correct here.
Example: a workflow that looks simple in the CRM, “generate an account renewal email,” is rarely simple. The real work involves pulling usage and billing from multiple systems, understanding prior concessions, applying pricing and approval policies, coordinating with finance and legal, and relying on tribal knowledge about similar renewals. None of that execution context is captured.
AI changes that.
These execution traces are precisely what you need to build a company’s context backbone, a living layer that reflects how work actually gets done: what mattered, which policies applied, where exceptions were made, who approved what, and why.
Context is not a feature. It is the missing substrate that allows agents to operate with real situational awareness inside a company.
That’s why we are building ContextFabric.
@gnychis @RohanMurty @NabeelQuryshi
Smarter models are no longer the barrier to deploying AI for real problems in enterprise - context is. Your AI is smart enough to be a teammate, it just doesn’t know what it means to do a good job.
I’m excited to be supporting @workfabricai led by @RohanMurty and @gnychis!
This use case highlights something we see in nearly every enterprise AI deployment. Tools can be fast, but without the right context they rarely change outcomes. A Fortune 500 contracts team using a generic AI drafting tool still spent most of their time gathering inputs, approvals, exceptions, and unwritten workflow rules that lived outside any single system. The issue was not the model’s ability to generate language. It was the lack of operational context that makes each contract situation unique. Once real work patterns and decision logic were captured and delivered at the moment of work, the AI began producing drafts that were situationally relevant and materially improved both cycle time and quality. This is not about better models. It is about giving AI access to the context of how work actually happens.
Not theory. Not abstract AI advice. Production.
These are real lessons from a live Fortune 500 retailer deployment last year, and they explain why we built ContextFabric.
The durable enterprise AI moat is context derived from execution traces of how teams actually work - the systems they touch, the checks they run, the exceptions they allow, and the approvals they require. And this is specific to each team in each org. It is not easily replicable.
From our HBR article (April 2025) written by @RohanMurty @imravikumars (CEO, @Cognizant) and @gnychis
https://t.co/lbKc4mb078
Not theory. Not abstract AI advice. Production.
These are real lessons from a live Fortune 500 retailer deployment last year, and they explain why we built ContextFabric.
The durable enterprise AI moat is context derived from execution traces of how teams actually work - the systems they touch, the checks they run, the exceptions they allow, and the approvals they require. And this is specific to each team in each org. It is not easily replicable.
From our HBR article (April 2025) written by @RohanMurty @imravikumars (CEO, @Cognizant) and @gnychis
https://t.co/lbKc4mb078
AI agents don’t fail because models are weak. They fail because they don’t understand how work actually happens inside a company. The real rules live in exceptions, handoffs, workarounds, and human judgment that never make it into documentation, systems of record, or data schemas.
That missing context is the bottleneck.
ContextFabric exists to solve that problem. It learns directly from real work as it happens across your organization and turns lived digital experience into structured, usable context for AI.
That context becomes shared infrastructure. Every enterprise AI agent receives the specific operational intelligence it needs to take the right actions, handle edge cases, and keep working as reality changes.
Backed by @bobmcgrewai (@OpenAI, @PalantirTech) and Chris Ré (Stanford AI Lab, @TogetherAI, Snorkel), whose support has shaped our direction.
If you are struggling with AI agents that work in theory but not in production, or want to help build what comes next, reach out at https://t.co/XGEZx6X4FG and [email protected]
#ContextEngineering #EnterpriseAI #AIAgents
The most important asset companies will have in the AI era is context, a durable record of how organizations actually get work done.
Most enterprise AI still treats context as static input - documents, emails, and stored records. But real businesses don’t run only on static data. They run on something far more dynamic and specific to them - decisions, exceptions, approvals, judgment calls, and hard-won operating knowledge that emerges in live workflows, handoffs, escalations, and day-to-day collaboration. This context is specific and unique to each team within each organization.
Today, that context rarely survives. A discount is approved, a contract is escalated, or an exception is granted. The system of record captures the outcome, but the reasoning as to why and how it happened disappears. No replay. No audit. No precedent. But that is the exact truth that AI needs to have situational awareness, serve your teams, and produce ROI.
That’s the gap we are building ContextFabric to fill.
ContextFabric turns your teams’ lived work into an execution backbone for AI:
-Learns intent from real workflows and work patterns
-Delivers the right context at the moment decisions are made
-Powers every agent with shared, governed enterprise context
This is how AI moves beyond pilots and becomes a true digital teammate, one that is embedded in daily operations, operating within real constraints, and delivering durable enterprise value.
Context is the enduring competitive advantage in the AI era. Read more about ContextFabric in the link below (in the comments section).
If you’d like to learn more, we’d love to hear from you. Feel free to reach out to us ([email protected])
We just launched a new site. See what we’re building: https://t.co/dp7xvDj7kJ
@RohanMurty @gnychis @guruprasad_r94 @NabeelQuryshi
The most important asset companies will have in the AI era is context, a durable record of how organizations actually get work done.
Most enterprise AI still treats context as static input - documents, emails, and stored records. But real businesses don’t run only on static data. They run on something far more dynamic and specific to them - decisions, exceptions, approvals, judgment calls, and hard-won operating knowledge that emerges in live workflows, handoffs, escalations, and day-to-day collaboration. This context is specific and unique to each team within each organization.
Today, that context rarely survives. A discount is approved, a contract is escalated, or an exception is granted. The system of record captures the outcome, but the reasoning as to why and how it happened disappears. No replay. No audit. No precedent. But that is the exact truth that AI needs to have situational awareness, serve your teams, and produce ROI.
That’s the gap we are building ContextFabric to fill.
ContextFabric turns your teams’ lived work into an execution backbone for AI:
-Learns intent from real workflows and work patterns
-Delivers the right context at the moment decisions are made
-Powers every agent with shared, governed enterprise context
This is how AI moves beyond pilots and becomes a true digital teammate, one that is embedded in daily operations, operating within real constraints, and delivering durable enterprise value.
Context is the enduring competitive advantage in the AI era. Read more about ContextFabric in the link below (in the comments section).
If you’d like to learn more, we’d love to hear from you. Feel free to reach out to us ([email protected])
We just launched a new site. See what we’re building: https://t.co/dp7xvDj7kJ
@RohanMurty @gnychis @guruprasad_r94 @NabeelQuryshi
@Citi my credit card is declining a legitimate transaction I'm trying to make will all information correct, including a 2-factor text message for the "ID Check" technology. I called for help, and am placed in a 30+ minute hold. What kind of terrible customer service is this???
@JennyENicholson Take a look at the ThermalStrike Ranger. You can heat and monitor the temperature to ensure a high enough heat to kill bed bugs but not melt things. Putting a towel around the interior will also help ensure no direct heat. I used this for all of our plushies, none damaged
@Bennndji @beckycwebs I would think that amount of time from now it should be clear and with all of the wind on the island (part of the problem with the fires...) as long as it's not burning fresh air will constantly be coming through.
@GemmaPeckham When you were walking down the road to the lighthouse, was it before or after the fire went through Kiotari? My family's house is on that same road so I'm trying to understand how far down the fire came to that road.
@British_Airways I purposefully didn't check a bag because you lose them. Was told Zones 8-10 needed to check rollers in Amsterdam. I came in Zone 6 and they insisted they checked my bag. I argued. They took it. Now it's missing. Huge waste of time. And there WAS overhead space.
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