AI at Scale: From Pilot Programs to Workflow Mastery

AI at Scale: From Pilot Programs to Workflow Mastery

By CEO Marc Cooper and CTO David Buza

Amid the dizzying claims about Artificial Intelligence (AI) and its transformative business potential, the sales pitch from Big Tech and consulting firms reads like a warning — embrace AI or risk missing out on a historic productivity boom. Over the course of the next decade, AI’s emerging capabilities and its impact on the financial world will only continue to accelerate. Handing over your IT budget to tech consultants and AI providers, however, is not the solution. Looking at AI adoption as a purely technical challenge sets businesses up to fail because successful integration of AI is much more of a people management challenge than it is an integration or technical issue.

For years investment banks have successfully leveraged workplace innovations, from computerized spreadsheets to algorithmic trading. Today, AI powers back-office efficiency, enhances due diligence, and empowers bankers with actionable insights. At Solomon Partners, we explore AI’s potential while continuing to emphasize the importance of human connections, relationship building, and reliance on the sage business judgment that characterizes our work. Similarly, as we transition from experimentation to scaling AI within workflows, our teams understand that our guiding strategic principles remain unchanged.

Even as AI’s capabilities streamline routine office work, its potential extends far beyond undertaking mundane tasks. Experts predict that generative AI will match or surpass human-level performance in some cognitive tasks such as logical reasoning and problem-solving decades earlier than anticipated.

The staggering pace of development and the seemingly limitless potential of this emerging technology creates both great excitement over its possibilities and anxieties over its implications. In this context, overcoming cultural resistance within an organization—typically driven by concerns about job security or discomfort with new tools—becomes critical. Leadership must find opportunities to demonstrate how AI enhances, rather than replaces, human expertise. Effective AI adoption hinges on comprehensive training programs that reach beyond mere technical instructions to demonstrate practical applications in everyday workflows. This approach ensures that employees understand, appreciate and leverage AI’s capabilities to their fullest.

While strategic advice, industry expertise, client relationship management, and complex decision-making still demand the intuition and expertise of seasoned professionals, AI provides a productivity booster that can further enhance those skills.

At its core, AI excels at automating repetitive tasks—pitchbooks, industry research, client meeting notes, and deal documentation—that have long consumed late nights and weekends of junior staff. By automating these processes, teams dedicate more energy to advancing deals, providing high-value outcomes, and—most importantly—delivering relationship-driven work.

This shift not only enhances productivity but also creates opportunities for more rewarding roles. For junior talent, AI creates opportunities to advance more rapidly and consistently to client-facing experiences.

Will some roles and recruiting strategies evolve? Almost certainly, but that has been true of every transformative technology that has reshaped the financial world over recent decades. Firms that deftly leverage AI tools to boost deal volume will, in turn, create demand for new, more rewarding roles for financial professionals.

One thing that will not change in the age of AI is the importance of trust and security. Client confidence hinges on the ability to safeguard sensitive information, which requires that AI systems exist securely within internal environments. Often the most impactful AI platforms in the investment banking context are those that are embedded in existing platforms that already house sensitive information—systems like email, spreadsheets, presentations, and video conferencing. To the extent that confidential or proprietary data is used as an input, any AI-generated content must be treated as confidential.

More broadly, transparency about how AI interacts with proprietary client information is critical. Robust privacy safeguards and clear communication with clients and regulators are essential for building confidence in these systems.

A key facet of Solomon’s approach is ensuring that AI feels intuitive and natural to its teams. To drive real value and ensure use, tools must be integrated directly into existing workflows, proactively surfacing insights when and where needed. Ideally, AI implementation avoids complex queries and steep learning curves, becoming instead an extension of bankers’ expertise. Rather than requiring bankers to manually query AI systems, we have focused on building solutions that proactively surface insights—such as financial data, sector analysis, and other recommendations—exactly when needed. This approach makes AI feel less like a standalone tool requiring effort to master and more like a natural extension of existing workflows.

Great challenges and opportunities lie ahead as firms explore the range of possibilities that AI affords. One challenge that AI cannot automate, however, is the cultural and organizational transformation required to unleash its full potential.

While there is no universal roadmap, placing the focus on people, not technology, allows the proper balance of technological capabilities with human judgment to best position firms to realize AI’s full promise.