What Your Business Can Learn from Goldman Sachs’ AI Assistant
TLDR: Goldman Sachs has given around 10,000 employees access to an internal AI assistant. It helps with everyday tasks like summarising content, drafting emails and translating code, and it works inside Goldman’s own systems using Goldman’s own data. The point for non-technical teams isn’t the technology. It’s the model: large organisations are now building AI that fits their workflows, standards and internal language instead of forcing staff to adapt to public tools. This is the simplest example of what enterprise AI adoption actually looks like.
What did Goldman Sachs build?
Goldman created an internal AI assistant that sits behind its firewall.
Employees use it the same way they use any chat tool: type a question, get a response, ask it to summarise something, ask it to explain something or ask it to draft something.
The assistant handles simple, repeatable tasks found in every organisation.
- It summarises emails.
- It drafts quick notes. It explains code.
- It helps with research.
- It rewrites content into shorter formats.
- It checks small details before something is sent.
The important part is that the assistant doesn’t rely on public data. It draws from Goldman’s own materials, terminology and internal knowledge. It reflects how work is done at the firm because it has been shaped by that environment.
For non-technical readers, the key idea is simple: this is an internal tool that behaves like a knowledgeable colleague because it uses the organisation’s own information, not the general internet.
Why build an internal AI Agent?
Goldman’s reasoning is straightforward. Public AI models don’t understand the organisation. They don’t know its processes, tone, quality expectations or the way staff communicate. They also don’t meet the security and compliance requirements of a regulated industry.
By keeping the assistant in-house, Goldman controls:
- the data used
- the rules the assistant must follow
- the behaviour it should learn
- the information it’s allowed to access
- the outputs it produces
When an assistant sits inside the company’s systems, it adopts the organisation’s expectations. It becomes predictable. It becomes familiar. It becomes safe to use in day-to-day work.
Smaller organisations don’t need the same infrastructure, but the logic applies everywhere: AI works best when it works inside your environment, not outside it.
What can smaller organisations learn from this?
You don’t need Goldman’s scale to take the right lesson. The assistant started with the tasks that slow staff down the most.
- Summaries.
- Drafts.
- Explanations.
- Conversions.
- Small checks.
These tasks are universal. They exist in every organisation regardless of size or sector.
The second lesson is context. Goldman’s assistant works because it is fed structured internal documents, clear examples and consistent processes. It mirrors the way employees communicate because it is trained on materials that already reflect those standards.
Most organisations underestimate how much clarity they gain simply by cleaning and organising their internal information before introducing AI. When the inputs are coherent, the assistant produces coherent outputs. When they are not, the tool becomes unpredictable.
The third lesson is adoption. Goldman didn’t start with complex agents that automate entire workflows. They started with simple assistants that remove the overhead around the work. That pattern is reachable for any organisation.
What changes when AI becomes an internal assistant?
The work doesn’t disappear, but the overhead does.
People who used to spend time rephrasing content or producing shorter versions of something rely on the assistant to handle the first draft. Instead of rewriting, they review. Instead of formatting, they check. Instead of producing variations by hand, they start with a version that already fits the shape they need.
It’s a change from manual reconstruction to supervised refinement.
This is what mature enterprise AI adoption looks like. Not flashy. Not dramatic. Just practical really, but not earth shattering, just like rolling out a new interface in the old days.
It’s also a realistic preview of how AI will show up in other organisations: quietly, inside existing systems, changing the friction around everyday tasks long before it changes the tasks themselves.
FAQs
Q: Do we need technical staff to build something like this?
A: No. The early value Goldman showed comes from simple tasks applied to clean, structured information. Most organisations can start with off-the-shelf tools configured to their environment.
Q: How do we stop an internal assistant from giving confident wrong answers?
A: By controlling the inputs. When the assistant is fed consistent internal documents and approved sources, it stays within boundaries. Uncontrolled data is where mistakes begin.
Q: Can this work without a huge internal dataset?
A: Yes. What matters is not the volume of data but the clarity. A small set of high-quality internal materials produces safer and more accurate outputs than large amounts of noisy information.
Q: How do we manage employees relying on the assistant too heavily?
A: Set a simple rule: the assistant handles the first draft, humans handle the final version. This keeps quality intact without slowing the work down.
Q: Does an internal assistant replace roles?
A: It replaces repetitive formatting and summarising, not expertise. People still make decisions, give context, and check what the assistant produces.
Q: What’s the first step for a smaller business wanting to try something like this?
A: Standardise your documents before you introduce any tools. When the information is consistent, the assistant behaves consistently.
AI compresses production work. Goldman’s assistant shows that the future of enterprise AI is not dramatic change but practical compression. When the overhead reduces, people focus on decisions instead of rework.
If your organisation wants a clear, practical path to introducing AI safely and effectively, the AI Strategy Blueprint gives you the structure and steps to start well.