Francis Hintermann directs Accenture’s thought leadership research machine. As global managing director of Accenture Research, he runs a 350-person unit that has been helping the $63 billion (revenue) global consulting, IT services and outsourcing company stay on the forefront of digital expertise. He joined the company in 1998, created its Strategy Research team (with members in North America, Europe, Latin America, Africa, and Asia Pacific) before being named to run all of Accenture’s thought leadership research activities. Francis moved from Paris to New York City in 2015 to expand the team.
A Chief Economists Community member of the World Economic Forum and a corporate strategy professor at the Paris-based university Sciences Po, Francis has a world of experience to draw from. In this episode of “Everything Thought Leadership,” he talks with Bob Buday about how Accenture’s thought leadership research has changed over the years; the impact of AI and ChatGPT on the research and writing process; how Accenture develops and tests its ideas; where the thought leadership research unit reports to at Accenture and why that’s important; how they partner with Accenture’s world-class marketing function; and how they work with universities on critical research.
Listen to the Podcast
Transcript: Francis Hintermann and Bob Buday
Bob Buday: Great to have you on the show, Francis. I interviewed you three years ago, in early 2020, before we had this video podcast series. Now people can hear and see you talk and not just read a transcript of our conversation.
This November you will have been at Accenture for exactly 25 years. If we turn back the clock to 1998 when you joined the firm, it was a little under $10 billion in revenue, and it had fewer than 100,000 employees — still a big firm. But today, revenue is $61 billion. That’s six times that of the year you joined. And headcount is more than 700,000 — seven times more than when you got there.
When you look back, how has thought leadership research changed at Accenture over those years?
Francis Hintermann: Bob’s, good to be with you today. The company has changed a lot. When I joined the company, it was not even named Accenture, it was Andersen Consulting. It was not a [stock exchange] listed company; it was a partnership. That mattered because I joined the company in France, and at the time each country was a partnership with its own direction, to a large extent.
Then we became a listed company with a new name. And we developed some global functions. It happened for the thought leadership research function. What used to be an archipelago of various small teams scattered around the world became a global team with a global role and some global direction.
That changed the way we work a lot, and our impact – both internally and externally — over the years. In terms of the content we produce and what we disseminate, the biggest change by far is about the data – recently, AI and even more recently, generative AI. What used to be an activity based on the collection of a few [pieces of] data has become a data-led organization that produces data- and AI-led publications in various formats. For me, that’s the biggest change we have lived through in the past 20 years.
Bob: I imagine that Accenture has also increased the number of people doing thought leadership research since it became a public company 20 plus years ago.
Francis: Yes. Altogether, it was something like 30 to 35 people [in thought leadership research back then]. Now we are between 300 and 350. So the company has grown 10 times in 20 years, and the team has grown 10 times as well, following the growth of the company because the activities have become a lot more complex. Our research covers 19 industries. We have significant activities in more than 20 countries. And we cover all the main functions of companies [sales, marketing, product development, supply chain and manufacturing, procurement, customer service, etc.].
And of course, it means that when you produce research for thought leadership, it’s not about producing some type of a global POV that has a minimum common denominator. It’s about producing something that is highly relevant for each industry and for each business function — and within each geography in which we operate. By the way, in most cases, it’s produced in local languages. We present local case studies.
That means our work has become a lot more complex. And we live through this paradox, which I’m sure you live with every day: On one end, our work is getting even more complex every day. And on the other end, we have to communicate to our clients, our ecosystem partners, and our communities in as simple a way as possible, and simpler every day.
It is not only about the famous Tweet; it’s about the infographics. It’s about the 15-second video on Instagram or on TikTok. It’s about communicating in a way that is snappy and memorable and must be based in fact, and on a very complex set of analysis that people are not aware of [but want to be], which leads them to dive into all of our data as much as possible.
We see and know that most of our audience just wants a simple conclusion with a few relevant pieces of data. That’s the paradox of our activity today.
Bob: One of the complexities of creating that simplicity is that you’re competing against many more points of view on any topic than you were (or than any of us were) 25 years ago, right?
Francis: That’s true to some extent. But we want to be very different from all other players in the market. We recognize that in some areas there is intense competition. Part of our work is to support the leadership of the company to build some things that are going to be really different than what our competitors have.
But overall, you’re absolutely right. I think what we’ve been living through is the democratization of access to ideas [due to] the democratization of access to technology. That’s what the cloud is about. And that’s what ChatGPT is about these days…It’s a great equalizer. Because of all these tools, we are giving lots of people access to more ideas and more data. That, of course, is raising the bar of the competition.
Deeper and Richer Data
Bob: Indeed. Tell us tell me more about the data you collect. What kinds of data are you collecting that maybe you didn’t collect 25 years ago?
Francis: When my former colleague, Paul Nunes, was doing the analysis of what we called high-performance business 20 years ago, it was based predominantly on financial data. Fast forward to today, and we are developing analysis around what our CEO Julie Sweet calls 360-degree value. That means we also collect and analyze lots of non-financial data.
That data is about the environment, social data, corporate governance data, and employee feedback that we were not collecting 20 years ago. All these types of data create much bigger datasets. They are much richer and much more complex than the data we used to collect and analyze 20 years ago.
Bob: That’s a function of the fact that all this data is now available. A simple example is having transcripts of quarterly investor calls that public companies make available online. They were hard to get 25 years ago. They often weren’t recorded or transcribed.
Francis: It’s an example that is dear to me because I started my professional life by analyzing transcripts of earnings calls, which in essence were on paper. And now we’ve got a team of data scientists that in a few minutes is analyzing data from hundreds or thousands of earnings calls every week. That has increased not only the volume of activity and productivity, which has gone up phenomenally. It has also changed the type of insights we can develop because of the automated analysis and data science that we use today.
We can uncover insights that we could not uncover 20 years ago when we were doing that line by line, with a pen basically. That, to me, is extraordinarily interesting because we bring lots of new insights that we couldn’t bring before. And we are bringing that to the leadership of our company and to our clients, and ultimately to our communities as well.
Bob: If you possess that data and you have the tools and the people to analyze it quickly, that must give Accenture an advantage in finding best practices over competitors that don’t have the tools.
Francis: Absolutely, yes. It’s a combination of various sources of [competitive advantage in doing thought leadership research]. The one you mentioned is definitely one of them. There are other sources of information. Accenture has built up a unique set of case studies from our own experiences with clients. We work with the 2,000 largest organizations in the world. We collect information inside from our client experiences every day. Now we sanitize, anonymize, and respect all the aspects that we have to respect. It gives us unique insights.
Where Accenture Research First Tests New Ideas: On Itself
Francis: [Accenture CEO] Julie Sweet says the first question she always gets when she presents to clients is this: “What about Accenture?” When you’re a company of $60 billion [revenue] with 700,000 people and you preach something, the is “Do you do it yourself?” That’s why when we develop new tools and techniques, we always test it on ourselves first.
For example, my thought leadership research team is piloting different applications of ChatGPT. Before we offer it to clients, we test it on our own knowledge to make sure that when we use it, it will be a relevant tool for them.
Bob: You don’t want to be the cobbler’s children who go without shoes?
Francis: Absolutely. For us and for me, it’s always a lesson to stay humble, to make sure that whatever insights we develop and whatever recommendations we give, what matters is what resonates with our clients. We hear from some clients who say, “Your insights are interesting. Most of the time you’re 15 minutes ahead of competitors. Now help us create a path between the insight and the actions, and give us recommendations that are really actionable.”
Ahead of the Curve on AI
Bob: I think a great example of what you’re talking about is your new generative AI research. It hit the market in March, not too long after ChatGPT was unleashed into the world last November. How did you have the foresight to launch a study on the impact of generative AI even before ChatGPT became famous?
Francis: That’s a very good point. The wonderful thing about ChatGPT is that it has triggered a much larger discussion on generative AI. And the discussion has been about the potential benefits as well as the risks of implementing AI at scale in organizations.
We’ve been studying AI for years in this company, and have published thought leadership on it for some time. We’ve been updating our analysis from working with clients and partners for years. In that sense, we were prepared to study it and establish a diagnosis that helps us look at it.
Generative AI is one step toward a larger discussion about the use of AI across organizations. And it’s part of the tools we use to understand in surveys why companies have adopted or not adopted new practices. It’s given us an element in surveys that is extremely interesting, and you generally don’t have it when you just collect survey data. You can look at correlation. You can’t necessarily establish causation with surveys, but you can get closer to it.
With lots of the data that we collect now, we can manipulate it and develop test models. These models are extremely helpful. When you think about generative AI, for instance, you can look precisely at its impact within each job in multiple industries – and in every single task. Will it be impacted or not impacted by generative AI?
From that, you can develop a diagnosis about the number of hours that are going to be impacted for each job in each industry. You can establish a more nuanced assessment about its future potential, which is a lot more useful to clients vs. saying, “It’s going to eliminate X thousand jobs.” In our view, the point of the discussion should be how it could impact each job, and what can be done about it.
That’s the modeling you can develop with lots of data, which is available these days. It enables Accenture to have much richer discussions with clients.
Bob: I really liked the way you did that research. It wasn’t a survey per se. It was using U.S. Bureau of Labor statistics, and analyzing the tasks of many jobs in many industries.
Francis: As a European person living in the U.S., I can tell you the richness of the data you have access to in the U.S. is absolutely phenomenal. It enables us, and of course lots of other companies, to do granular analysis. That means it is going to be useful to our clients. Chief human resource officers, chief financial officers, chief marketing officers – they’re not going to have necessarily the same issues. But you can massage with data to make the data analysis meaningful to each audience.
Bob: I imagine that study has generated a lot of client interest.
Francis: Yes, absolutely. We’ve seen that since ChatGPT came into the world last November. As well, we regularly survey C-level executives. … Our clients want to see much more granular analysis [from our thought leadership content], and that happened before ChatGPT, and even before COVID.
The fascinating aspect is that you’ve got exponential growth of technologies such as generative AI. At the same time, business processes are not advancing exponentially; their advancements are more linear in the best cases. In between the exponential growth of technology and the linear growth of the business processes, you’ve got a value gap. Companies understand that the faster they close that value gap, the more value they will create.
The Shift in Accenture’s Thought Leadership Research Model
Bob: Let’s switch gears here. At our thought leadership conference last November in California, in your speech you mentioned that Accenture Research has shifted its research strategy from quantity to quality. Tell us tell us more about that and what triggered it.
Francis: It’s been an interesting journey. On one end, you’ve got a company that has been growing predominantly through organic growth, as well as some inorganic growth. We’ve got more and more people joining Accenture who are thought leaders and are keen to publish books, articles and other thought leadership.
At the other end, the decision that we took with the person to whom I report, the chief strategy officer of Accenture [Bhaskar Ghosh]; and our chief marketing officer, Jill Kramer, with whom I work closely as well; and with the company’s business leaders; and ultimately Julie Sweet, was not to publish more of the same every day. We don’t want to get into a volume contest.
Our position is to get into the value game. [We want] to be extremely careful about our number of publications — and when we publish, to have something significantly valuable to say to our communities, our clients, our ecosystem. It must differentiate us, and it must add value to them.
That’s the way we’ve been driving thought leadership for the past few years. It means we have to implement a quite rigorous and disciplined process to get there. We are fortunate that Accenture has plenty of business leaders with smart ideas. It’s a matter of getting them involved in the right process.
They have the incentive to work with us and therefore make sure that what we publish is making a difference in the market. We must always strike a balance: We want to unleash the creativity of all our business leaders. At the same time, we want to be very intentional in the way we communicate to the 19 industries, the 20 countries and the seven functions that I mentioned earlier. It requires some degree of coordination to make sure we converge in a way that is ultimately beneficial to our clients.
Bob: Has it been easy to make that shift from quantity to quality — from too many studies to fewer but deeper, more substantive studies with bigger ideas?
Francis: Of course, it has been easy (laughs)! Of course, it has been easy because we’re working with smart people. The point is to find the right way to motivate all the people to join us on that journey. That is what we’ve been doing. I’ve been fortunate to get the support of top leadership at our company, in working with external partners to show internally what good looks like from a client’s point of view.
Then it requires working on ourselves, using reviews to make sure that what we publish is at this bar we set for quality. In essence, it means it’s more value gain than volume gain. When you get into a virtuous circle, [Accenture leaders and others] want to join in because they can see the impact of it.
We’ve developed a certain number of mechanisms as a forcing function for that. One is what we call our Thought Leadership Forum. Once a month we feature the two best projects of the month. Our business leaders come and speak as they would speak to clients about what’s great in their projects. We have that and other ways of mobilizing and showing people how to convince our clients we’ve got something that’s worth listening.
We’re not going to overwhelm [clients] with publications that are not differentiated. We are careful about that. We are intentional in what we share with them.
Where Thought Leadership Research Reports Makes a Difference
Bob: It seems to me that one of the secrets to the success of Accenture Research is where it’s positioned in the company. You report to the chief strategy officer, who reports to the CEO. And then you’ve got a great partner in Jill Kramer, your CMO. In many other organizations, you find thought leadership reports to a CMO who really doesn’t believe in thought leadership. And if there is a thought leadership research function, it can be starved of resources and starved of respect at the top.
It’s not that way at all at Accenture. Do you agree with the premise that thought leadership research reports can have a big impact on how well the function does?
Francis: I totally agree. The fact that we report to the [chief strategy officer] means we are aligned with business priorities. We are part of some business leadership meetings so we know what is coming from clients and what clients crave. We are aware of the company’s main priorities as well, and we can design our thought leadership programs accordingly. That’s one important point.
The second important point is the one you mentioned. We are lucky enough to have a CMO who loves thought leadership and understands the value of it in the marketing journey — especially at the beginning of the journey, when it’s about connecting with C-level executives and opening meaningful business discussions.
So [thought leadership] is really a collective game. We have been very intentional about the way we have developed this collaboration across the different entities [of the company] – with Accenture’s business leaders from Consulting, from Technology, from [Accenture] Song, which is our digital marketing activity, and former technology and operations. It really is a collective game.
The last point is that there is always a creative tension between centralization and decentralization. Decentralization is very good because it unleashes creativity. We have put some locks in place, which are more centralized in the overall process, to ensure there is a certain level of coherence in our messages. Of course, it takes time to convince colleagues why centralization is needed at some points in the value chain.
Where Generative AI Fits Within the Accenture Research Toolkit
Bob: Let’s change gears a bit. Tell me, to the extent that you can, how you are using generative AI in thought leadership research. Are you using it to collect data? Analyze data? Write reports?
Francis: Well, first of all, I loved the blog you published on that some months ago because I think you nailed it. If we look at the six steps of the thought leadership process that you developed in your blog, I agree with everything you wrote — especially about the way that ChatGPT can be used at the start of the process, when you are in research design, looking at the white space, looking at the formulation of a problem. That can help a lot.
Similarly, in the sixth step of your process [writing], using ChatGPT to produce a first draft for a publication can be extremely helpful. Then the big question is in the middle steps, and that’s the only nuance I was adding to your analysis, the middle being the data analysis, where we believe we can actually do a lot with [the technology].
Of course, that raises the question about what intellectual property you put and don’t put in the public domain. But broadly speaking, these models can be used a lot for data analysis. That’s where I think it’s going to make a difference as well.
In the first part of the process – in thought leadership research design, as I mentioned earlier – I think ChatGPT and other models are a great equalizer that are going to help people all over the world who want to develop thought leadership to [raise the baseline of their performance]. The floor is going to rise, and it’s going to get higher. Everybody will be able to get to that floor.
The same thing for the writing part of the process. Now, while you have a [technology] that can do a good draft, it won’t necessarily make you the best writer of the year. So I think we have to keep that in mind. But again, as a way to raise the bar of writing quality … I think it’s going to be extremely helpful. And we can see that internally.
Now the big question for us is the data analysis in the middle of [that six-step process]. When we speak about generative AI and ChatGPT being the great equalizer in terms of raising the floor [of competence in research design and writing], that’s about productivity. The question is how we use it to get to the “ceiling” [of content quality] – which is about creativity. In my view, that’s where you’re going to see it make a difference. And that’s where we want to compete: We want to raise ceiling with this type of model. … And we hope to show you some of the results very soon.
On Generative AI for Framework-Making
Bob: That’s fascinating. Do you think within three years that people who conduct thought leadership research will be using ChatGPT or another model of generative AI to create thought leadership frameworks? You know, to create the equivalent of Michael Porter’s five forces or value chain models, or Michael Hammer and Jim Champy’s business system diamond for business reengineering? This has typically has been the province of really smart consultants and smart professors.
Francis: It’s a very good question. I don’t think we can answer that right now. That gets us more into the area of consulting, where my consulting colleagues, should you be interested in inviting them to answer that, could answer better and more precisely than I can.
What I can tell you, though, is that in the thought leadership research space, I strongly believe that every single thought leadership team will have the data science core. In three years, we will outcompete the teams that do not have the data science core. I am convinced of that. That’s why we started building our data science core some years back, and why we are accelerating it now and growing it. We believe it will eventually create a competitive advantage.
Creating a Strong Internal Partnership with Accenture Marketing
Bob: Great stuff. Let’s talk about a conversation we had three years [at our 2020 virtual conference on thought leadership], when Jill Kramer was about to become chief marketing officer at Accenture. You talked about the very strong working relationship that Accenture Research had with Jill and her marketing group. Explain why it’s important for a company’s thought leadership research group to have a very strong working relationship with marketing, and how one establishes such a relationship.
Francis: It’s absolutely crucial. We really believe in the need for our two teams – Accenture Research and Accenture Marketing – to work together. Jill was in charge of brand when you interviewed her three years ago, and then she became chief marketing officer afterwards.
We have always believed in that type of deep collaboration. It really has an impact on the content and on the channels. The channels for reaching and convincing an audience of our ideas are critical [for us in thought leadership research]. With Jill and her group, we have developed a smartphone app that is now available on iOS and Google Android, where we publish a lot of thought leadership.
Now it’s good to have our content in PDF-style reports. But when you publish on an app, you can do a lot more. You can do videos, a lot more data visualization and podcasts. And we can give access to people who want to look at our research data.
Jill herself has been doing 15-minutes fireside videos on topics. Whether it’s a video blog, or podcast or long-form publication, an ad or open data, it’s something that Accenture Marketing and Accenture Research discuss together. We want to get the right combination for the audience we think we should target for a specific Accenture thought leadership publication.
There is no one-size-fits all; we do not believe in that. So it’s very important to get the right mix of channels every time. And the good thing with our app now is that you get feedback nearly instantaneously in the sense that people open it or do not open it. They stay on the video or they do not stay on the video. It is extremely helpful to us.
Bob: The app is fantastic. I applaud everybody who was involved with that app. It must have been in the works for many months.
Francis: Yes, and it’s because of the same point I mentioned earlier: Given the size of our company, we had the mandate and obligation to test it on ourselves first. That’s what we’ve been doing behind closed doors for a certain number of months. When we opened it to everybody, we knew it was already good enough. We continue to improve it, but it was already at a certain level of quality before we released it.
Bob: What kinds of things are you learning from the data that the app is giving you? And how are you using that?
Francis: To a large extent, it’s in the making; we are in the early stages of the deployment of the app. It’s available everywhere in the world, and we learn more every day. We can see in the number of people who are getting connected to it. It’s still increasing. In that sense, we are very optimistic about the future.
And we can see interest in a variety of formats. That was something that we were already doing to some extent on the website. But we can now see that even more clearly on the app — as I was mentioning earlier, whether it’s a video or podcast, data visualization, this type of blog, this type of short format is attractive to the audience.
Partnering with Universities on Thought Leadership Research
Bob: You said at our thought leadership conference last November that at times you partner with professors on research, and that these research collaborations can take longer. You mentioned you worked with Harvard Business School for three years on a topic.
How important is it to do research with leading academics, and what has proved to be crucial in working with them effectively?
Francis: It’s absolutely fundamental. The partnership with Harvard Business School has been very fruitful for Accenture and my own team. We’ve got a partnership with MIT as well. And we have a partnership in Europe. I was in Paris recently, where we have a new partnership with a school in France called SciencesPo. And we have one with an engineering school called Polytechnique. We’ve got partnerships of that kind around the world, and we are developing them.
These partnerships with academia bring a level of depth and rigor in the research that is fundamental to the work we do. It’s fundamental for us to get to the core of the topics we study with them. By partnering with them, it takes more time. These may not necessarily be the hot topics of the day, but they may become hot topics in four years.
As a research team, we have the right to pick topics at the right time with the right type of outcomes. For that reason, these partnerships are fundamental. And on the human side of it, we preach diversity all the time at Accenture. For me, it’s fundamental that we implement that in our daily life. Working with professors brings a certain type of diversity in our work, which is extremely helpful.
I’ve been in consulting for 20 years. It’s extremely helpful to get the perspective of people who are somewhere else in our communities and ecosystem. That’s what these people bring as well. At the end of the day, we are a people-based organization. It’s important that we develop the right connections with people around the world, including for my team as well, because they can see how people who are professors and researchers for 10-20-30 years do their work.
That’s inspiring for the next generation — even if the next generation stays in consulting.
Why Thought Leadership Must Go Faster
Bob: What questions should I have asked you that I didn’t on how to run thought leadership research?
Francis: I think what has really changed in thought leadership is the speed. Now you can test and fail and test and succeed a lot faster than you could 20 or 30 years ago, when you had a good idea and tested it in an article in a business journal. Depending on the success of the article, you can develop it into a book and share it with clients and then develop consulting offerings alongside it.
Now things go a lot faster. To some extent, they can overlap. But in terms of what you call the supply side [of thought leadership], the fundamentals are still the same.
One thing I communicate again and again to students, colleagues and the people we recruit is that it’s not enough to have access to everything and anything — through the web, through generative AI, and other tools that you can find online – to help you develop some original ideas that you can support with new facts and data. You still need an intellectually rigorous process of developing and testing hypotheses.
That process is not a commodity. Of course, you need to use all the new tools so you can get faster, cheaper, and more productive overall. At the end of the day, you still have to develop these new ideas, because it’s the best way that to convince organizations that they need to change and how do it.
Bob: Well, Francis, this has been a really great conversation. Keep up the wonderful things you and your and your people are doing.
Francis: Thank you, Bob. We’ve got a pretty full agenda in front of us.
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