Integrating Generative AI to Amplify Human Potential in the Workplace

For this edition of The Regenerative Edge I sought an experts’ understanding of the transformative power of AI, and reached out to my colleague Dr Sean Gallagher who was delighted to co-author this comprehensive and insightful article with me.

More than a new tech buzzword

Despite their brief history, computers and so-called ‘artificial intelligence’ (AI) have radically changed what we see, what we know, what we do and how we do it.? Not much matters more for ourselves and our planet than how this history continues to play out in the next decade.

How rapidly the world has changed becomes clear when we consider that until recently computational power doubled about every 20 months in line with Moore’s Law, but during the past 10 years this has sped up to a doubling time of about 6 months. ?This is an astonishingly fast rate of growth, and we are likely unaware of just how deeply computers and AI have permeated every aspect of our lives.

If the exponential infiltration of Generative AI (GenAI) into business and society is a game-changer, in much the same way as the democratisation of the internet was in the nineties, what does this mean for your business?? Will it create or threaten value?? Will we be able to step up and take full advantage or do our structure, systems, processes and culture hold us back??

As we stand on the threshold of a transformative era in work, GenAI isn’t just a buzzword; it's the new reality of productivity and creativity in the workplace. ?With GenAI, we aren’t merely observing an incremental change; we’re witnessing a paradigm shift that challenges the very fabric of our work culture and strategy.

Right now, not many organisations can claim success with scaled implementations of AI to deliver business value. ?In this edition of The Regenerative Edge we explore what boards, and the c-suite can do to harness the positive potential for GenAI to build more creative capable, effective, transparent, sustainable, ethical and ultimately more valuable organisations.

And it starts by understanding that the competitive advantage fueled by GenAI isn’t the tech – it will be your people.

Throw out the manual

GenAI is breaking the mold of traditional AI. ?Leaders often express a keen interest in adopting GenAI for their business, only to realise their fundamental misconception. ?GenAI is not the narrow, predictive AI that many leaders mistake it for; it's a generative powerhouse.

Unlike its predecessors, GenAI has the capacity to innovate, create, and enhance. ?It’s an AI that doesn’t just solve problems but helps us discover new solutions and create pathways we hadn’t even considered.

In a recent podcast[i] on YouTube, Brad Lightcap, COO of OpenAI the company which developed ChatGPT, lamented about how executives view GenAI. ?He says, “enterprises have a very natural desire to want to throw the technology into a business process with the pure intent of driving a very quantifiable ROI, [their tactic is] ‘I want to take AI and throw it at a very specific process in supply chain management and cut 20% of my spend’.”

Lightcap says while the technology can help with a specific challenge, he thinks leaders, “criminally underrate” how much return you really get by just providing people access to the technology. ?

You can’t quite quantify an exact ROI from GenAI.

He gives the example of a task that previously took a worker two days to complete but now takes two minutes and frees that worker up to do a heap of other more valuable work. ?This doesn’t show up in how we traditionally think about ROI as an enterprise as it doesn’t relate to a budget line item, but it has an enormous cumulative effect.

There is no instruction manual for GenAI.? The technology is not deterministic, meaning it can give different responses every time to the same prompt. ?And it can make mistakes. ?But it is exceptionally powerful. ?Unlike any previous business technology, GenAI makes humans smarter and allows them to do better work.? More and more studies[ii] show that when used by knowledge workers, those working with GenAI complete more tasks, complete them more quickly, and deliver much higher quality results than those workers not using the technology. ?To realise these productivity benefits requires frequent use.

Imagine having the ability to double your team without increasing overheads – that's GenAI. ?But before we get ahead of ourselves, it’s essential to start where it counts. ?Integrating GenAI in the mundane, everyday tasks can empower your workforce from the ground up. ?And it’s the right low-risk approach. ?In doing so, an intuitive understanding of GenAI can be developed, enabling every employee to harness its power across all levels of work.

Can we finally solve the productivity challenge?

The UK[iii], Australia[iv] and many other western nations are facing a productivity crisis and have been for some time. ?Can AI help us tackle the productivity challenge and propel our companies and economy forward - 85% of global CEOs surveyed[v] seem to think so.? Recent research by Gallup[vi] into the effect of advanced digital transformation suggests AI benefits individuals, organisations and economies that take advantage of its productivity benefits and value-creating opportunities:

• Advanced digital skills boost worker income and raise global GDP by an estimated $6.3 trillion annually;
• Companies with high levels of digital skill utilisation report annual revenues that are close to 170% higher than companies that do not use digital skills (after adjusting for the number of employees, country and sector effects);
• Almost three quarters of organisations that employ advanced digital workers introduced a new product in the past two years, compared to less than half of organisations that employ basic digital workers; and
• Employees with advanced digital skills have high job satisfaction (scoring at least 8 out of 10) – almost three quarters compared to under half those with intermediate or basic digital skills.

What might this look like in the office of 2025 or 2026?? Firstly it might finally mean unchaining ourselves from our laptops because mundane tasks that take up to 70% of our time today might be automated by generative AI and associated technologies[vii].? It might also free up more time for overworked, stressed-out employees with estimates that generative AI could automate or enhance 40% of working hours, even more in the software, finance, insurance, and capital markets industries[viii].?

One way of looking at this is through a reductive lens - more can be done with less.?

Another way of looking at this potential is to see employees as having much more of their time available for value-creating activities, assuming companies can develop and harness these capabilities and the associated employee motivation.?

Studies have consistently shown that when employees have more time generally (such as not having to commute every day), they tend to give half back to their employer and take half personally.? It’s therefore not unreasonable to assume that if people are assisted at work by AI they will have more time to devote to important priorities that require higher-order capabilities, more time to invest in learning and skill development, more time to invest in their health and wellbeing and more time recharging outside work which means they are more likely to be creative, collaborative, constructive and committed at work.?

All up this seems more likely to be a win-win rather than a win lose situation for employees and for business.

Collaborative intelligence is the new worker capability

It’s not all good news though.? Gen-AI is expected to drive business transformation in the next 4-5 years by increasing not only the amount of information and data processing tasks performed by machines, but more significantly for knowledge workers, many of the reasoning and decision-making tasks we have assumed only we can do effectively[ix].?

This is the stuff of headlines predicting mass redundancies in accounting, law, property, software engineering and similar professions.? The debate concerning the degree to which AI will be complementary rather than substitutional in different industries will be with us for a while, but for all the transformational change in business, leaders expect AI to be a net creator of jobs over the next 5 years (25.6% net positive effect)[x].?

But inevitably some people will be left behind as priorities for future workforce capabilities shift rapidly.? Organisations identify skills gaps and an inability to attract talent as the key barriers preventing industry transformation, with 60% of surveyed companies highlighting the difficulty in bridging skills gaps locally and 53% identifying their inability to attract talent as the main barriers to transforming their business[xi]?

According to the World Economic Forum, the top skills expected to increase in importance and priority for workforce development between now and 2027 are creative thinking, analytical thinking, technological literacy, curiosity and lifelong learning, resilience, flexibility and agility, systems thinking, motivation and self-awareness, talent management, service orientation, leadership and social influence, empathy, and active listening[xii].?

When viewed as core capabilities and attributes, roughly two-thirds of these attributes are classed as cognitive skills, knowledge, and abilities and a significant one third being attitudinal (self-efficacy, working with others, ethics).? As has been predicted by WEF and others for the past decade, the self-efficacy capabilities (socio-emotional attitudes) which businesses consider to be growing in importance most quickly are evidence that businesses emphasise the importance of resilient and reflective workers embracing a culture of lifelong learning to adapt to disrupted workplaces and who are able to solve complex problems with others and with GenAI.?

The crux of the challenge lies not in AI specific expertise but in 'collaborative intelligence.' ?It's about nurturing a synergy between human and AI, where GenAI becomes integral part of work, like a highly capable virtual co-worker. ?It’s more than just commanding the AI to do a task; it's about understanding how GenAI elevates and complements a worker’s capabilities, filling gaps, and bolstering their strengths.

Underlying these skills and attitudes is a core ability to communicate? - with colleagues and with Gen AI.? Conor Grennan of NYU makes the point [xiii]that GenAI is fuelled by natural language prompts, not coding or tech skills. ?So the ability to communicate and clearly articulate a business need or work task through effective prompts and intentional terminology is crucial.

Integrate rather than implement

We must acknowledge the fluid nature of GenAI; it's an ever-evolving entity. ?Brad Lightcap urges leaders to see this technology as unlike any technology they have adopted previously.? “A lot of companies think the tech is static, who think that GPT-4 is the best the technology will get, but they are not prepared or aware of how steep the change will be for AI and what the next wave of the technology [holds].”

Organisations need to adopt a nimble stance and be as adaptive and dynamic as the technology itself.? And the way to do this is through having a GenAI-enabled workforce. ?

By cultivating a collective consciousness around AI, GenAI augments the team’s capabilities.? This approach not only ensures teams continue to leverage the evolving capabilities of GenAI but also allows workstyles and culture to evolve alongside the technology. ?

In research led by Sean[xiv] that measured how more than 2,000 Australian workers were using GenAI within their work, three key workplace settings were identified as essential in supporting a GenAI-enabled workforce: policy, training, and culture.? This should come as no surprise to any leader who’s implemented organisational transformation or a major change project.? Yet this study found less than one in four Australian companies had either a formal policy or formal training program in place.?

Formal training is a strong indicator supporting frequent use of GenAI at work, especially through firsthand experiential learning and not simply show and tell. ?For instance, analysis for a major financial and insurance company found their workers struggled to uptake GenAI without policy, training and cultural settings in place.

The guiding objective leaders is to integrate rather than implement GenAI: ‘every worker, every task, every time and in every team.’

This approach serves two purposes.? Firstly, that AI is not a tool that we only reach for to help with certain tasks but with every task.? And secondly, creating a team culture where everyone shares experiences and compares results amplifies everyone’s learning.

One terrific suggestion we’ve seen is to make AI an active participant in team meetings, problem-solving sessions, and creative brainstorming sessions. ?Well beyond transcribing and summarising the discussion, the AI participates by providing real-time analysis of a problem and suggestions for a way forward, prompting different perspectives and discussion of alternatives.

The gap is less about tech, more about culture

We only need to read the weekly business headlines to comprehend the gap between where leaders believe we need to be, and where the workforce is now.? This is not intended to be an indictment on employee capability – organisations need to recognise that you can’t put a square plug in a round hole and should commit to making long overdue changes that truly align structure, culture, processes and expectations with desired workforce capabilities, attitudes, and value-generating activities.?

Let's take a cue from a global engineering design firm considering how it needs to recalibrate its business model from labour-centric design solutions and strategies to service-dominant models, leveraging GenAI. ?It's a glimpse into a broader shift in the means to value-creation, where billable hours give way to bespoke solutions, and where GenAI operates at the heart of an innovative work culture and collective learning environment.

This means re-engineering business models around value creation and cascading this through management coaching, cultural change, distribution of control and elevation of trust and its twin, accountability.? This is likely a bigger ask of organisations - especially major corporations – than the ask of workers!

The potential of AI challenges the status quo in many organisations because as we explained earlier, generative AI will empower employees for example by democratising access to problem-solving tools, reducing time spent on unproductive and boring tasks and by using data to make time-sensitive decisions, especially decisions that have direct customer impact.? Companies already see a “demand pull’ from employees keen to experiment with AI, hinting at the early stages of a more ‘self-service’ and entrepreneurial era within organisations.

The risk of democratisation in organisations will be threatening for many leaders and technology department, who traditionally have relied on control of access to information and resources to restrict autonomy and decision-making. ?This style of management remains prevalent in Asia in global and local organisations, where risk aversion and cultural factors such as fear of failure and fear of being ‘wrong’ need to be confronted by companies seeking to drive AI adoption in the workforce. ?Highly structured and tightly controlled organisations don’t encourage risk-taking or speaking up[xv], and as a result may take a longer time to adapt to and use new initiatives.?

This is no time for leaders to use their shield of authority to avoid or delay conversations about how to open up the ecosystem to allow experimentation and adaptation with AI as a partner.

What does this demand of leaders and boards??

As previous editions of The Regenerative Edge have discussed, a key trait of successful leaders in this decade is an ability to tolerate uncertainty and complexity, even (or especially) as AI becomes increasingly able to provide more insightful analysis of big data and predictive capacity.? Good CEOs will need critical thinking and perspective-seeking to navigate computer-generated opinions, because even with the most sophisticated modelling of accurate, robust data a forecast is still essentially a well-informed opinion.? ?

This is not going to be an easy path for leaders who are still grappling with making strategic decisions and navigating the post-covid environment – leaders need their boards and especially their chairmen to work constructively and proactively with them through this journey.?

As a board director or CEO-advisor or mentor we must recognise that our executives are unlikely to possess all the capabilities and judgement needed.? Be supportive, ask questions and be clear that there is a shared responsibility for organisational strategy.?

Do not expect your leaders to be superheroes and if needed encourage them to seek individual coaching to work on areas that are challenging for them and let them know the board does not see this as a sign of failure or weakness but as an indicator of self-awareness and a desire to grow.?

The board will also need to deal with governance challenges posed by AI that are likely to exceed the scope of existing data governance frameworks in their organisation[xvi]. ?As is evident in the WEF future of work research, ethical and critical thinking are increasingly essential capabilities and nowhere is this more important than around the board table.? While ensuring ethics are part of AI conversations and distributing responsibility for security and privacy through the organisation will help manage AI-associated governance risks, ignorance or excessive trust in AI is a very real concern for experienced but non-expert directors.?

Both boards and executives will need to become comfortable with providing more direction and priority setting but exercising less direct control of the people who report to them – individuals and teams must be given more responsibility and guidance to make decisions that affect their performance individually and collectively.? This requires a cultural, managerial, and performance-management shift to imbue roles with more autonomy and accountability - but not to the extent that it paralyses action for fear of being wrong. ?A gradual approach to educating, encouraging and rewarding ‘good’ decision—making (even if the outcome isn’t what was expected) that is aligned with the value-creation model and with organisational priorities is the best way to implement such sweeping yet necessary reforms.

Leaders who realise that AI’s impact reaches well-beyond technology and process understand that the Future of Work is less about hard skills and increasingly about subtler cognitive and self-efficacy skills, and attitudes relating to self-management and working with others.? These leaders are already asking where the gaps are, how they are going to be reduced (for example through increased workforce diversity, targeted recruitment, training and coaching, incentives alignment etc.) and how long this will take.?

Have you established and properly resourced a plan of action to maximise the opportunities and manage the risks of AI for the next year, 3 and 5 years?

Leadership lessons - it’s a path not a project

We all know organisational change initiatives have a terrible track record of success, and digital transformation efforts are no exception to this.? It’s critical that boards and top leaders accept AI as a critical propellant towards a business that is ready to make the most of the challenges and opportunities of 4IR and a VUCA environment.? Unfortunately transformation failures often stem from leadership issues[xvii], so understanding the most common reasons for failure (in no order or impact or importance) and learning from them can help boards and the c-suite lay the foundations for sustainable strategic transition.

1. Narrow, technology-focused vision

A common reason organisational (and especially digital) transformation fails is due to a lack of clear vision and purpose, which along with planning is the foundation for success. ?Without a clear understanding of what GenAI transformation could look like across the organisation, it’s easy for companies to get lost in the weeds or focus on a specific problem to “fix.” ?

Board and executive (including the CIO) mindsets need to shift and embrace a much broader concept of GenAI’s potential - unlike any previous business technology, GenAI makes humans smarter and allows them to do better work.? It’s not a technology tool, it’s a workforce transformation enabler.?

We know of companies that have set up working groups to integrate GenAI into their organisations which include the COO, CTO and subject matter experts – but not the CHRO. ?We believe this is a fundamental mistake.

Invest the time and energy with key stakeholders from across the organisation to establish a shared vision with ambitious goals.? Develop a realistic roadmap and RACI for achieving outcome-based milestones for a defined timeframe (for example a year), and create new outcome milestones for the next period, and then the one after that, incrementally building on progress, learnings and new opportunities over time.

2. Short-term or inflexible thinking, lack of agility

Digital transformation is a long-term process and GenAI is going to continue to evolve faster than organisations and people, so a short-term mindset can derail the entire effort. ?Likewise seeing GenAI as a technology implementation project to solve a specific problem is likely to result in failure to integrate with existing systems and processes and result in a wasted investment in ‘fixes’ that don’t create long term value. ?

Leaders need to be patient and have a long-term, open perspective and be willing to adjust throughout the process to keep the ultimate goals on track. ?Digital transformation requires agility and flexibility and implementing GenAI is not a one-time event but a continuous evolutionary and adaptive process - failing to improve and iterate can result in outdated technology and processes that fall short of evolving business needs and employee expectations.

3. Insufficient budget, time or governance framework

Digital transformation can be expensive, and boards and executive teams that do not allocate enough budget and time to the initiative may struggle to succeed. The pressure to do too much too quickly can come from the marketplace (keeping up with competitors) or from pressure to deliver a speedy ROI on the budget to validate the investment decision.? Rushing through a change increases the risk of mistakes and reduces your ability to respond to changing circumstances and AI improvements. ?Plus moving too fast can quickly burn out both leaders and cause change fatigue to permeate the organisation.

Adequate resourcing is more than just time, budget and people, it’s also important to ensure robust data management processes are in place to collect, store, and analyse data effectively.? Ensuring data quality, privacy, and security is essential considering the exponential ability of AI to process and incorporate all information it comes across, regardless of quality.

Be realistic about the costs of digital transformation and allocate sufficient human and financial capital to achieve your goals. Enthusiasm for new initiatives can quickly fade when it takes too long, or gains aren’t perceived as “big enough. ?This may mean creating new internal roles, reallocating priorities or putting some projects on the backburner to give people time to learn, experiment, refine, report and share.? Communicate and report on short- and mid-term targets to help your team stay committed without moving too quickly.

4. Forgetting the best ideas are often generated at the coalface

As we’ve discussed, while an evolutionary approach to strategy rather than the typical top-down approach probably makes you squirm, allow employees to experiment with AI and work out how it can best help them - within clearly articulated guidelines, responsibilities and clarity on governance and compliance.? Your employees know their frustrations and most of them probably want to spend more time doing interesting work that is seen and rewarded, and less time doing mundane pointless tasks (if they don’t, you have bigger issues than AI implementation!). ??

It’s likely your people will find creative and unexpected ways to blend AI into their daily routines that might not be the big-bang stuff of HBR and Forbes case studies, but which will actually make a material difference to their productivity and satisfaction.? It won’t hurt employee engagement either!?

This approach has substantial flow-on effects; studies show that giving employees a say on where digitisation should be adopted, or establishing practices related to working in new ways such as continuous learning or flexible work practices as part of their change efforts is 40% more likely to result in a successful transformation[xviii].

5. Failing to acknowledge and deal with fear

Any organisational change can be threatening (or at the very least uncomfortable) but combined with headlines about AI destroying millions of jobs is a recipe for fear translating into passive or active resistance, fear of experimentation, fear of speaking up, fear of failure. ?

Clearly articulating and reinforcing through actions as well as words the ‘why’ of GenAI integration is critical. ?What are your organisation’s values and principles that guide the decision to adopt GenAI? ?What’s in it for your employees??

Reimagination of business processes sits at the core of digital and AI-related transformation, and so, by definition, it challenges the status quo, throwing we-have-always-done-it-this-way sentiment out of the window. ?Every step of the way provides an opportunity for employees to resist new technologies or processes, which can derail even the most well planned and implemented AI strategies.

A study by McKinsey found that companies that prioritised cultural factors in digital transformations were four times more likely to succeed than those that focused on technology alone[xix]. ?Culturally organisations need to encourage and support regular GenAI application in everyday tasks, enable teams with AI, and foster a collaborative environment for innovation among peers.

Be up front that AI integration requires a different mindset, as well as an agile, ready-to-experiment workforce that knows they have permission to try things out, test, tweak and share without fear of criticism or promotional impact.

6. Treating transformation as a project instead of “BAU”

Most transformation efforts are structured as discrete programs, separate from the day-to-day operations of the business —with a clear beginning and end and a separate programme management office.? Although that model may have made sense when most business transformations were transitory or had a clearly defined scope, it’s not well suited to deliver major change in today’s highly dynamic environment, and less so given the pace of AI’s development.

Corporates have a propensity for investing huge sums in custom-built AI solutions, but because of the exponential rate of development existing off-the-shelf frontier models like GPT-4, Gemini 1.5 Pro and Claude 3 Opus are the best performers. ?Bloomberg spent $10 million on finetuning a financial GPT model based on ChatGPT 3.5 to get a competitive advantage. ?The result? ?The new publicly available GPT-4 performs much better at USD $20 per month.

Embrace GenAI as part of business as usual and encourage a hands-on learning experience for workers with large language models (LLMs). ?Allowing employees to engage with these AI tools in their daily work not only builds their proficiency but sets your organisation up to get the best out of enterprise solutions once they become more sophisticated.

Slowly integrating AI into the operational fabric of the organisation takes time and requires everyone to be involved.? A broad but incremental, prioritised approach is likely to build and maintain momentum without overloading individuals or departments – or blowing the budget on a redundant ‘solution.’?

What are you waiting for?

Shifting to GenAI doesn’t just recalibrate what productivity means; it redefines it.

GenAI is not a tech solution but a solution your people.

By offloading mundane tasks to AI, it liberates human intellect to focus on higher-value activities and pursue more creative, impactful work - turning what was once a slog into an opportunity for strategic, meaningful work.? We may yet realise the promise and productivity of true ‘knowledge work.’

Most fundamental will be increasing emphasis on what makes us different from AI – our innate human capabilities such as empathy, curiosity, and our ability to understand context and make judgements. ?How will we foster a culture of collaborative intelligence that embraces continuous learning and adaptation? Preparing for a GenAI future starts with cultivating these competencies today.

Now is the time to consider how new ways of working and creating value could disrupt or recreate business models, especially those reliant on billable hours. ?

As AI begins to automate tasks, particularly those tasks previously done by highly paid, highly educated knowledge workers, we need to look at how we deliver value through innovation, inspiration and service rather than through accumulating hours of white-collar labour.

It would have been nearly impossible to imagine a world in which production and distribution are primarily automated if you were a factory worker a century ago.? Similarly, it can be difficult for those of us who lead and govern organisations today to imagine what our industry might look like in just ten years.? What new needs, desires and jobs will be created by GenAI, what will be the next ‘smartphone’ breakthrough that sends ripples through business and society for decades?[xx]? The potential seems limitless, and the time to review our business strategies and business models is now.

Boards and executives don’t need to have accurate foresight or uncanny predictive ability to successfully harness AI and evolve ways of working to thrive in the digital age.?

What is needed is openness to new ideas and new ways of doing things, awareness that you don’t (and can’t) know everything, acceptance that there will be failures, deviations, casualties and U-turns throughout the process, and commitment to evolution with AI as the ‘new normal’ for the organisation.

[i] 20VC with Harry Stebbings, Sam Altman & Brad Lightcap: Which Companies Will Be Steamrolled by OpenAI? | E1140, https://www.youtube.com/watch?v=G8T1O81W96Y

[ii] National Bureau of Economic Research, Generative AI at Work, Erik Brynjolfsson, Danielle Li & Lindsey R. Raymond, https://ssrn.com/abstract=4573321 https://www.nber.org/papers/w31161

[iii] Be the Business, Productive Business Index, Edition Six Q1 2023 states that during the 20th century, UK productivity growth sat steadily around 2.2% a year, however, from the mid-2000s this has deteriorated less than 1%, with headline figures masking a trend of deep and sustained regional disparity.

[iv] The Australian Productivity Commission found that Australia’s labour productivity fell by 3.7 per cent in the 2022–23 fiscal year, after the biggest slump in productivity growth in 60 years during the period 2010-20.

[v] WEF, Future of Jobs Report 2020, 2020

[vi] Gallup, AWS Global Digital Skills Study: The economic benefits of a tech-savvy workforce, 2022

[vii] McKinsey & Company, “The economic potential of generative AI”, June 14, 2023, https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier#/

[viii] Accenture, “Gen AI LLM—A new era of generative AI for everyone,” April 17, 2023, https://www.accenture.com/content/dam/accenture/final/accenture-com/document/Accenture-A-New-Era-of-Generative-AI-for-Everyone.pdf

[ix] WEF Future of Jobs Report 2023, 2023

[x] LinkedIn for the Future of Jobs Report 2023

[xi] World Economic Forum, Future of Jobs Survey 2023.

[xii] World Economic Forum, Future of Jobs Survey 2023.

[xiii] https://www.ai-mindset.ai/ai-mindset-newsletter/issue-1-nlbwm-ycp8w

[xiv] Gallagher, S., Hopkins, J., Jones, S. et al., GenAI and the Future of Human Work, in print.

[xv] James R. Detert and Amy C. Edmondson, Why Employees Are Afraid to Speak, Harvard Business Review, May 2007, https://hbr.org/2007/05/why-employees-are-afraid-to-speak

[xvi] MIT Technology Review, The great acceleration: CIO perspectives on generative AI, 2023.

[xvii] Forbes, Why Change Management Really Fails: 4 Shocking Causes And How To Fix Them by Mark Murphy, Oct 26, 2022, https://www.forbes.com/sites/markmurphy/2022/10/26/why-change-management-really-fails-4-shocking-causes-and-how-to-fix-them/?sh=1308f79958cc

[xviii] McKinsey Digital, Culture for a Digital age, by Julie Goran, Laura LaBerge, and Ramesh Srinivasan, July 20 2017, https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/culture-for-a-digital-age

[xix] Ibid.

[xx] World Economic Forum, “Why there will be plenty of jobs in the future - even with AI”? https://www.weforum.org/agenda/2024/02/artificial-intelligence-ai-jobs-future/