フランクリン・テンプルトンにおけるAIによる投資の変革

What would you do with artificial intelligence if you were confident that it would transform your industry? What actions would you take if you felt that you were at an inflection point in that transformation? Would you try to be an early proponent of AI-first in your industry, or a fast follower?

Those are some of the questions faced by the leaders of Franklin Templeton — officially Franklin Resources Inc. — a large investment and asset management firm with about $1.7 trillion in assets under management that was founded in 1947.

Over its 79-year history, Franklin Templeton has grown through strategic acquisitions that have enhanced its capabilities and global reach and expanded its competencies across asset classes, geographies, and investment philosophies.

Today, however, AI is an important driver of future growth and profitability. Consultants, academicsそして industry associations agree that the technology is already powering research, compliance, and client relationships in the investment field and that it will transform them further in the future.

Jenny Johnson, Franklin Templeton’s CEO and a third-generation leader of the firm, has long combined investment leadership with deep technology fluency, having managed technology organizations earlier in her career. Before AI became a board-level mandate, she had already been focusing on AI for years. She personally experiments with generative AI, building AI agents and using techniques like vibe coding (using generative AI prompts to write code) to create computer programs.

But even Johnson has been amazed by the rapid advancement of AI in the industry. “This is faster than even I thought it was coming,” she said in a November 2025 video interview. “Every big financial institution spends a lot of money on reconciliation between systems and reconciling data. AI can help with that.” She noted that AI could also review company research reports and sell-side reports, analyzing, for instance, how tariffs would affect U.S. pharmaceutical companies versus those in Europe. “I don’t think everyone will have the same models,” she said. “Training the model is all going to be about your own data.” The future, she said, will be having the company’s entire talent force using AI as a tool.

AI Capabilities Today at Franklin Templeton

Franklin Templeton is moving rapidly toward that future, with a huge variety of internal AI capabilities and transformative platforms at both production and pilot status. The company has product teams that work with business units such as distribution, operations, and investments. Each product team operates under an AI-first model that combines product management, engineering, and data science into one unit. There is a common AI platform team and a research team. There is also an adoption and solutions team that drives employee implementation of AI and helps align business benefits with the products.

Deep Ratna Srivastav, the company’s chief AI officer, is responsible for AI product management, engineering, research, and adoption. He was involved in the conceptualization and launch of Franklin Templeton’s Goals Optimization Engine, one of the company’s early portfolio selection and optimization offerings. He told us that the engine integrates with global fintech ecosystems — including those with recordkeepers, managed account providers, custodians, and digital wealth platforms — to deliver personalized investment strategies aligned to investors’ financial objectives. It currently generates recommendations for over 40,000 investors, primarily focusing on retirement goals. The application has been embraced by several of the company’s strategic partners and is part of the firm’s forward-looking AI road map. The next phase, Srivastav said, will apply reinforcement learning to advance portfolio optimization.

Franklin Templeton offers its sales and distribution team its Intelligence Hub, which brings together AI and digital capabilities designed to enhance insights, facilitate territory management, and strengthen client engagement in meetings with financial advisers. The hub centralizes previously fragmented data sources, research, and over 15 workflow tools into a single interface, reducing manual search time and accelerating access to important content for sales meetings. AI-powered workflows automate list generation, meeting preparation, and dynamic prioritization. A Franklin Templeton salesperson can get a recommendation on which independent financial advisers to highlight, what to focus on in a client conversation, how best to get visibility with the adviser, and the most appropriate clients to meet with based on geographical proximity.

Following a yearlong pilot, Intelligence Hub was made broadly available to the company’s sales professionals in early 2026. Srivastav said it has delivered measurable efficiency improvements, including reduced daily preparation time before client meetings. It has also led to a significant increase in value-added client interactions.

The company has also applied AI to end-to-end processes in the middle and back offices of the organization. There are AI-enabled platforms in production for automated reconciliation of trades and for creating scalable communications with custodians, counterparties, and core trade operations.

Investment analysis is also increasingly supported by AI. The goal is not to automate investment advice but to support it with better information, faster iteration, and insights that humans alone couldn’t arrive at. “Copilot, not autopilot” is the overall objective.

To that end, a system called MosaiQ combines portfolio construction, manager research, and analysis into a single platform. An AI assistant named Pixel guides users through MosaiQ using natural language to explain complex investing concepts and, increasingly, to perform end-to-end tasks on users’ behalf. There is a new portfolio manager “copilot” assistant in place that can provide early warnings of market shocks, identify behavioral biases in training, and provide insights on portfolio creation. Franklin Templeton has also built an agentic investment analyst called Gromit that can independently analyze nuanced topics (for example, the impact of higher oil prices on U.S. labor trends), fact-check humans, and offer contrarian viewpoints by analyzing a breadth of proprietary and third-party data sources. Those systems are primarily powered by generative AI.

Looking Forward

To position the company for evolving client demands, Franklin Templeton’s $103 billion multi-asset group, Franklin Templeton Investment Solutions, tasked Max Gokhman, formerly its deputy chief investment officer, to lead the new AI & Digital Asset Solutions team. It will focus on three areas: further developing AI-driven investment capabilities, launching strategies incorporating digital assets and tokenized products, and advising clients on the effective use of these technologies in their own portfolios and organizations. Gokhman’s experience as an AI company founder, digital asset investor, institutional asset allocator, multi-asset portfolio manager, and chief investment officer made him uniquely suited to lead this effort.

“I’ve seen our industry change multiple times over my career, but never at a pace this rapid,” Gokhman said. “Tenacious focus and a willingness to pivot are requisite for any asset manager that wants to be relevant five years from now.”

Chief AI officer Srivastav and his colleagues are working across many other end-to-end processes. One involves voice intelligence for the U.S. retail business to transform customer engagement. “Portfolio commentary” AI, which will deliver timely insights to strengthen the client experience, is in the planning stage. Utilizing the multi-agent orchestration portfolio management copilot for the investment team is another step in the end-to-end redesign of the investment process. Marketing is streamlining its content creation process, enabling it to produce more personalized, timely, and high-quality content. Other corporate functions — including legal, compliance, HR, and finance — will be similarly reengineered with AI.

Neither Srivastav nor CEO Johnson is terribly concerned about whether Franklin Templeton’s employees will go along with the AI transformation. While the opportunities for AI education have been only somewhat popular, there has nonetheless been rapid adoption of virtually every AI tool made available to employees, Srivastav said. In many cases, these tools have been visible to clients and partners, which is helpful in persuading employees to use them. Srivastav noted that noncompliance with the company’s extensive AI governance policy and procedures has not been a concern thus far.

The leadership team of Franklin Templeton isn’t sure whether its AI capabilities will result in a “big bank” transformation or whether they’ll power a slower evolution toward increased efficiency and effectiveness. They do know, however, that they want to be ready in advance of customer and market demand and that they need to be among the industry’s leaders.