By Luca Collina, Mostafa Sayyadi and Michael Provitera  

The article starts by looking at the roots of corporate social innovation, which come from ideas like corporate social responsibility and social innovation. It then shows how these ideas are connected but also different, clearly understanding what each one is and how corporate social innovation works. Finally, the best leadership practices for corporate social innovation capability in the AI age are presented.  

It is widely agreed that corporate social responsibility has been the fundamental aspect of defining companies’ commitments towards society, considering the support of business activities. ^{[1] [2]} The literature has also tried to determine what actions could be taken and link them between business results’ expectations and positive effects on social problems. Research from 2011, started introducing the innovation aspect in the forms i.e., service and green innovation. So as Chu et al. (2022) say, achieving success through innovation and considering the opportunities and connections with social and environmental needs. ^[3] Through social innovation and corporate social innovation, it internally clarifies (values and culture) and externalizes (reputation and corporate brand) the company’s commitment and values. There are four different perspectives from the company: 

• The Functionalist Perspective  

This perspective produces programs, products, and services to align company interests with society’s needs. With the Shared Value Initiative, Michael Porter and Kramer (2006) tried to encourage companies to create offerings (products, prices) that could be affordable by society while creating a social benefit. ^[4] 

• The Culturalist Perspective  

Culturalists understand that both companies and societies have cultures and that companies adapt their actions to their surroundings. Companies use this perspective to reflect their cultural relationships with the community in which they operate. 

• The Sociopolitical Perspective 

The company and society are in a power relationship. Management, mediation, and action are the functions of corporate social responsibility. This power is also directed to maintain respect for the community and its components while showing commitment to helping social system characteristics be maintained. 

• The Constructive Perspective 

It is the approach that the company shows that it is socially responsible and is the first step to gaining credibility. Activating corporate social responsibility for companies indicates that they can deal with societal issues and shows that they take responsibility. This activated perspective fosters the company’s reputation. 

As for the companies’ interests in pursuing social initiatives, the following points have been highlighted: 

1. Economic and Financial Performance: corporate social responsibility should aim to increase profits.  
2. Competitive Advantage: Generate an edge against rival firms by either benefitting the company itself or stopping others from gaining similar advantages. 
3. Reputation: Aligning stakeholders’ interests with those of the company, improving its standing within society by adopting social causes as its priorities, and building up its image through engagement in society. 
4. Cost and Risk Reduction: Companies reduce costs and mitigate risks through social actions designed to minimize operational expenses and address any potential threats associated with the production and distribution of goods or services. 
5. Value Creation: the process of producing economic, social, environmental, and institutional benefits regardless of any driving force or motivations for doing so. 

The Boundaries of Social Innovation 

The growing relevance of social innovation comes from the progressive shift from a centralized point to local entities, which, with a progressive utilization of entrepreneurship and a market-focused approach, try to offer specific and tailored innovative solutions to particular issues evidencing both tangible and intangible low-resource availability. ^{[5] [6]} The research about social innovation has seen the growing interest of scholars since the 70s, and during the 90s was explicitly mentioned and received growing attention, with a changing focus, particularly in the management and business world. ^{[7] [8] [9]} 

Among the various definitions of social innovation, we need to consider why it emerged in the past decades: different public and private subjects have put it into their agendas; one concept is relevant: resolving diverse problems going through a deep societal transformation. ^{[10] [11]} We should also consider that authors have let emerge that many diverse concepts about social innovation require then a better understanding to connect social innovation with enterprise innovation. [12] Social innovation aims at providing innovative solutions that address societal problems and bring about social transformation through novel ideas such as solving society-related issues or encouraging inclusion, improving people’s well-being, or altering values, practices, or systems in society; however social innovation is considered different from those innovations with social elements. 

• Diversity of Social Innovation in Addressing Issues 

An in-depth review of various social innovation case studies underscored the wide-ranging nature of social and environmental issues that social innovation can effectively address. These case studies span sectors and geographical boundaries, offering compelling evidence of social innovation’s vast scope and versatile nature. 

• Key Aspects of Social Innovation 

Five critical aspects should underpin any comprehensive definition of social innovation. These include: 

• addressing a social need,  
• incorporating an innovative element,  
• managing the implementation,  
• inducing improvement  
• fostering relationships and collaborations.  

This multi-dimensional approach offers a more holistic and meaningful interpretation of social innovation. 

• SDGs and Social Innovation 

In an attempt to categorize the myriad forms of social innovation, we found the United Nations’ Sustainable Development Goals (SDGs) to be a practical classification framework. The versatility and global prominence of the SDGs lend them credibility as a classification system, supported by the fact that 89% of the scrutinized case studies could be associated with one or more SDGs. 

• Actors in Social Innovation 

Through the case studies, the study dispels the common misconception that the realm of social innovation belongs exclusively to social entrepreneurs. It highlights that the development and implementation of social innovation can often be the result of efforts by a diverse spectrum of actors. This inclusive nature of social innovation helps expand its reach and potential impact by drawing from the insights and contributions of various stakeholders. 

• Social Innovation and Unmet Needs of Society  

A company’s innovations meet social needs effectively and create new social relationships and collaborations are considered social innovation. It still appears that social innovation could be misunderstood and confused either with the evolution of corporate social responsibility or understood that a big idea could solve problems. ^[13] Another aspect of social innovation could be related to innovative companies that explicitly consider social innovation and sustainability, including economic, social, and environmental elements, even if a suggestion for better implementation and management is required. ^[14] Another characteristic of the links between firms and social innovation was found by Przychodzen (2018) when they investigated the factors that characterize the link: sector, where social innovation acts as a stimulus to innovate, and high net margins and ROE promote social innovation. ^[15] 

The Boundaries of Corporate Social Innovation 

Corporate social innovation has its own identity, distinguished from other forms of innovative social support.  

• Social Entrepreneurship and Corporate Social Innovation 

Social entrepreneurship is a specific way to create social value by building an organization and business model to satisfy its mission, while corporate social innovation aims to generate both economic and social value. 

• Organizational Innovation and Corporate Social Innovation 

Organizational innovation doesn’t include involving stakeholders and doesn’t aim to generate effective change in society 

• Corporate Social Responsibility and Corporate Social Innovation 

These two definitions have a link that can be compared with vision and mission. The former represents a values declaration based on ethics overarching companies’ actions toward creating benefits for social aspects. Corporate social innovation works on specific projects to deliver social value. Our attention will be on corporate social innovation with the assumption that a vision that aims to satisfy unmet social needs (social innovation scope) and corporate social responsibility is accepted as the critical point of the social vision. 

The Best Leadership Practices for Corporate Social Innovation Capability 

The best leadership practices for developing corporate social innovation capability within organizations are what companies should consider using a model to embed business and social innovation. It emphasizes the need to prioritize social empathy and shared value creation in their social innovation efforts. The importance of integrating dynamic capabilities for innovation, stakeholder engagement, and top management support is fundamental. Organizations should focus on aligning their leadership and change organizations for innovation to support social value creation. The development of a vision for corporate social innovation, along with strategies, processes, and structures that facilitate successful social innovation projects, is crucial. 

The next step for our future endeavors will be completed by the unlearning processes and connecting them with the learning ones.  

Unlearning and Learning 

The unlearning and training activities are a great way to learn through experience, and we experienced that “action learning” is the best way. Removing or, better, identifying what is not working anymore, with an effective reality check, allows new learning with experimentation. 

Action Learning “learning by doing” involves actively engaging with real-world challenges and reflecting upon them to gain new knowledge and insights. When combined, people can effectively draw from experience to address complex problems and reflect if they are applicable. They benefit from supportive peers who offer new perspectives to explore emerging issues through novel inquiries and probes.  

We also present a view of the approaches with and without AI and Chatbots: 

• AI and Chatbots 

AI-powered Decision Support System (DSS) It effectively supports unlearning outside real-life decision-making scenarios. We can design specifically to learn how to provide a safe space for employees to unlearn old habits and learn new ones.  

Unlearning Process (With AI and Chatbots). The same approach is used with DSS, only different in the use of technology. 

• Without AI and Chatbots 

Scenario Planning and Future-back Thinking. It involves envisioning possible futures and working backwards to let trainees use their skills to identify the skills, knowledge, and behaviors needed in those scenarios.  

Negative Learning. It is a powerful tool to challenge pre-existing beliefs and assumptions, facilitating unlearning and opening the door to new learning. This is particularly effective in extreme cases where existing behaviors or mindsets may harm existing or future leaders, particularly in negative situations. 

About the Authors 

Luca Collina is a transformational and AI Business consultant at TRANSFORAGE TCA LTD. York St John University awarded him the Business – Postgraduate Programme Prize and CMCE (Centre for Management Consulting Excellence-UK) for his paper in Technology and Consulting Research Prize. Author/External Collaborator of CMCE. 

Mostafa Sayyadi works with senior business leaders to effectively develop innovation in companies, and helps companies—from start-ups to the Fortune 100—succeed by improving the effectiveness of their leaders.  

Michael J. Provitera is an Associate Professor at Barry University. He is an author of Level Up Leadership published by Business Expert Press.  

References 

1. Pless, N.M., Sengupta, A., Wheeler, M.A. & Makk, T. (2022). Responsible Leadership and the Reflective CEO: Resolving Stakeholder Conflict by Imagining What Could Be Done. Journal of Business Ethics 180(1), 313–337. https://doi.org/10.1007/s10551-021-04865-6 
2. Fietz, B., Hillmann, J. & Guenther, E. (2021). Cultural Effects on Organizational Resilience: Evidence from the NAFTA Region. Schmalenbach Journal of Business Research, 73, 5–46. https://doi.org/10.1007/s41471-021-00106-8 
3. Fietz, B. & Günther, E. (2021). Changing Organizational Culture to Establish Sustainability. Controlling & Management Review, 65, 32–40. https://doi.org/10.1007/s12176-021-0379-4 
4. Bhattacharya, C.B., Sen, S. & Edinger-Schons, L.M. (2023). Corporate Purpose and Employee Sustainability Behaviors. Journal of Business Ethics, 183, 963–981. https://doi.org/10.1007/s10551-022-05090-5 
5. Fischer, M. et al. (2023). Corporate Sustainability. In: Sustainable Business. SpringerBriefs in Business. Springer, Cham. https://doi.org/10.1007/978-3-031-25397-3_4 
6. Rojot, J. (2017). Corporate Social Responsibility and Culture. In: Azoury, N. (eds) Business and Society in the Middle East. Palgrave Studies in Governance, Leadership and Responsibility. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-48857-8_8 
7. Beschorner, T. & Hajduk, T. (2017). Responsible Practices are Culturally Embedded: Theoretical Considerations on Industry-Specific Corporate Social Responsibility. Journal of Business Ethics 143(4), 635–642. https://doi.org/10.1007/s10551-016-3405-2 
8. Kejžar, A., Dimovski, V. & Colnar, S. (2022). Social Innovation from the Perspective of Quality of Life of Old People and in Long-Term Care. In: Baikady, R., Sajid, S., Przeperski, J., Nadesan, V., Islam, M.R., Gao, J. (eds) The Palgrave Handbook of Global Social Problems. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-68127-2_12-1 
9. Bataglin, J. & Kruglianskas, I. (2022). Social Innovation: Field Analysis and Gaps for Future Research. Sustainability 14, 1153. https://doi.org/10.3390/su14031153 
10. Portales, L. (2019). Social Innovation: Origins, Definitions, and Main Elements. In: Social Innovation and Social Entrepreneurship. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-13456-3_1 
11. Todd, J. (2005). Social Transformation, Collective Categories, and Identity Change. Theory and Society, 34(4), 429–463. http://www.jstor.org/stable/4501731 
12. Slee, B., Burlando, C., Pisani, E., Secco, L. & Polman, N. (2021). Social innovation: a preliminary exploration of a contested concept. Local Environment, 26, 791 – 807. https://doi.org/10.1080/13549839.2021.1933404 
13. Hristov, I. & Chirico, A. (2023). The cultural dimension as a key value driver of sustainable development at a strategic level: an integrated five-dimensional approach. Environment, Development and Sustainability 25(2), 7011–7028. https://doi.org/10.1007/s10668-022-02345-z 
14. Piccarozzi, M. (2017).  Does Social Innovation Contribute to Sustainability? The Case of Italian Innovative Start-Ups. Sustainability, 9(12), 1-28. https://doi.org/10.3390/su9122376 
15. Przychodzen, W. A.P.J. (2018). Sustainable innovations in the corporate sector–The empirical evidence from IBEX 35 firms. Journal of Cleaner Production, 172(2), 3557-3566. https://doi.org/10.1016/j.jclepro.2017.05.087 

By Luca Collina MBA

Drawing parallels between the initial exuberance for Electric Vehicles (EVs) and Artificial Intelligence (AI), this paper argues for paying heed to learning from the adoption of EVs. It underscores the need for strategic foresight, ethics, and robust digital infrastructures following the ‘learn to walk before running’ manner that AI should take to be sustainable yet transformative.

“The two most powerful warriors are patience and time.” — Leo Tolstoy.

Two of the greatest things that will make great changes in our lives and work and help our planet are Electric Cars and AI. Both will bring about a great revolution with new technology, changing our world in a way that impacts how problems get solved and how businesses, among other areas, grow well. But, just to underscore how innovation does help shape the future, look at Electric Vehicles (EVs): they are leading the most sustainable way of mobility and are a perfect example of using consciousness to advance in technology. 

The Paths of Electric Cars and AI  

Significant technological progressions revolve around AI and electric cars. The initial wave of EVs generated excitement among the public, and with this enthusiasm for cars, many anticipated a swift adoption by the public. However, this expectation did not materialise as quickly as initially thought. It took a considerable amount of time before individuals started buying EVs. The public’s acceptance of cars did not align with sparkling projections. AI serves as a technology that replicates human actions on computers. It encompasses roles such as customer service representatives, virtual assistants and personalised recommendations. AI systems are driven by the increased access to data.

Electric Vehicles (EVs): Pioneering Sustainable Transportation

Electric Vehicles (EVs) have emerged as leaders in revolutionising 21st-century transportation, notably reducing environmental impacts. Modern EVs, with advanced technology, ensure efficient commuting experiences and significantly contribute to cleaner air by lowering greenhouse emissions ^[1]. However, challenges such as the high initial costs and environmental concerns over battery life and disposal persist.^[2] Yet, the expanding charging infrastructure highlights a move towards overcoming these hurdles. While we go through the change with EVs, a similar shift is happening with AI that impacts our lifestyle and business operations.

Artificial Intelligence (AI): Transforming the Fabric of Society

AI is reshaping our world in more ways than one, if not literally. A new development of such advanced technology is bound to drastically change our way of living. AI, mainly through intelligent assistants and automation, will just keep taking off huge chunks of our daily interactivity and jobs, making everything easier. It will improve essential fields, such as healthcare, with the help of people staying healthy. AI will give a chance to new opportunities by taking routine jobs so that humans can focus on creative and strategic pursuits^[3]. AI will power the service to make it more efficient and personalised by providing all services, hence meeting our needs and wants.^[4]

AI is an astonishing innovation that society needs to use efficiently to unleash human potential. Now, It becomes imperative to examine the lessons learned from the journey of Electric Vehicles (EVs).

What are the lessons learned from EVs for AI?

This brings us to the interweaving journey of Electric Vehicles (EVs) and Artificial Intelligence (AI) Competence, with findings and strategic recommendations arising from compelling narratives. The EV journey has been storying and has critical milestones and challenges to share learning that can deftly be applied to the domain of AI to chalk a roadmap that is both progressive and pragmatic.

• Advanced infrastructures and reliable networks

The charging systems of electric vehicles are being designed and made in such a way that they become convenient and accessible for people. This has been done so people are encouraged to use electric vehicles more frequently and find them convenient. The lesson of electric cars is to build all the systems and structures that would allow an electric vehicle system to spread and work at a large scale.^[5]

However, it might even slow growth or make it difficult to do business if systems were not put in place, such as the charging infrastructure for electric car systems that followed later. 

We should understand that investment in the digital core systems for AI can make it part of everyday running businesses. If the digital system is rightly put in place, then artificial intelligence will be incorporated and commonly used.^[6]

• Investments and availability gap

The other typical area would be in the cost discussion; one level above the debate on costs would be the discussion of long-term value^[7]. The former underlines their value in the long run, and the latter develops strategies that could bring those financial barriers down to the accessibility and attractiveness of technologies in the larger sense.[8] The other examples of closing the investment gaps for AI are the growing availability of small language models, cloud service, and off-the-shelf and data-as-a-service solutions ^[9].

• Consumers /Stakeholders’ trust, ethics, and regulatory

Electric vehicles and artificial intelligence both have complex challenges to deal with. For electric cars, some of the early concerns were about how far they could go on one charge and not having enough places to charge them. ^[10]

For artificial intelligence, some of the main concerns are about ethics, privacy, and people possibly losing their jobs but also getting better capabilities and skills. Like electric vehicles, it is imperative to be open and honest with people, teach them about artificial intelligence, and show them the benefits ^[11]. But again, it will also require working closely with groups like the government that make rules to ensure innovations with artificial intelligence do not happen faster than considering the ethical issues^[12] and ensuring employees, people involved, and society as a whole entity .

Learning from the challenge and associated strategies within EV adoption, AI companies can arm themselves with a repertoire of tools to help them begin to traverse the complexity of landscapes to drive technological innovation ^[13]with strategic foresight and ethical consideration, making the implementation impactful.

Conclusion: Hype vs Effective Adoption

There is the last point that regroups the above connections explained: Hype vs effective adoption. An opinion writer ( myself) would say, ‘Many people are now as excited about AI as they were in the beginning with Evs. However, business leaders need to learn that AI, like electric cars, initially had practical issues solving problems that slowed consumers from using them.’ In terms of integrating AI, leaders should come up with realistic plans with enough time and money to do things right^[14]. That would surely help in the sustainable benefits of AI among stakeholders rather than fizzing out when the hype has died down. If corporations have to use AI to their advantage, there are no shortcuts.

Companies that would like to adopt AI must learn from the slow transition time to popularise electric cars. Moving too fast with new technologies often comes with unexpected problems ^[15]. Instead, careful methodologies should be applied to design AI to solve business problems, not just the flashy technology innovations that don’t help much ^[16].

Bringing innovations like AI means that companies must learn to walk before they can run.

Figure 1 – Parallel analysis of EVs and AI adoption paths

The main point is that though promising, AI needs careful management, which did not take place fully for EVs’. Promising innovations like AI take time, massive shifts that change many things. Realistic expectations and patience are the keywords, with progressive evolution from meeting immediate problems and opportunities while creating building blocks of benefits and performances^[17] for the future using AI systems. Moving carefully and tactfully is better than rushing forward without properly thinking it through.

About the Author

Luca Collina is a management and transformational consultant who has managed transformational projects and Automation internationally (Tunisia, China, Malaysia, Russia). He now helps companies understand how GEN-AI technology impacts business, use technology wisely, and avoid problems. He has an MBA in Consulting, has received academic awards for his research, and is a published author. Thinkers360 named him one of the Top Voices, Globally and in EMEA in 2023. Luca continuously upgrades his knowledge with experience and research to transfer it. He ecently developed interactive courses on “AI & Business” and “Human Centric AI”. 

References

1. Hawkins, T., Singh, B., Majeau‐Bettez, G., & Strømman, A., 2013. Comparative Environmental Life Cycle Assessment of Conventional and Electric Vehicles. Journal of Industrial Ecology, 17.
2. Cox, B., Mutel, C., Bauer, C., Beltran, A., & Vuuren, D., 2018. Uncertain Environmental Footprint of Current and Future Battery Electric Vehicles.. Environmental science & technology, 52 8, pp. 4989-4995 .
3. Lakhani, K.( 2023) “AI Won’t Replace Humans — But Humans With AI Will Replace Humans Without AI” HBR
4. Danaher, J., 2018. Toward an Ethics of AI Assistants: an Initial Framework. Philosophy & Technology, 31, pp. 629-653.
5. Straka, M., Falco, P., Ferruzzi, G., Proto, D., Poel, G., Khormali, S., & Buzna, L., 2020. Predicting Popularity of Electric Vehicle Charging Infrastructure in Urban Context. IEEE Access, 8, pp. 11315-11327.
6. Chatterjee, S., Chaudhuri, R., Vrontis, D., Thrassou, A., & Ghosh, S., 2021. Adoption of artificial intelligence-integrated CRM systems in agile organizations in India.
7. Rao, A. ,2021– Solving AI’s ROI problem. It’s not that easy.–com
8. Enholm, I., Papagiannidis, E., Mikalef, P., & Krogstie, J., 2021. Artificial Intelligence and Business Value: a Literature Review. Information Systems Frontiers, 24, pp. 1709-1734.
9. Alshamaila, Y., Papagiannidis, S., & Li, F., 2013. Cloud computing adoption by SMEs in the northeast of England: A multi-perspective framework. J. Enterp. Inf. Manag., 26, pp. 250-275.
10. Aduama, P., Al‐Sumaiti, A., & Al-Hosani, K., 2023. Electric Vehicle Charging Infrastructure and Energy Resources: A Review. Energies.
11. Ahmad, S., Rahmat, M., Mubarik, M., Alam, M., & Hyder, S., 2021. Artificial Intelligence and Its Role in Education. Sustainability.
12. Brendel, A., Mirbabaie, M., Lembcke, T., & Hofeditz, L., 2021. Ethical Management of Artificial Sustainability
13. Li, X., Wu, T., Zhang, H., & Yang, D., 2022. Digital Technology Adoption and Sustainable Development Performance of Strategic Emerging Industries. Journal of Organizational and End User Computing.
14. Chatterjee, S., Chaudhuri, R., Vrontis, D., Thrassou, A., & Ghosh, S., 2021. Adoption of artificial intelligence-integrated CRM systems in agile organisations in India. Technological Forecasting and Social Change.
15. Demirkesen, S., & Tezel, A., 2021. Investigating major challenges for industry 4.0 adoption among construction companies. Engineering, Construction, and Architectural Management.
16. Leesakul, N., Oostveen, A., Eimontaite, I., Wilson, M., & Hyde, R., 2022. Workplace 4.0: Exploring the Implications of Technology Adoption in Digital Manufacturing on a Sustainable Sustainability..
17. Tse, T. , Esposito, M., Goh,D. , Lee, P., 2024 – Why Adopting GenAI Is So Difficult-HBR

By Luca Collina & Ben Warnes

AI has still to earn its spurs as a reliable tool in the locker of business. So what guidelines are available to executives to aid in determining the appropriate level of trust to apply towards AI systems? To date, different jurisdictions’ regulatory frameworks vary in their approach.

As artificial intelligence becomes more embedded across sectors, governments respond with new regulations to manage risks while enabling innovation. However, complex and fragmented regulatory approaches could undermine trust in AI among key decision-makers. This analysis compares emerging AI governance laws and regulations in the EU, US, and UK, specifically examining their potential impact on trust for executives, managers, and workers adopting AI systems.

The EU’s AI Act categorises risks and sets rules to protect rights while enabling innovation. The US has an AI Bill of Rights and order for safe AI, but no comprehensive laws yet. The UK takes a pro-innovation approach with guidelines for responsible AI use overseen by existing regulators.

Building Trust in AI

EU: The EU Act promotes accountability and transparency in AI. This can help executives trust AI more through audits to check processes. Managers have duties around monitoring systems to ensure progress and compliance. Restrictions on problematic AI protect workers while allowing innovation, although some uses could still undermine rights.

US: Over 90 per cent of AI executives say that AI improves their decision-making confidence, but others lag. Academic research shows that ethics shape trust in AI. Companies would use AI more with guidelines for fairness, explainability, and privacy. However, common values across industries do not yet exist.

UK: In the UK, the rules want companies to feel good about using AI if it’s open on how it works and is fair. However, there are lots of complicated regulations between industries, which is confusing. This may stop executives from adopting AI. There are worries about the economic impact, too. Pros and cons mean boosting or preventing AI trust for executives.

A deeper analysis and interpretation of the different laws and regulations among the countries and continents needs to focus on the pros and cons of executives’ trust in AI for adopting it.

Regulations can build leaders’ trust in AI, but balancing risks and progress has trade-offs.

The EU prioritises responsibility through limited innovation. The US enables unfettered development yet breeds uncertainty. The UK is confused, with complex, sparse oversight. Striking the optimal equilibrium to sustain trust requires measured governance for principled AI expansion, not drastic swings between overbearing restrictions versus little accountability, which deter adoption. As priorities diverge locally, executives must weigh their context amidst competing aims. Steps upholding ethical standards, welfare, and technological advancement stand the best chance of motivating cross-regional public investment and leadership buy-in.

Regulations through elements related to trust and further actions expected by executives.

Why are further actions expected from the EU’s managers?

EU executives must take additional actions to build trust in AI beyond regulations due to two considerations:

The EU AI Act establishes accountability and restrictions to manage risks. However, achieving genuine adoption and confidence from executives requires further cultural leadership and commitment to ethical AI.

While regulations provide an oversight framework, progress depends on executives driving change through active capability building, risk management, and internal governance. Going beyond rules to instil ethical AI across operations builds authentic trust and acceleration.

A piece of recent positive news about Biden’s AI Bill of Rights is related to the initial pilot applications from the research done by NAIRR (National Artificial Intelligence Research Resource) to make AI tools and resources more available, secure, compatible, and accessible for everyone to learn.¹ This can be seen as an additional support for management.

Why are further actions expected from US managers?

Top executives already trust AI to improve decisions. The problem is with the employees’ trust. Most staff lack confidence in the technology’s fairness and transparency. Without shared ethical guardrails in place across sectors, uncertainty persists.

Managers must translate high-level AI principles into understandable workplace policies and training. Openly addressing concerns about bias and job loss rather than ignoring them expands trust in AI. Cross-industry collaboration to align core values, cementing transparency and accountability, can give employees confidence that AI will be applied ethically.

The UK’s rules aim to make companies confident in using AI by promoting transparency, accountability, and other trust-building principles. However, regulatory complexity across sectors could reduce this confidence. There are also concerns about economic impacts. On the other hand, the generative AI framework for HM Government,² even if directed to the public sector, supplies an additional point of reference for supporting businesses in topics related to the adoption and implementation from make or buy, to ethics, data and privacy.

Why are further actions expected from UK managers?

The UK wants people to feel good about using artificial intelligence by being open about how it works and ensuring that it’s fair. But lots of complicated rules between industries are confusing. This could make executives afraid to adopt AI. While the goals are good, too much red tape and most rewards going to companies that use AI early on may slow things down.

Thus, managers need to ask for simpler regulations in their field. Checking how the technology will impact workers and being honest about it counters fears. And taking the lead to spread AI’s money-saving powers more evenly brings everyone along. Removing obstacles this way helps get wholehearted buy-in across British businesses.

We discussed some of the new rules that different governments make for using artificial intelligence responsibly. These rules help make company leaders feel OK about putting the technology to use. However, while regulations set intentions, putting principles into practice presents challenges. Having explored high-level policy impacts, we now transition to additional considerations for responsible AI adoption.

Beyond regional differences, what are the common executive actions to include in an AI systems strategy?

Company leaders can directly act beyond laws to make staff and stakeholders trust AI more. Key steps include policies for ethics, training on AI, evaluating job impacts, and talking to officials about rules. If leaders visibly care about people through these concrete moves, it shows inside and outside the company that AI helps more than it harms.

• Set organisational policies and culture focused on ethical AI – Leaders spelling out guidelines for fair AI in a company rulebook proves to all that they want self-checking rules, not just fast money.
• Provide extensive training and internal guidance resources on AI – Leaders paying for their whole company to take classes on AI risks shows that they worry that AI could harm people by accident without learning.
• Proactively evaluate workforce impacts and address employee concerns – Leaders studying how AI changes jobs, with care for staff livelihoods, makes employees feel that higher-ups don’t just coldly ignore their welfare for profits.
• Collaborate with regulators and other stakeholders on sensible ground – Leaders respectfully meeting with them about practical new AI laws builds public trust more than ignoring worries.

Leaders put visible effort into the steps above to show that they walk the walk. Rules that are shown to be genuine build trust. Staff knowing that leaders invest to avoid AI harm means a lot, too. Evaluating work changes with heart gains loyalty when profits still matter, too. And fair rules forged together end suspicion. These concrete moves towards responsibility make the public and employees cheer executives forward on using AI to aid lives.

EU policymakers could further compel internal audits on existing models while requiring accountability for automated decisions. US leaders might demand rapid course-correction abilities if systems display unfair performance deviations post-deployment. UK governance could authorise external ethical inspectors to halt dubious projects based on mounting evidence.

Collaborate across sectors to align on AI best practices guided by shared values. Where specific regulations emerge, view compliance as a floor rather than a ceiling for responsible innovation.

When leaders show care for impacts on people rather than uncaring profits, staff and society reward the care by trusting leaders’ judgement on using AI moving forward. So responsible foundations laid with care then open doors to confident progress. Leaders get trust once trust is earned, boosting their confidence in AI systems.

“Earn trust, earn trust, earn trust. Then you can worry about the rest.” ³

Responsible AI relies on earning trust through ethical practices and inclusive governance. Organisational adoption hinges on leadership approaches within companies as well.

How executives steer emerging technologies proves critical for stakeholder confidence. The specific leadership styles implemented around AI strategy internally shape acceptance across levels.

Examining common models aids constructive analysis regarding systemic trust factors.

Leadership Essentials for Successful AI Adoption

Every workplace’s AI journey will differ. What are the key areas for leaders?

• Share the compelling vision.
• Enable ongoing learning.
• Role-modelling values-based behaviour.
• Empower accountable decisions.

Aligning AI to Vision

“Greatness is not a function of circumstance. Greatness is largely a matter of conscious choice.” ⁴

Keep aims bold but diligently embed ethics. Prioritise infrastructure and controls over aggressive AI deployment. Track quantifiable indicators – business returns, algorithmic bias, data quality, and stakeholder sentiment. Rapid innovations should align with unchanging ethics like fairness and transparency. Major achievements often come through small, steady efforts, such as adding community reps to boards and iterative data tweaks.

Foster Learning Culture and Psychological Safety

“Team learning happens when team members suspend assumptions and enter into genuine thinking together.” ⁵

Frame AI as an ongoing learning journey with all staff playing roles that uphold ethics and minimise harm. Welcome diverse voices in decisions, supportively respond to challenges, and enable collaborative troubleshooting without blame. Model openness about incomplete AI knowledge. Task cross-functional groups to identify risk controls; support experimenting with audits and oversight. Provide resources for employees to share literacy skills and stay updated.

Leading Ethically

“A leader knows the way, goes the way, and shows the way.” ⁶

Admit limitations in AI literacy and seek multi-disciplinary input to enable debate. Proactively surface potential biases, risks, or inequities in AI systems. Assess sociotechnical challenges; conduct impact redress when necessary, slowing rapid deployment. Prioritise ethics and people over trends or quick wins. Build connections to gather community perspectives on appropriate AI uses – accountability spans boundaries.

Enable Staff for Governance

“The best executive is the one who has sense enough to pick good men to do what he wants done, and self-restraint to keep from meddling with them while they do it.” ⁷

Emphasise staff governance roles. Share challenges openly, not only policies. Allow individuals to be AI-fluent and involve stakeholders in co-creating policies to distribute, then responsibility. Provide team authority to halt questionable deployments via accessible feedback systems enabling agile responses. Sustained, transparent communication and inclusive decisions distribute capabilities across an empowered workforce to uphold ethics.

Conclusions

As new rules come out to oversee AI, leaders must take steps to allow people to trust the technology. Laws alone don’t build trust; company cultures matter, too. If leaders show they care about being responsible with AI, it helps staff and stakeholders gain confidence.

Rules for AI differ by country, but some worries and hopes are the same everywhere – like wanting AI to be fair, looking out for workers, and still seeing progress. Leaders across borders can work together to make balanced guidelines.

Inside companies, leaders need to:

• Share an inspiring vision for using AI.
• Encourage ongoing learning about AI.
• Set a good example of values with AI.
• Give staff the power to question AI systems.

There is still a lot to figure out on the best ways to balance new AI and accountability. Moving forward calls for balanced new rules, not drastic swings back and forth. As laws take shape, leaders must weigh trade-offs but stick to ethics. With care and wisdom, AI can be trusted. 

About the Authors

Luca Collina’s background is as a management consultant in Italy in supply chain and manufacturing. In the UK, since 2012, he has managed transformational projects also at international level (Tunisia, China, Malaysia, Russia). He now helps companies understand how GEN-AI technology impacts business, use technology wisely, and avoid problems. He has an MBA in Consulting, has received academic awards for his research, and is a published author. Thinkers360 named him one of the Top Voices, Globally and EMEA in 2023. Luca continuously upgrades his knowledge with experience and research to transfer it. He recently developed interactive courses on “AI & Business” and “Human Centric with AI”.

After earning a postgraduate degree in marketing in 1995, Ben Warnes worked in corporate communications before co-founding an NYC design and build firm in 1999. Returning to the UK, he worked as a consultant in communications for various blue-chip companies. Moving into construction, he created his design and built a high-end construction company. He attained MCIOB status in the construction industry, managing over £150 million in construction projects. Ben now runs a property investment fund. In 2020, Ben founded LMA Coaching 2020 to pursue his passion, drawing on 25+ years of experience and insights. He specialises in helping leaders foster purpose-driven cultures centred on psychological safety and intrinsic motivation. Ben holds an MBA in Leadership Management. His research on remote work guides organisations in flexible models.

References

1. Joe Biden’s big AI science project gets pledges from Microsoft, Nvidia, and others; https://www.theverge.com/2024/1/24/24049467/national-science-foundation-ai-research-biden-eo
2. https://assets.publishing.service.gov.uk/media/65a806bf94c997000daeb98e/6.8558_CO_Generative_AI_Framework_Report_v7_WEB.pdf
3. Seth Godin – https://seths.blog/2014/02/the-most-important-question/ 
4. Jim Collins – Good to Great
5. Peter Senge – The Fifth Discipline: The Art and Practice of the Learning Organization
6. John C. Maxwell
7. Theodore Roosevelt