Goldman Sachs is dramatically transforming its investment banking operations with artificial intelligence, according to CEO David Solomon’s revealing remarks at Cisco’s AI Summit on Wednesday. The Wall Street giant is leveraging AI to automate tasks that previously required significant human effort and time.
The most striking example involves IPO prospectuses (S-1 documents), which are critical regulatory filings detailing a company’s business, financials, and risk factors. Solomon explained that a decade ago, Goldman would assign a team of approximately six people to spend two weeks drafting these documents to impress potential clients going public. Today, AI can produce something “95% of the way there in a few minutes,” fundamentally changing how the bank competes for business.
Solomon outlined three strategic priorities for AI implementation at Goldman Sachs. First, the firm is focused on making its 11,000 engineers 30% more productive with coding tasks by freeing up capacity through AI assistance. Second, Goldman is working to better leverage its vast data repository, including 40 years of trading history, making this information more accessible to clients. Third, and perhaps most transformative, is deploying AI directly in investment-banking operations.
The bank is developing an “investment-banking copilot” using proprietary data to help bankers prepare for client meetings, draft materials, and deliver better investment insights. These AI copilots will draft text, analyze information, and suggest ideas to boost productivity. This technology is particularly significant given Goldman’s position as one of the top banks for taking companies public, alongside Morgan Stanley and JPMorgan.
Equity research analysts will also see major workflow changes. Solomon noted that analysts and their teams currently spend considerable time reporting on companies and feeding information into financial models to assess trajectories, growth, and risks. “Obviously that can all be automated now with this technology,” he stated. However, he emphasized this doesn’t mean wholesale job replacement. Instead, Goldman envisions smaller, more focused analyst teams supported by a “horizontal engine” that performs automated work across all industries, rather than maintaining separate pods for each sector.
The transformation won’t be easy. Solomon acknowledged that achieving these efficiencies requires “massive process change” and faces human resistance. “It’s hard, because people don’t want to change their process,” he said. “They like their team. They like the fact they have complete control.” The success of these AI deployments will depend heavily on effective change management. For Goldman Sachs, which generated over $53 billion in revenue in 2024, these AI-driven productivity gains could significantly boost an already booming business.
Key Quotes
Now you can basically have something that’s 95% of the way there in a few minutes
CEO David Solomon described how AI has transformed the creation of S-1 IPO prospectuses at Goldman Sachs, a process that previously took a team of six people two weeks to complete. This dramatic efficiency gain illustrates AI’s immediate impact on high-value investment banking work.
Obviously that can all be automated now with this technology
Solomon was discussing how equity research analysts currently report on companies and feed information into financial models. This statement acknowledges AI’s capability to automate complex analytical work that has traditionally required dedicated human teams.
You really need the analysts, and you need smaller teams, and you need a horizontal engine that basically does all that work for everyone as opposed to individual pods for every single industry
The Goldman CEO outlined his vision for AI-augmented equity research, emphasizing that jobs won’t simply disappear but will be restructured around smaller teams supported by centralized AI systems rather than industry-specific groups.
It’s hard, because people don’t want to change their process. They like their team. They like the fact they have complete control.
Solomon candidly addressed the human challenge of AI implementation, acknowledging that organizational resistance and attachment to existing workflows may be the biggest obstacle to realizing AI’s efficiency gains at Goldman Sachs.
Our Take
Solomon’s transparency about AI’s impact at Goldman Sachs is remarkable and strategic. By publicly stating that AI can replicate 95% of previously high-value work in minutes, he’s both signaling Goldman’s technological sophistication to clients and setting expectations for the industry’s future.
What’s particularly insightful is his focus on the remaining 5% as the competitive differentiator. This suggests that AI won’t eliminate investment banking jobs but will radically redefine them, shifting emphasis from document production to strategic insight, relationship management, and creative problem-solving—the uniquely human elements that AI cannot easily replicate.
The “investment-banking copilot” concept also reveals how Goldman is thinking about AI: not as a replacement but as an augmentation tool. However, the move toward “smaller teams” and “horizontal engines” clearly indicates workforce restructuring ahead. The financial services industry should view this as a preview of their own near-term future, where AI adoption becomes a competitive necessity rather than an optional innovation.
Why This Matters
This announcement from Goldman Sachs represents a watershed moment for AI adoption in the financial services industry. When one of Wall Street’s most prestigious institutions publicly commits to AI-driven transformation at this scale, it signals that artificial intelligence has moved from experimental to essential in high-stakes finance.
The implications extend far beyond Goldman’s operations. If AI can reduce complex IPO document preparation from two weeks to minutes, similar productivity gains are likely across the entire investment banking sector, potentially reshaping how thousands of financial professionals work. The shift from large teams to smaller, AI-augmented groups suggests a fundamental restructuring of white-collar work in finance.
Solomon’s candid acknowledgment of the “95% commodity” problem is particularly significant. It highlights how AI is commoditizing previously high-value work, forcing firms to compete on the remaining 5% of human insight and relationship-building. This dynamic will likely spread to other professional services industries including law, consulting, and accounting.
The resistance to change that Solomon mentioned also matters greatly. Even with powerful AI tools available, organizational inertia and human reluctance to abandon familiar processes may be the biggest barrier to AI transformation, not the technology itself. How Goldman manages this transition will provide a crucial case study for other enterprises navigating similar changes.
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