Artificial intelligence (AI) is enjoying a long moment in the spotlight. But debate continues over whether it’s a “shortcut to utopia” or possible harbinger of the end of the world.

Meanwhile, one group has quickly leapt on both the technology and all the hype—consulting firms. And they’ve been spending big.

Advocates of the technology are heralding a new era of professional efficiency for consultants. Once-tedious emails, presentations and reports can now be completed in a flash.

Many consulting firms have also already leapt at the opportunity to professionally advise other businesses on making the most of new generative AI tools.

So why does the consulting industry see such potential for transformation—and is hurling itself headfirst at this technology a good idea?

Wait, what do consultants do?

The consulting industry is notoriously shrouded in mystique, despite regularly winning huge contracts from governments and major businesses.

But in simple terms, consultants aim to offer their clients expert advice and solutions to help improve their performance, solve problems and achieve certain goals.

They often possess specialized knowledge, skills or experience relevant to a particular client, so the nature of their work can vary significantly.

Clients often seek consulting services because they want help with problem-solving and decision-making on a particular project, or want external validation for their decisions and need an independent report.

A wide range of professional services firms offer consulting services, including the “big four”: Deloitte, PricewaterhouseCoopers (PwC), Ernst & Young (EY) and KPMG.

There are also many specialist consulting firms, including McKinsey, Bain & Company, Boston Consulting Group, Kearney and L.E.K. Consulting.

Much of the demand for consultancy services is driven by the increasing complexity of doing business, due to globalization, digitalization, changing regulations and many other factors.

However, growth in Australia’s consulting sector has slowed this year amid the fallout from the PwC tax leaks scandal and sluggish economic growth.

How can AI help?

Artificial intelligence (AI) technology has been around in a range of forms for a while now. But until recently, it was mainly used internally by organizations and required specific training. This changed with the public launch of “generative AI” models, such as OpenAI’s ChatGPT.

These models differ from traditional AI in their capability to generate something new, such as text that is virtually indistinguishable from one written by human, or other types of output such as images, videos or sounds.

Large language models like ChatGPT were some of the first to give the general public a sense of what AI could be used for.

But this has had some specific implications for consulting businesses. The models can analyze large amounts of data quickly and cheaply to generate tailored feedback.

With generative AI offering such efficient services for business analysis and strategic planning, many would-be clients might now be questioning whether buying consulting services will remain worthwhile in the long run—particularly as these technologies improve.

Getting ahead of the curve

It should therefore come as no surprise that the consulting industry is investing heavily in generative AI. Just take the big four, for example.

Deloitte and EY have already deployed conversational AI assistants aimed at boosting staff productivity.

KPMG’s customized version of ChatGPT—KymChat—was launched in March to speed up the preparation of sales proposals for consulting work, by quickly identifying relevant experts.

In May, PwC became OpenAI’s biggest enterprise customer after purchasing more than 100,000 licenses for the AI giant’s latest models.

Other players operating in the knowledge work space have also been hyping up the productivity-boosting potential of generative AI for similar tasks.

US finance behemoth JPMorgan Chase recently rolled out its own large language model called LLM Suite, which it says can “do the work of a research analyst”.

What does this mean for the business model?

To harness this technology’s potential, firms will need to use it effectively. This means ensuring they maintain a human-centered value proposition for clients that goes above what the technology alone can offer.

Generative AI tools will not replace the human trust that is often crucial for successful consulting, nor provide the depth of specialized knowledge (and access to relevant human experts) that a seasoned consultant currently can.

But the technology will streamline a lot of everyday tasks. It can also provide a sounding board for decisions and business strategies, and suggest solutions.

At least in the near term, firms are likely to use AI to “augment” human consultants, rather than replace them.

What are the risks?

The uptake of generative AI also presents risks to the consultancy business.

One big one concerns creativity. Since the models produce their outputs based on past data, the range of potential solutions they can identify will always be limited to their training data.

Excessive reliance on the same models could start eroding consultant companies’ ability to innovate, by diluting their distinct competitive advantages and make them increasingly resemble each other.

Such a phenomenon has already been observed in research looking into generative AI’s effect on student creativity.

My own research has shown that excessive reliance on automation technologies like generative AI can lead to the erosion of professional expertise. In the long run, this effect could seriously organizations’ knowledge and business reputation.

If younger consultants still in training offload too much of their thinking and analytical work to generative AI, they may fail to develop their own analytical abilities.

And of course, the technology itself isn’t perfect. Generative AI is known to make mistakes and even “hallucinate”—that is, completely make things up.

All of this highlights the importance of using generative AI thoughtfully, and perhaps above all, not losing sight of the unique value humans can bring.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Economic prospects have improved in recent years for Black Americans born poor, according to new research from Opportunity Insights. At the same time, earnings have fallen for white Americans from low-income families.

The analysis, drawn from 40 years of tax and Census records, finds a dramatic narrowing of the economic divide between the poorest Black and white Americans. But it also reveals a widening gap between low- and high-income white people, driven by shifts in the geography of employment.

“This is the first big data study to look at recent changes in economic opportunity within the same place over time,” said study co-author Benny Goldman, M.A., Ph.D., a research affiliate with Opportunity Insights. “And what we see are shrinking race gaps and growing class gaps.”

The research follows what Goldman called “a long history of folks studying intergenerational mobility.” That includes Opportunity Insights co-founder and director Raj Chetty, the William A. Ackman Professor of Public Economics and one of the study’s five co-authors. For more than a decade, Chetty has built an influential body of work demonstrating how access to the American Dream varies by region, race, and history.

Social scientists have found the patterns he uncovered to be persistent. For example, a Swedish demographer compared findings from a 2014 study co-authored by Chetty on upward mobility across generations in the U.S. to the prevalence of slavery from the 1860 census. Counties with high rates of bondage at the outbreak of the Civil War showed less mobility for residents born more than 100 years later.

With the new study, Chetty, and others, set out to investigate whether these dynamics are changing. Anonymized records provided by the federal government were used to compare earnings at age 27 with socioeconomic factors from childhood. The sample included 57 million Americans born in 1978 or 1992.

Across the country, the sample’s Black millennials fared better than its Black Gen Xers. Individuals born in 1978 to low-income families (with earnings in the 25th percentile or lower) averaged $19,420 per year in early adulthood compared to an inflation-adjusted $21,030 for poorer members of the 1992 cohort.

Outcomes also improved slightly for children born to high-income Black families, though researchers noted “noisier,” or less reliable, estimates for this population due to a small sample size.

Outcomes showed wide variation by region, with Black Americans making the biggest strides in the Southeast and Midwest—areas traditionally associated with high rates of Black poverty.

“Take where I grew up in Kalamazoo, Michigan,” offered co-author Will Dobbie, a professor of public policy at Harvard Kennedy School and faculty research fellow at the National Bureau of Economic Research. “Poor Black kids born there in 1992 were earning $4,700 more at age 27 than poor Black kids born there in 1978, an incredible improvement in just a few years.”

Meanwhile, white Gen Xers from poorer families averaged $27,680 per year versus $26,150 for millennial peers. The gap between the poorest and richest white people ballooned by 28% over the same period, as those born at the top watched their fortunes climb.

Results were particularly stark in a few regions of the country known for prosperity.

“Outcomes for low-income white children born in the ’90s from parts of Massachusetts, Connecticut, rural New York, and California started to look like Appalachia, the Southeast, and the industrial Midwest did for low-income white children born in the late ’70s,” noted Goldman, now a newly installed assistant professor of economics and public policy at Cornell University.

“This work reinforces the importance of childhood communities for outcomes in adulthood, consistent with our prior findings,” Chetty wrote in an email.

“But it shows that it is possible for these communities to change rapidly—within a decade—in a way that has significant causal effects on children’s long-term outcomes.”

To be sure, vast racial disparities persisted. For Gen Xers who grew up poor, the racial earnings gap between Black and white Americans was $12,994. For millennials, it fell 27 percent to $9,521. In a research summary, modest changes in economic mobility were noted for Hispanic, Asian, and Native American children.

As an additional aspect of their analysis, the researchers checked their findings against historic rates of parental employment at the neighborhood level. This approach was inspired by the work of Harvard sociologist William Julius Wilson, author of “When Work Disappears: The World of the New Urban Poor” (1996). “It was used as a broad way to measure the health of any given community where kids grew up,” Goldman explained.

The researchers saw that neighborhood employment tracked neatly with emerging race and class differences. “We found a sharp decline in employment rates among lower-income white parents relative to low-income Black families and higher-income white families,” Goldman said.

Declining earnings were hardly the only negative associated with growing up amid low parental employment. In a testament to the power of social connections, places with fewer working parents also saw rising mortality and falling rates of marriage.

Yet this wasn’t a case of opportunity moving from one group to another, since neighborhoods with higher rates of adult employment saw better outcomes for people of all races. “In areas where Black kids did best, low-income white kids and their parents also did better,” Goldman said.

What’s more, the researchers found that moving to areas with strong parental employment was associated with higher earnings in early adulthood. According to Goldman, this was especially true for those who landed in the new neighborhood before the age of 10.

“Growing class gaps and shrinking race gaps did not result from unequal access to a booming economy,” he said. “Instead, what matters is how many years of childhood were spent in a thriving environment.”

This story is published courtesy of the Harvard Gazette, Harvard University’s official newspaper. For additional university news, visit Harvard.edu.