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The Drucker Forum, held annually since 2009, is a yearly opportunity to review management practice and question the state of research, a favourite combination from “management guru” Peter Drucker (1909-2005). The IADS attended the 16th edition of the Forum this month in Vienna. The theme was “the next knowledge work," questioning how organisations can deliver new value creation and innovation levels.

AI was obviously a centrepiece of the conversations, given the impact it has had so far on knowledge and innovation. While the overall conference themes were oriented towards knowledge workers, including researchers, scholars, and academics, it was interesting to relate them to the current situation in retail, where AI is seen as a transforming force for business models. Taking on what was discussed during the conference, AI appears to be, in fact, a pretext for more radical organisational transformations.

Paradoxically, achieving such transformation also does not systematically involve ground-breaking technological or intellectual innovation, as, many times, speakers were calling for a “back to the basics”movement in an updated way.

Introduction: the concept of “next management”

The late Peter Drucker predicted that the challenge for the 21st century would be finding ways to improve knowledge work productivity like manual and factory work did during the 20th century. He was also famous for considering management as a foundational value creating capability, rather than a mere business role. However, most of the political, intellectual and cultural elites keep on considering management as a tool serving short term goals, rather than a true social innovation able to change society at large.

This is why this 2-days session started with Richard Straub, founder and President of the Forum, introducing the audience to the concept of “next management” (new to half of the room). This five-year research initiative aims to provide organisations with a holistic method to boost knowledge workers’ productivity by continuously injecting innovative practices (and not implementing them in an incremental way as has been done so far). In addition, this method aims at optimising human investments rather than increasing them in a world where resources are increasingly limited.

Due to its englobing approach, it challenges the traditional boundaries of management and questions many of the structural elements that every professional has grown to take for granted during the 20th and 21st centuries: organisation charts, hierarchy and processes.

In short, a world which has radically changed can not be seen through lenses that have not been updated, independently of any technological breakthrough such as AI. While AI is accelerating the tempo, defining the “next management” playbook goes well beyond adapting to this new technology as it is a way for companies to adapt to the realities of a new world that has become much more complex, in many aspects.

However, for the “next management” to be perfectly accurate, one needs to review first the nature of knowledge workers and understand how it has evolved in the age of AI.

Dealing with innovation and knowledge

Where does knowledge work stand today, and where is it going?

Giampiero Petriglieri, an associate professor at INSEAD, thought-provokingly opened the topic by stating that “knowledge work as we know it is dead, and this is not due to AI.” For him, current work organisations have killed knowledge work due to their inability to evolve past a productivity-oriented model, inherited from the 20th century using measurement tools created for the industry and then transferred to intellectual work, still in use after five decades. Not only is a mechanistic approach to knowledge work, prioritising efficiency and productivity over humanistic values such as inclusion and freedom, obsolete, but it also puts the job in danger because it creates the very wrong impression that AI is a replacement for it.

However, he points out that organisations are increasingly efficient but also struggling to innovate. For him, this relates to the fact that knowledge productivity is not so much of an issue anymore but the purpose of learning itself due to the emergence of AI. To counter this, he used the analogy of a "machine" versus a "home" to illustrate the difference between instrumental and humanistic approaches to organisations, leading him to call for creating efficient but safe and hospitable workplaces, fostering a sense of belonging. AI is not enough to enable companies to be a good “home” to knowledge workers: “The knowledge world is dead...because now we realise that even when we share those humanistic values...we often do it through an instrumental lens. Let's keep people more comfortable; let's make our culture more congenial so we can all be more productive.”

The fact that AI pushes companies to re-think their core purpose and how welcoming they want to be to their teams has become even more urgent due to AI: Alex Adamopoulos, CEO of Emergn, stressed the importance of maintaining a human-centric approach amidst the AI boom, cautioning against the hype and emphasising the need for practical knowledge and a common vocabulary around AI. This remark from a practitioner suggests that fostering home-like working environments where employees feel a sense of belonging and are encouraged to grow personally and professionally is key to dealing with all the changes AI is bringing to intellectual work in general and innovation in particular.

Such views go beyond the traditional interrogations on how to deal with innovation in legacy retailer organisations (through new business units, dedicated committees, or resorting to consulting companies…). The Drucker Forum speakers suggest that to become a truly next-generation structure, current retail players need to reinvent themselves by rethinking the value proposition they want to bring forward to all their knowledge workers to get the best from them and implement a generalised culture of innovation.

But do we have the right innovation frameworks within organisations?

All Drucker Forum speakers agreed that the existing innovation frameworks are outdated. Valla Vakili, Global Head of Innovation at Visa, highlighted that AI now questions the very notion of innovation itself in an era where organisation size does not matter to be the most innovative possible. While in the past, large organisations had an edge in innovating for a simple question of available resources, we now live in a time of potential “one-person unicorns” as coined by Bain & Co during the IADS AI Retreat from last June. AI also redefines what progress is: while in the past, innovation was often associated with disruption and a defensive, antagonistic approach (the “innovator’s dilemma), AI now allows innovation to be much more offensive and imaginative. Vaikili argued that AI offers new tools to overcome past constraints on innovation, enabling a shift from a scarcity model to an abundance model (in other words,while many companies are good at innovating in a forward-thinking model, backward thinking is often overlooked).

Jayshree Seth, Chief Science Advocate at 3M, echoed this sentiment, emphasising the need to move beyond one-off initiatives like hackathons and “ideathons” towards a culture where innovation is a foundational element. She explained that “hackathons are often internally viewed as very cool, teams present beautiful ideas to ecstatic management… and nothing happens.” Instead, she stressed the importance of employee empowerment and radical collaboration within and across the broader ecosystem, a view supported by Julie Teigland, Managing Partner at EY, who explained that true innovation could only stem from “a close connection with all stakeholders, customers, employees, shareholders.”

Organisational reinvention is inescapable

Companies have little choice but to reinvent themselves in a world shifting from expertise-based to skills-based learning, as this is the only way to ensure employees can adapt and contribute in an ever-changing environment. The implications include investing in employee training and development, fostering open communication, and promoting cross-functional collaboration. Implementation requires a concerted effort from leadership to cultivate a culture that values collaboration and continuous learning.

Going further, this framework review, accompanied by a new approach to employee empowerment, is the only way out of the current lacklustre in AI block building. Vakili suggested a shift from an experimentation-focused approach to one driven by imagination, truly leveraging the power of generative AI. Organisations need to release the constraints of legacy systems (whatever their nature) to unlock this imaginative potential. This echoes a remark made by Bain & Co during the IADS AI Retreat in Berlin last June: while they acknowledged that AI had a disruptive potential for retail, they also mentioned that, so far, all the use cases looked like the same from one retailer to another, suggesting that, due to a certain mindset, innovation capabilities were hitting a glass ceiling in all companies. Vakili concluded by stating that, from her Global Head of Innovation perspective, a radical change of business model was needed to unlock new opportunities in innovation.

What AI really changes

Timing is paramount, but identifying the right people to educate too!

Professor David Beatty from the University of Toronto was very clear on how AI was seen in North America, not just as transformative but as an existential imperative for businesses: "In the United States, we regard AI...not as transformative, but as an extinction event. If you don't get started on this as a business, you're dead.” Failure to embrace AI could result in rapid obsolescence.

He also made the very interesting statement that AI was already reshaping industries at an unprecedented pace, but this was not visible in mainstream business press. This point was echoed by Rainer Zahradnik, Country Head Switzerland at Tata Consulting Services, who highlighted the "hidden revolution" of AI, where its most successful applications are often invisible to the end-user. He cited examples such as energy optimisation in Formula E cars and compliance software for banks. He also emphasised the potential of AI to push boundaries, using the example of designing a new air plane landing gear with minimal human intervention. He noted, "It's almost a hidden revolution of AI. Nobody knows that in your American car there's software that's optimising it."

Beatty was very vocal about the hurdles potentially preventing legacy companies from embracing AI:

• The average age of directors is 68 at the board level. Walmart only has 3 directors under 40. In the US, 41% of board directors are more than 70. However, this does not prevent boards from pressurising CEOs to move forward with AI; on the contrary, they are more active than CEOs. For instance, Marriott inked a deal with Alibaba only after significant pressure from the board of directors on the CEO, Anthony Capuano. CEOs have been resisting the change due to the necessity of ensuring “business as usual” was keeping the right pace. To overcome this, Beatty mentioned that an increasing number of companies were considering independent incubators, fostering innovation separate from established structures.
• Regulation also impacts the level of innovation. Beatty contrasted the relatively light regulatory environment in the US with the more stringent regulations in Europe, suggesting that the latter might stifle innovation. However, the panellists agreed that this could not be the only reason: routine and bureaucracy are also major obstacles to AI adoption in large organisations, with a strong tendency to reinvest in existing processes. Also, for Beatty and Zahradnik, Europe's risk-averse approach stifles innovation, a major source of concern at a moment when US, China and India are moving forward.

The leadership responsibility and its needed evolution in decision-making

Beatty called for a clearer understanding of everyone’s rules: the role of any growing organisation is to create procedures helping to normalise operations, while their CEO’s role is to have a clear enough mind to be able to see what is coming and might disrupt the business if no appropriate course change is taken (AI in this case).

He also urged board directors to engage with AI actively, emphasising the need for directors with relevant skill sets to help and advise CEOs. He recommended a phased approach, starting with educating the chair, then the full board, and finally the management team. Having said that, the extensive use of AI at the management level, especially to help the decision-making process, also calls for a mindset reset if leaders want to remain honest and transparent.

Matthis Bitton, a Ph.D student at Harvard University, had a fascinating exchange with Liesje Meijknecht, partner at McKinsey, on that topic. They both reminded the audience that while AI is a tool to manage complexity (which has been, in the past, traditionally outsourced to partners such as SAP or Salesforce), it is, in essence, trained on sets of data that are not neutral, objective or even fair.

From that perspective, using AI to prioritise decisions implies the acceptance that the criterium of trust does not matter at all: AI does not have any ethics and is not able to. Instead, they raised the fact that AI should be used in fields where it surpasses humans much more, such as big data, mathematics, testing. In the decision-making field, AI raises more issues than what it solves, not to mention that the more it is used, the less transparent it becomes. Bitton and Meijknecht pondered over the dangers of over-regulation (which raises the question of knowing if algorithms should be more scrutinised than humans and if yes, why) and it's contrary, i.e. granting too much power to Silicon Valley.

All in all, the panel concluded, AI creates a moral dilemma, i.e. a choice where both options are problematic. Given that AI is unavoidable, the only way for leaders to make their way through it is to define what kind of pair of “ethical glasses” they want to wear and make sure they use them. Interestingly, that also led to the conclusion that this was the opportunity for businesses and academic institutions to focus again on human sciences rather than hard sciences and data. Mattis mentioned that the Harvard Business School had not hired a single philosopher in 20 years time. It is rather ironic that AI finally pushes us into becoming more human.

How questions about AI end up reviewing the old way of seeing the world

Artificial Intelligence raises questions that go beyond it, as it actually forces us to challenge some of the visions that have shaped the business world for the past years.

Rethinking the role of offices

The expansion of remote collaborative work that was favoured during the pandemic is now ending, with many companies asking their teams to return to their office (this applies especially to knowledge workers). Giampiero Petriglieri, from INSEAD, raised the topic when discussing the fact of “humanising” the workplace by mentioning that remote working was also a trap for junior profiles, who were growing with more limited access to experience than when in the office with their co-workers. He qualified the online meeting as being “a constant reminder of each other’s absence”.

This created some exchanges between practitioners: Pierre Le Manh, President and CEO of PMI Project Management Institute, described PMI's fully remote model, highlighting the benefits of increased access to a broader talent pool and reduced environmental impact. He emphasised the importance of intentional, meaningful in-person interactions rather than forcing a daily return to the office.

In contrast, Liz Cane, VP People at Palo Alto Networks, described Palo Alto Networks' approach, which encourages a return to the office for certain roles, particularly those involving early career development, R&D, and collaboration. She highlighted the importance of in-person interaction for fostering relationships, creativity, and innovation.

The discussion concluded with a call for a collaborative design process to determine the optimal work arrangement for each organisation, considering its specific needs and goals. In other words, the topic is not so much about coming or not coming to the office but adapting physical presence according to the projects and issues currently being solved.

Redefining success

With AI allowing the phenomenon of “one-person unicorns”, the size of organisations does not matter anymore, as previously said. Going further, Julie Teigland from EY argued that this also called for a redefinition of how we measure and assess success: it might not be measurable in market shares anymore. For her, “big is no longer beautiful”, as illustrated by Tesla, which is not the largest EV manufacturer in the world (BYD produces twice as much), but generates unprecedented levels of loyalty ,or companies such as Dyson or Patagonia, all seen as market leaders in spite of them not being the largest players. She argues that large operators are under cost pressure to keep the leading position, while being smaller and more efficient, a feature allowed with the generalisation of AI, allows to be more agile.

Keiishiro Nishi, Senior VP and head of CEO office at Fujitsu, provided an interesting example of this when he mentioned that Fujitsu, a tech company, willingly decided to close its PC business and halve its revenue to launch new higher-margin businesses.

Kill “zombie ideas”

A full session was dedicated to “zombie ideas”, defined as “good old recipes” that have resisted the test of time for the wrong reasons, as they appeal to an apparent common sense that is unproven. Now that AI allows a data-driven approach, such zombie ideas should be eliminated (even though human instinct and nuanced interpretation should be kept in the loop). Michele Zanini, co-founder of the Management Lab, Tammy Erickson, Leadership Advisor at the LBS, Lenka Pincot, Chief of Staff to the CEO at PMI , and Robin Speculand, CEO of Bridges Consultancy, together reviewed the following ideas:

• “More control leads to better performance”: overemphasising standardisation, rules, and control stifles adaptation, innovation, and responsiveness to local conditions. Zanini highlighted the example of SAP riddled with 500 KPIs, demonstrating how over-standardisation can cripple a company. He advocated for mutual accountability, norms and principles over measurement.
• “Top-down changes work”: engineered, top-down change initiatives often fail due to insulation of leadership, leading to incremental or overly risky changes. Zanini advocated for syndicating responsibility for change more broadly.
• “Leadership is positional” (i.e., experience and wisdom are correlated with rank): Equating leadership with organisational rank discourages initiative and talent development outside the executive level. Zanini argued for recognising leadership competencies regardless of position.
• “Planning is everything”: sticking to rigid strategic plans in a volatile environment limits agility and responsiveness. Pincot emphasised the need for "anti-fragility" and adaptability, citing the example of athletes training for a race with obstacles. Erickson cautioned against excessive planning, which can hinder flexibility and lock organisations into outdated trajectories.
• “Strategy first, corporate culture second”: Speculand questioned the continued emphasis on strategy over culture, referencing Peter Drucker's observation that "culture eats strategy for breakfast."
• Sticking to outdated management concepts: Speculand criticised the reliance on out dated management models and frameworks, comparing it to using Windows 95 in the modern era.
• Consider that full-time employment is ideal and what all workers are looking for: Erickson suggested that work is increasingly chosen based on marketability and human asset value development rather than solely on compensation. She argued against paying based on hours worked, advocating for payment based on tasks, outcomes, and value creation. She also emphasised the need to treat employees as volunteers, recognising their autonomy and choice.

Such a conversation is not only theoretical: Speculand shared the example of DBS Bank, which successfully addressed the "zombie idea" of unproductive meetings through a structured approach, saving significant employee hours. Here, also, the panel was adamant that AI had the potential of both perpetuating and slaying zombie ideas. It concluded by emphasising the importance of thoughtful prompting and avoiding a "tyranny of data."

The 16th edition of the Drucker Forum highlighted how AI acts as a catalyst and a pretext for fundamental organisational transformations, extending far beyond technological innovation, including in the analogue world.

While AI offers unprecedented opportunities for imagination, creativity, and operational efficiency, it also underscores the importance of retaining human-centric approaches to foster innovation and adaptability. AI has an amplification effect that allows to challenge and review processes taken for granted for decades, as mentioned by Amy Edmondson, professor at the Harvard Business School. She explained that AI allowed businesses and individuals to fail more often, and take “smart risks”. AI ushered in the age of “intelligent failures” (different from “preventable failures” to avoid), which should be celebrated by “learn-it-all” teams willing to learn from every experience and learning opportunities.

As the discussions at the Forum emphasised, success in this evolving landscape will depend on adelicate balance between harnessing AI's potential and reinforcing the human values that underpin sustainable and innovative workplaces. Ultimately, redefining the role of knowledge work in an AI-driven world offers an unparalleled opportunity to shape a future that is not only more efficient but also deeply human.

Credits: IADS (Selvane Mohandas du Ménil )