She says building a company culture with opportunities for two-way learning and conversation is key.
This article is part of "Workforce Innovation," a series exploring the forces shaping enterprise transformation.
Alicia Pittman, the global people-team chair at BCG, has been at the consulting firm for nearly 20 years. It's a testament, she said, to the company's culture.
"It's a place built to make talent do things that they didn't even know they could do," Pittman said. "I'm included in that. I love the learning that comes with it."
Pittman said one aspect of leadership development she's focused on is ethical practices. "We teach and train our people to understand how small choices that don't seem like major ethical choices matter," she said. "The responsibility is to show up with high ethics in everything that you do and think about the bigger picture of how you do things."
She said the firm had implemented programming through partnerships to help the company's leaders navigate the need to drive innovation ethically: "It's a place that we continue to invest because it's quite important for us."
The following is edited for length and clarity.
Where is BCG on the adoption curve of artificial intelligence, and what do you want to see in 2025?
I am excited about how BCG is driving change and grabbing the reins on generative AI. Gen AI is important to our clients, industry, and people.
We have a suite of tools, some of which we developed internally and some that are available off the shelf, that we've made available to all of our staff. Nearly everyone is a user to some degree.
What we're focused on now is moving from casual use to what we refer to as habitual use. It's habitual use that gets the value so that you can change how work gets done, based on the frequency, sophistication, and depth to which they use the tools.
We have a lot of enablement resources for our people, both as individuals and as teams, to make sure that we're moving up that habitual usage curve as quickly as we can. A firm like BCG is under pressure to stay on top of things because its clients look to us.
So how do you strike that balance and not go so fast that you risk leaving some of your people behind? We have an enablement network of more than a thousand people who are there to help both individuals and teams adopt gen AI. It's in all of our core curriculums.
Just this fall, we held AI days across every one of our offices at BCG with hands-on training. So we have people who are naturally there and ready for it, but we're also investing heavily to bring people up the curve.
You've mentioned in Workforce Innovation-board roundtables that apprenticeship is now a two-way street. What advice would you give leaders looking to deploy apprenticeships differently?
At BCG, we're fortunate to have a pretty flat structure so that you always have a good proximity between your senior leaders and all your staff. There are two ways we focus on helping to support this idea of two-way mentorship.
One is we just talk about topics. I recently wrote a piece about a mental-health town hall we held. It was quite moving. We had BCG employees who were generous and vulnerable in talking to thousands of people on a virtual town-hall panel about their struggles with things like addiction, grief, and depression, both before their time at BCG and during their time at BCG, and how they work through it.
It's about having those difficult conversations, getting the points out there, and starting to have shared language or shared opportunities to talk about these topics.
The AI days that I mentioned already are another way we do this. A lot of it is about getting cross-cohort connections on technology and other topics, creating forums so that people can talk about it.
The other is ensuring continual, structured feedback. Our staff provides 360-degree feedback all the time. It's an important part of what we do, and we're piloting doing it even more frequently. For example, we're giving people 360 feedback on how to be an inclusive leader. So it's both the formal mechanisms and also just creating the formats and discussions.
So much of culture and moving culture forward is really about having the language so we can share and talk about things. Creating those forums helps. It's an invitation to engage in productive ways.
What innovations are happening around DEI, especially as the topic has become more politicized?
DEI is built into our business model. We need great talent. We grow way faster than our talent pools, so just to get people in at quality, we need to be able to reach a lot of people; we need them to thrive.
Our business requires innovation, which requires diverse thought and experience. So, for us, it's quite core. One of my areas of focus is on inclusion and inclusive leadership. In some ways, it's the simplest thing to focus on. We all know that when people feel comfortable being themselves at work, you get the best out of them. They're most motivated, ready to take risks, ready to collaborate, and all of those things.
In North America, where we have the best statistics, 75% of our workforce is part of one or more of our DEI groups. Whatever intersectionality people have, whatever group they belong to, it's about how you make everybody able to show up at their best. That's really where our focus is.
This article is part of "Workforce Innovation," a series exploring the forces shaping enterprise transformation.
Diversity, equity, and inclusion programs have become the subject of a heated, politicized debate over the past few years.
Several major corporations, including John Deere, Microsoft, and Molson Coors, have made headlines recently for rolling back their DEI initiatives.
Meanwhile, Walmart, the world's largest retailer, announced it would no longer use the acronym in its communications and would not extend its Center for Racial Equity, a nonprofit established in 2020 with a five-year, $100 million commitment to address racial disparities.
Even so, as we've reported in this series, many companies remain committed to the values of DEI β but are shifting their strategies for a new era. Whatever the motivation of the companies, it's clear that DEI is undergoing a period of change.
Business Insider asked its Workforce Innovation board to participate in a roundtable to discuss how DEI programs are evolving. We wanted to find out what structural changes are happening, how companies can continue to build trust with employees, and what role artificial intelligence is poised to play.
The consensus around the virtual table was that the focus of the DEI story is shifting to business outcomes and the skills needed to achieve them. "We can't do it the old way," Purvi Tailor, the vice president of human resources at Ferring Pharmaceuticals, said. "We have to have the conversation in a new way. It becomes much more about inclusion and changing mindsets and creating awareness about your own biases."
Skills-based hiring is one way companies are working to identify diverse candidates organically. "Let's focus on the skills that are required for the future of work and what we are looking for from leaders in our company," Maggie Hulce, the chief revenue officer at Indeed, said. "And then be more consistent in the application of holding that bar."
By homing in on the skills organizations need to succeed and how to use AI tools to help surface in-house talent, companies could move the DEI story away from conflicts and focus on its benefits.
"It dismisses this notion that you have to lower the bar if you want diversity in your organization," said Spring Lacy, the global head of talent acquisition and DEI at Verizon. "We've got lots of super smart, super skilled people of color, women, people with disabilities, LGBTQI community, who just aren't seen for all of the biases that you talked about. You don't have to lower the bar."
Roundtable participants included:
Anant Adya, executive vice president, service offering head, and head of Americas Delivery, Infosys
Lucrecia Borgonovo, chief talent and organizational effectiveness officer, Mastercard
Chris Deri, president, Weber Shandwick Collective
Maggie Hulce, chief revenue officer, Indeed
Spring Lacy, vice president, chief talent acquisition and diversity officer, Verizon
Purvi Tailor, vice president of human resources, Ferring Pharmaceuticals USA
Here are six key takeaways from the discussion.
Skills-based hiring, supercharged with AI tools, helps companies find 'hidden figures'
Skills-based hiring is a strategy that some companies are using to identify candidates and reduce bias in the hiring process. The approach focuses on the skills needed to fulfill the role, minimizing qualifications like college degrees or previous job titles.
With artificial intelligence, talent leaders can accelerate the hiring process and uncover strong candidates within their companies that they might have missed before.
Lacy, who was previously an HR leader at Prudential, said AI is empowering existing employees to showcase their abilities more effectively.
"When went to recruit internally, and we pulled people based on the skills profile and not based on proximity bias or any other bias, our slates were inherently more diverse," Lacy said.
The critical piece for companies is to figure out the best way to capture an accurate and comprehensive view of employees' skills.
Verizon uses the Workday HR platform and is piloting a program with its partner company, Censia, that uses an AI tool to help employees craft their profiles.
Lacy has seen how difficult it can be for employees to isolate their skills in ways that might help them be identified for new opportunities. "When we said to employees, 'Go build a skills profile,' the page was blank," she said. "It was really hard for people to get started." AI tools can pull information from a range of sources and serve up a framework that guides employees through the process.
Mastercard has launched an employee-skills initiative with the software company Gloat. "It has been a really great way to democratize access to opportunities for employees," said Lucrecia Borgonova, Mastercard's chief talent and organizational effectiveness officer.
The outcome for companies can be a more diverse talent pool from inside the house.
Lacy said Verizon is conscious of the potential for bias in the AI programs, but early indicators suggest that more individuals are being considered for roles than in the past.
"We are uncovering hidden figures in this organization because there are people who we don't know, because they are not well networked, they don't have sponsors," Lacy said. "If not for this technology, we wouldn't have known that they were there, to be able to lift them and perhaps provide them with other opportunities."
Leaning into the 'I' of DEI β inclusion
DEI programs have many aspects, including employer branding and attracting a diverse talent pool, screening and hiring, and compensation.
Inclusion relates to a person's workplace experience and their sense of belonging at an organization, which research suggests makes people want to join and stay at a company. Benefits are an essential part of that employee experience, and companies may want to think about how these packages reflect their values to staffers and prospects alike.
Ferring Pharmaceuticals introduced a program in 2022 that includes unlimited financial support for creating a family β through IVF, adoption, surrogacy, or birth β for all employees, regardless of gender or sexual orientation.
Ferring's Tailor said it is one way that the company emphasizes its approach to its entire workforce.
"We talk about more of the 'I' than we do about the 'D' and the 'E,'" Tailor said. "We do it to show the kind of culture and working environment that we want to have. It's all about inclusion and bringing your whole self to the workplace."
Linking AI tools with culture and leadership
As companies develop new hiring strategies, culture does not stand still.
"Inclusion and belonging are essential parts of the culture, the value proposition, and key to driving the outcomes of our business," said Mastercard's Borgonovo."It's really important that we drive shared accountability across our 34,000 employees around the role that each of us has to collectively play in creating this culture of inclusion where everybody feels that they can belong."
Borgonovo said that Mastercard is exploring ways to leverage AI to help business leaders across the organization improve efficiency and be more intentional about DEI and other workforce goals.
"How do we enable people, leaders, from an automation or efficiency standpoint? How do we help them be more proactive?" she said. "How do we help them create more bandwidth by automating certain processes so then they have more time to coach and develop their teams."
She said the company is exploring how AI can be used to coach leaders to role-play and get feedback on how they engage with their teams. "AI can be your coach, your copilot, and help augment your leadership," she said.
Ditching the DEI silo
Indeed's Hulce said a lot of time goes into optimizing the company's structure. "How do you make it the norm that equity needs to be built into processes, period," she said.
It's not just about interviewing and hiring diverse candidates, but about leading teams through every opportunity and decision, including promotions, performance bonuses, and assignments.
"How do you measure that? How do have regular conversations with managers at different levels in the organization about the expectation that we will be looking at equity in all of these steps," Hulce said.
Indeed once had a DEI team that worked parallel to the HR function. But when the previous HR leader left the company, they decided to reorganize and embed the DEI discipline across the business, elevating the previous head of DEI to chief people officer.
Hulce said realigning DEI was essential to scaling goals, standards, and accountability across the company. "It's almost an impossible task to ask a separate group to influence everybody else unless it's built into core processes somehow," she said.
Infosys is also considering its optimal DEI structure."We are slightly decentralized," Anant Adya, an executive vice president, said. The global company has a centralized corporate DEI team, with DEI councils at the individual industry units. Adya said the company will leverage AI tools to help measure effectiveness.
Hulce emphasized the need to regularly and consistently review management decisions. "It can't be just once a year," she said. "You evaluate, you check, and if there's a correction to be made, you say, 'OK, guys, something looks amiss.' The expectation is we will be following equitable processes."
Using AI to scrutinize hiring, while retaining the human touch
Adya said Infosys is using AI to analyze patterns in its hiring data.
"It is very important to look at and analyze the data based on how hiring patterns are being used and if there is any bias in the hiring process itself," he said.
AI will grow increasingly important in analyzing the efficacy of various recruitment sources. "A lot of times we see that employee referrals actually work the best," he said. "But that might not be true when it comes to specific DEI initiatives."
By enlisting AI tools to analyze online sources, university partnerships, and other talent alliances and platforms the company is using, Adya said it should be able to optimize its approach around specific goals.
But all the AI analysis in the world does not negate the need for the human touch. Adya said that sometimes there's a perception at the company that hiring is being done only to hit certain DEI benchmarks and that the process is too onerous.
Adya said that hosting a "clear dialogue" about the company's decision-making process around recruitment methodology has helped employees understand the company's rationale.
"It's always better to sit down and explain why this is critical for the unit and why it is important," he said. "Sometimes open dialogues, going back to the old school, not using AI or gen AI, but just sitting and talking and removing that uncertainty and lack of transparency helps a lot."
Leveraging AI-powered insights to change the DEI story
Proponents of DEI maintain that a diverse, inclusive workplace yields better business results, and there are studies that also support that view.
Opponents of DEI, said Chris Deri, the president of Weber Shandwick's corporate advisory business, tend to focus on the methodology of achieving workplace diversity, such as companies actively seeking women for leadership positions, seemingly at the expense of male candidates.
"That's what DEI opponents are focused on," Deri said. "Like, how do you pull together a candidate pool, like having women candidates somehow be seen to be at the front of the line."
Deri said that companies should work to shift the perspective to DEI outcomes and tangible business benefits β and should leverage artificial intelligence to surface insights that might not be obvious.
"AI can do that in a way that human knowledge management and analysis is not going to be able to do," Deri said. "We can use the power of AI to look across our enterprises' data and knowledge and start to collect the outputs and outcomes of the principles of applying DEI. "
Deri said that if a large language model can be trained on the outcomes, such as attracting new customers, creating new products, and building community trust, "that might be something that uses technology to help the storytelling about DEI. We really need to change the entire story now."
Top Row: Greg Jennings, Aditi Mithal, Pooya Amini, and Shruti Kapoor. Bottom Row: Neeraj Verma, Kesha Williams, and Igor Ostrovsky.
Alyssa Powell/BI
This article is part of "CXO AI Playbook" β straight talk from business leaders on how they're testing and using AI.
The future of software-development jobs is changing rapidly as more companies adopt AI tools that can accelerate the coding process and close experience gaps between junior- and senior-level developers.
Increased AI adoption could be part of the tech industry's "white-collar recession," which has seen slumps in hiring and recruitment over the past year. Yet integrating AI into workflows can offer developers the tools to focus on creative problem-solving and building new features.
On November 14, Business Insider convened a roundtable of software developers as part of our "CXO AI Playbook" series to learn how artificial intelligence was changing their jobs and careers. The conversation was moderated by Julia Hood and Jean Paik from BI's Special Projects team.
These developers discussed the shifts in their day-to-day tasks, which skills people would need to stay competitive in the industry, and how they navigate the expectations of stakeholders who want to stay on the cutting edge of this new technology.
Panelists said AI has boosted their productivity by helping them write and debug code, which has freed up their time for higher-order problems, such as designing software and devising integration strategies.
However, they emphasized that some of the basics of software engineering β learning programming languages, scaling models, and handling large-scale data β would remain important.
The roundtable participants also said developers could provide critical insight into challenges around AI ethics and governance.
The roundtable participants were:
Pooya Amini, software engineer, Meta.
Greg Jennings, head of engineering for AI, Anaconda.
Shruti Kapoor, lead member of technical staff, Slack.
Kesha Williams, head of enterprise architecture and engineering, Slalom.
The following discussion was edited for length and clarity.
Julia Hood: What has changed in your role since the popularization of gen AI?
Neeraj Verma: I think the expectations that are out there in the market for developers on the use of AI are actually almost a bigger impact than the AI itself. You hear about how generative AI is sort of solving this blank-paper syndrome. Humans have this concept that if you give them a blank paper and tell them to go write something, they'll be confused forever. And generative AI is helping overcome that.
The expectation from executives now is that developers are going to be significantly faster but that some of the creative work the developers are doing is going to be taken away β which we're not necessarily seeing. We're seeing it as more of a boilerplate creation mechanism for efficiency gains.
Aditi Mithal: I joined Amazon two years ago, and I've seen how my productivity has changed. I don't have to focus on doing repetitive tasks. I can just ask Amazon Q chat to do that for me, and I can focus on more-complex problems that can actually impact our stakeholders and our clients. I can focus on higher-order problems instead of more-repetitive tasks for which the code is already out there internally.
Shruti Kapoor: One of the big things I've noticed with writing code is how open companies have become to AI tools like Cursor and Copilot and how integrated they've become into the software-development cycle. It's no longer considered a no-no to use AI tools like ChatGPT. I think two years ago when ChatGPT came out, it was a big concern that you should not be putting your code out there. But now companies have kind of embraced that within the software-development cycle.
Pooya Amini: Looking back at smartphones and Google Maps, it's hard to remember how the world looked like before these technologies. It's a similar situation with gen AI β I can't remember how I was solving the problem without it. I can focus more on actual work.
Now I use AI as a kind of assisted tool. My main focus at work is on requirement gathering, like software design. When it comes to the coding, it's going to be very quick. Previously, it could take weeks. Now it's a matter of maybe one or two days, so then I can actually focus on other stuff as AI is solving the rest for me.
Kesha Williams: In my role, it's been trying to help my team rethink their roles and not see AI as a threat but more as a partner that can help boost productivity, and encouraging my team to make use of some of the new embedded AI and gen-AI tools. Really helping my team upskill and putting learning paths in place so that people can embrace AI and not be afraid of it. More of the junior-level developers are really afraid about AI replacing them.
Hood: Are there new career tracks opening up now that weren't here before?
Verma:At Nice, we have something like 3,000 developers, and over the last, I think, 24 months, 650 of them have shifted into AI-specific roles, which was sort of unheard of before. Even out of those 650, we've got about a hundred who are experts at things like prompt engineering. Over 20% of our developers are not just developers being supported by AI but developers using AI to write features.
Kapoor: I think one of the biggest things I've noticed in the last two to three years is the rise of a job title called "AI engineer," which did not exist before, and it's kind of in between an ML engineer and a traditional software engineer. I'm starting to see more and more companies where AI engineer is one of the top-paying jobs available for software engineers. One of the cool things about this job is that you don't need an ML-engineering background, which means it's accessible to a lot more people.
Greg Jennings: For developers who are relatively new or code-literate knowledge workers, I think they can now use code to solve problems where previously they might not have. We have designers internally that are now creating full-blown interactive UIs using AI to describe what they want and then providing that to engineers. They've never been able to do that before, and it greatly accelerates the cycle.
For more-experienced developers, I think there are a huge number of things that we still have to sort out: the architectures of these solutions, how we're actually going to implement them in practice. The nature of testing is going to have to change a lot as we start to include these applications in places where they're more mission-critical.
Amini: On the other side, looking at threats that can come out of AI, new technologies and new positions can emerge as well. We don't currently have clear regulations in terms of ownership or the issues related to gen AI, so I imagine there will be more positions in terms of ethics.
Mithal: I feel like a Ph.D. is not a requirement anymore to be a software developer. If you have some foundational ML, NLP knowledge, you can target some of these ML-engineer or AI-engineer roles, which gives you a great opportunity to be in the market.
Williams: I'm seeing new career paths in specialized fields around ML and LLM operations. For my developers, they're able to focus more on strategy and system design and creative problem-solving, and it seems to help them move faster into architecture. System design, system architecture, and integration strategies β they have more time to do that because of AI.
Jean Paik: What skills will developers need to stay competitive?
Verma: I think a developer operating an AI system requires product-level understanding of what you're trying to build at a high level. And I think a lot of developers struggle with prompt engineering from that perspective. Having the skills to clearly articulate what you want to an LLM is a very important skill.
Williams: Developers need to understand machine-learning concepts and how AI models work, not necessarily how to build and train these models from scratch but how to use them effectively. As we're starting to use Amazon Q, I've realized that our developers are now becoming prompt engineers because you have to get that prompt right in order to get the best results from your gen-AI system.
Jennings: Understanding how to communicate with these models is very different. I almost think that it imparts a need for engineers to have a little bit more of a product lens, where a deeper understanding of the actual business problem they're trying to solve is necessary to get the most out of it. Developing evaluations that you can use to optimize those prompts, so going from prompt engineering to actually tuning the prompts in a more-automated way, is going to emerge as a more common approach.
Igor Ostrovsky: Prompt engineering is really important. That's how you interact with AI systems, but this is something that's evolving very quickly. Software development will change in five years much more rapidly than anything we've seen before. How you architect, develop, test, and maintain software β that will all change, and how exactly you interact with AI will also evolve.
I think prompt engineering is more of a sign that some developers have the desire to learn and are eager to figure out how to interact with artificial intelligence, but it won't necessarily be how you interact with AI in three years or five years. Software developers will need this desire to adapt and learn and have the ability to solve hard problems.
Mithal: As a software developer, some of the basics won't change. You need to understand how to scale models, build scalable solutions, and handle large-scale data. When you're training an AI model, you need data to support it.
Kapoor: Knowledge of a programming language would be helpful, specifically Python or even JavaScript. Knowledge of ML or some familiarity with ML will be really helpful. Another thing is that we need to make sure our applications are a lot more fault-tolerant. That is also a skill that front-end or back-end engineers who want to transition to an AI-engineering role need to be aware of.
One of the biggest problems with prompts is that the answers can be very unpredictable and can lead to a lot of different outputs, even for the same prompt. So being able to make your application fault-tolerant is one of the biggest skills we need to apply in AI engineering.
Hood: What are the concerns and obstacles you have as AI gains momentum? How do you manage the expectations of nontech stakeholders in the organization who want to stay on the leading edge?
Ostrovsky: Part of the issue is that interacting with ChatGPT or cloud AI is so easy and natural that it can be surprising how hard it is actually to control AI behavior, where you need AI to understand constraints, have access to the right information at the right time, and understand the task.
When setting expectations with stakeholders, it is important they understand that we're working with this very advanced technology and they are realistic about the risk profile of the project.
Mithal: One is helping them understand the trade-offs. It could be security versus innovation or speed versus accuracy. The second is metrics. Is it actually improving the efficiency? How much is the acceptance rate for our given product? Communicating all those to the stakeholders gives them an idea of whether the product they're using is making an impact or if it's actually helping the team become more productive.
Williams: Some of the challenges I'm seeing are mainly around ethical AI concerns, data privacy, and costly and resource-intensive models that go against budget and infrastructure constraints. On the vendor or stakeholder side, it's really more about educating our nontechnical stakeholders about the capabilities of AI and the limitations and trying to set realistic expectations.
We try to help our teams understand for their specific business area how AI can be applied. So how can we use AI in marketing or HR or legal, and giving them real-world use cases.
Verma: Gen AI is really important, and it's so easy to use ChatGPT, but what we find is that gen AI makes a good developer better and a worse developer worse. Good developers understand how to write good code and how good code integrates into projects. ChatGPT is just another tool to help write some of the code that fits into the project. That's the big challenge that we try to make sure our executives understand, that not everybody can use this in the most effective manner.
Jennings: There are some practical governance concerns that have emerged. One is understanding the tolerance for bad responses in certain contexts. Some problems, you may be more willing to accept a bad response because you structure the interface in such a way that there's a human in the loop. If you're attempting to not have a human in the loop, that could be problematic depending on what you want the model to do. Just getting better muscle for the organization to have a good intuition about where these models can potentially fail and in what ways.
In addition to that, understanding what training data went into that model, especially as models are used more as agents and have privileged access to different applications and data sources that might be pretty sensitive.
Kapoor: I think one of the biggest challenges that can happen is how companies use the data that comes back from LLM models and how they're going to use it within the application. Removing the human component scares me a lot.
Verma: It's automation versus augmentation. There are a lot of cases where augmentation is the big gain. I think automation is a very small, closed case β there are very few things I think LLMs are ready in the world right now to automate.
The office β and the role it plays in companies β is at the center of workforce change.
Neil Murray, a Workforce Innovation board member, discussed workspace purpose, leadership, and AI.
This article is part of "Workforce Innovation," a series exploring the forces shaping enterprise transformation.
Commercial real estate has experienced a tumultuous few years, with pandemic-related office vacancies and high interest rates. The sector is also at the epicenter of significant changes to the global workforce.
"It is the most incredible time to work in this industry," said Neil Murray, the CEO of Work Dynamics at JLL. "We are at the center of some really important strategic conversations about the very nature of work."
Work Dynamics is a division of the global real-estate corporation that collaborates with corporate clients on technology, employee experience, and design strategies. Murray says its goals are to help client companies attract and retain employees and foster productivity.
In its annual global Future of Work survey, which involved 2,300 corporate real-estate and business decision-makers, some 64% of respondents said they expected to increase their head counts by 2030.
JLL's third-quarter earnings beat estimates β it reported revenue of $5.87 billion, an increase of 15% from the same period in 2023.
Murray talked about companies mulling the purpose of the office, how leaders can incentivize employees to willingly go into their workplaces, and how to harness AI for concrete breakthroughs.
The following has been edited for length and clarity.
How have the priorities of your clients changed in the aftermath of the COVID-19 pandemic and the changes that brought to office life?
What we do for a living changed dramatically through the pandemic. Previously, corporate real estate may have been seen as a sort of factor of production. We weren't intentional about why we had space and where we had it, what we wanted that space to do, and its function. Is it a cost line, or is it an investment?
Suddenly every chief executive in the world had a view on real estate. It brought much more intentionality about its function within the organization and its ability to contribute to broader organizational goals.
Our business now is about helping our clients navigate that complex situation where they're planning to grow their workforce over a number of years, balancing what that might look like in the macro environment we're living in. It's a very complex environment for leaders to think through.
What's the state of return-to-office you're seeing among your clients?
There's a fairly even split between companies that are embracing some sort of hybrid policy and those that want their people back full time.
In our Future of Work survey, we found that 85% of organizations had a policy of at least three days of office attendance per week, and 43% expected the number of days in the office to increase by 2030.
It's still very much an evolving scenario. The metrics of productivity that we've relied upon to make database decisions don't always capture the challenges that businesses are facing. The time people spend doing emails or logged in doesn't necessarily translate to productivity.
One client, for example, has found that while their productivity metrics looked just fine, the number of patents had fallen off a cliff from prepandemic levels.
That led to this notion that what we're missing is, as the phrase goes, people painting on the same canvas at the same time.
Now we've seen some high-profile companies coming out, wanting more time spent in the office, saying there's something lost around culture and the collective sort of personality and purpose of an organization because of remote working. Companies are finding it really difficult to balance that.
What aspects of the workplace are most effective for enticing workers to return to the office?
The overwhelming evidence is that it's not a single amenity but it's other people βΒ and, in particular, leaders. Companies that are intentional about their leaders being present have seen the greatest results in terms of people coming back.
What people crave is proximity to leadership for personal development. So without getting leaders back into the office, you can add whatever amenities you want and you'll still have significant challenges.
Clients that enacted three-days-a-week mandates but didn't focus on leadership presence have exactly the same attendance as those who didn't have three-day mandates.
Could that be attributed to people just wanting to be visible when the boss is around?
I wouldn't purport to understand entirely the psychology of humans, but I do believe that our research and my own experienceΒ is that people enjoy other people. The most important amenity in any workplace is that notion of community and other folks to chat to.
The notion of apprenticeship in all aspects of what we do is very real. The ability to learn from others, to absorb how things are done or navigate the complexities of an organization, is really difficult to do among 30-minute slots. You don't get to sort of naturally observe through osmosis what's happening in the world around you.
You mentioned in one of our roundtables that companies need to focus on consistent, breakthrough innovation across the organization as opposed to incremental innovation from a centralized department or team. Why is that important, and how can leaders work toward that goal?
When you centralize innovation, you can get stuck in the paradigm of trying to incrementally improve a particular way of working. But the technology breakthroughs mean that it's fundamentally shifting how we do business.
In my business alone, the rapid adoption of AI tools in daily business use has surprised us all. We are an organization with 250 years of data on everything from how buildings are occupied and used to what they cost to run to their utilities to their capital values.
The tools available to us now to cut and splice and curate and make connections in that data, which we were never able to make before at scale, are driving us to think about the business in completely different ways.
Breakthrough innovation comes about when you use a large language model to interpret multiple data sets and then you start to ask the second, third, and fourth questions, going deeper and deeper into a particular topic. You find things that you could not have possibly seen or connected otherwise.
