By mid-year, all of Morgan Stanley’s thousands of wealth advisors are expected to have access to a new AI-based chat tool.
The tool, which is already used by some 600 employees, provides advisors with answers to questions such as “Can you compare the investment cases for Apple, IBM and Microsoft?” and follow-ups like “What are the risks of each of them?” An advisor can ask what to do if a client has a potentially valuable painting — and the knowledge tool can provide a list of steps to follow, along with the name of an in-house expert who can help.
“What we’re trying to do is make every client or financial advisor as smart as the most knowledgeable expert on a given topic in real time,” said Jeff McMillan, chief of analytics, data and innovation for Morgan Stanley Wealth Management.
Experts disagree on whether AI will ultimately destroy more jobs than it will create over time. But it is clear that AI will change work for most knowledge workers, shift the skills they need and change the workforce needs of most companies. Now it’s up to business leaders to figure out how to take advantage of today’s technologies while preparing employees for the medium-term disruption brought by the tools.
Acting too slowly can mean missing out on gains in productivity, customer service and – ultimately – competitiveness, similar to what happened to companies that didn’t embrace the Internet fully or quickly enough. But at the same time, leaders must beware of the mistakes and biases that AI often perpetuates and think carefully about what this means for employees.
“Almost whatever industry you’re in, you should think of your company as an AI-first company,” said Alexandra Mousavizadeh, CEO of Evident, a start-up that analyzes the AI capabilities of financial companies.
The type of AI that underpins Morgan Stanley’s tool for advisors is called generative AI. It can create content – including text, images, audio and video – from information it has analyzed. In addition to answering questions, it can be used in countless other ways, such as composing memos and emails, creating presentation slides, and summarizing long documents. Early research suggests that tools built using generative AI can speed up many tasks and increase employee productivity.
For example, researchers at the Massachusetts Institute of Technology and Stanford found that customer support agents equipped with an AI tool that suggested answers resolved an average of 14 percent more customer issues per hour.
But the profits were not evenly distributed. Less experienced workers made greater productivity leaps as the tools effectively “captured and disseminated” the practices of their higher educated colleagues. Other recent MIT research similarly noted that workers who were initially not very good at tasks managed to close the gap with those who were more skilled, performed better and took less time when aided by AI
One possible takeaway from these findings is “that the advantage someone had with respect to his or her performance has now diminished because a young person with ChatGPT can perform just as well as someone with a few years of experience,” said Azeem Azhar, president of Exponential View, a research group. If the research takes place in the wider field, it could lead some companies to invest more in junior employees, while paying less for more expensive employees who have been with them for longer.
Some companies are already starting to make workforce decisions based on the expected impact of AI tools. IBM recently said it was slowing down or stopping the hiring of some back-office positions, such as human resources positions, which could be replaced by AI in the coming years.
AI’s speed and productivity gains will raise customer expectations, said Bivek Sharma, the chief technology officer for PwC Global Tax and Legal Services. “It’s about making sure we can upskill the workforce and get them on AI fast enough to meet the obvious demand that’s coming,” he said.
PwC has teamed up with Harvey, an AI start-up that creates tools for lawyers, to roll out an AI chat tool for the entire legal advisory practice in the coming months. It plans to extend such technology to its tax and human resources experts as well.
In addition to quickly providing employees with answers based on the company’s expertise, PwC’s goal is to generate new insights, including by ultimately also analyzing its clients’ data, Sharma said. The AI could potentially get all the contracts of two companies considering a merger, for example, and allow PwC experts to search for specific types of facilities and risks.
“Consider this really an expansion game rather than a time-saver for us,” Mr. Sharma said. “This is almost like a senior associate who is connected to all of our legal and tax advisors and contributes to what they can do for their clients on a daily basis.”
Larger companies generally need to invest in AI-savvy technical staff, who can adapt the technology to their business. “There are already companies that can’t adopt ChatGPT because they simply don’t have the kind of baseline to run it on, which is content management and the data in order,” Ms. Mousavizadeh said.
They also need to hire or train new specialists, for roles that don’t necessarily require technical expertise. Morgan Stanley’s Mr. McMillan and other business leaders say the AI platforms must be constantly “tuned”, with humans adjusting parameters and sources of information to get the best results for users. This alignment has created a need for a new group of workers known as “fast engineers” or “knowledge engineers.”
Morgan Stanley and PwC, among others, are building their own versions of AI chat tools that draw on internal material.
Concerns about security, confidentiality, accuracy, and intellectual property rights have led many companies to restrict their staff’s access to public ChatGPT and other generative AI tools. They want to avoid what allegedly happened at Samsung, where semiconductor division employees allegedly shared confidential computer code and meeting notes while using ChatGPT. Executives are also concerned about the frequent errors and built-in biases in some AI tools.
But part of the opportunity with tools that use generative AI, which let users type questions or commands in plain language, is to engage a broader group of non-technical employees in figuring out how it can change a company’s business. “Your people should be using these tools very, very regularly so they can start building their competencies and your own internal company competencies,” said Mr. Azhar.
He suggests public AI tools can be used in ways that don’t compromise confidentiality or security. For example, an employee can ask ChatGPT about the best ways to combine types of sales data to tell a compelling story without entering the data yourself. The opportunity, he says, comes from “frontline workers of any seniority deciding to improve their work through generative tools.”
Kevin J. Delaney is co-founder and editor-in-chief of Charter, a media and research agency focused on the future of work.
