AI and the future of work

This paper aims to shift the narrative surrounding artificial intelligence (AI) and what it means for the future of work. Commonly, AI is seen as a technology that will displace workers, whereas F-TAG believes artificial intelligence should be viewed as an enabling force.

To shape a desirable future, this paper emphasises the need for:

• Anticipation
• Adaptation
• Empowerment

The following is a list of some aspects of AI that can be used to tell a story of what a balanced future with AI could look like:

1. Augmented intelligence: Augmented intelligence integrates artificial intelligence (AI) with human intelligence. Rather than replacing human capabilities, the aim is to enhance or ‘augment’ our abilities. It's a concept that emphasises collaboration between humans and machines, leveraging each other’s strengths. Rather than competing, augmented intelligence is all about collaboration. Importantly, augmented intelligence unlocks new possibilities for personal growth, creativity, efficiency, and autonomy.
2. Shifting business focus: Encouraging businesses to shift their focus from outputs to outcomes, generating greater value for shareholders, customers, employees, and society.
3. Empowering workers and shaping leaders: Beyond enabling people to focus on more strategic and creative tasks, AI will also allow leadership on –– every level – to increase focus on more distributed and collaborative leadership structures and styles.
4. AI integration: Emphasising how AI will become an integral part of the work experience, liberating human workers from mundane tasks and limitations imposed by their circumstances. The potential of combining AI algorithms, machine learning models, and data-driven insights with traditional systems to achieve better outcomes at work by enhancing functionality, efficiency, and performance.

When considering the future of work, we encourage all stakeholders to engage in this dialogue and to answer the key questions at the end of the paper. Progressing one step at a time, we can shape a positive future.

Background: time to shift the narrative

During the past decade, AI has been making advancements in many areas. The recent advances in generative AI have, however, made the technology citizen-accessible. Since late 2022, these developments have led commentators to wonder what AI means for humanity’s future. Specifically, how AI might change how we define work and employment.

To help address public anxiety and add clarity, BCS advocates for positively changing the discourse and reframing how the future of work is depicted. The narrative can be shifted from negative (displacement) to positive (collaboration) by asking the right questions. Engaging in impactful dialogue is also necessary to create collaborative solutions and desirable outcomes.

For example, the workforce is losing the experience in our 50+ senior workforce at an unprecedented rate. This threatens the development of the younger workforce by removing the opportunity for, among other things, mentoring. Globally – specifically in the UK – our workforce also lacks digital skills at all levels.

Against this backdrop, we must foster a growth mindset. This means continuous learning and improvement – while harnessing AI's power to transform entry-level roles and enable career progression.

The augmented intelligence economy: powering society's advancement

The augmented intelligence economy leverages AI to enhance human potential, enable self-directed living, and facilitate continuous learning.

This economic paradigm aims to enhance existing roles and create new ones. Prioritising AI ethics and regulation to ensure transparency, fairness, and accountability are also important. Public and private sectors and a unified global response play vital roles in shaping and governing this economy^{2 11}.

• Self-directed living: AI will handle routine tasks, provide insights, and offer personalised support, thus allowing individuals more freedom and flexibility to pursue their interests, passions, and personal goals.
• Continuous learning refers to fostering an environment where individuals have ongoing opportunities to learn new skills, explore new interests, and evolve personally and professionally. AI can facilitate this by providing personalised education, real-time feedback, and access to vast resources and information, adapting to individual needs and pacing.

New business models: shifting from outputs to outcomes

New business models focusing on desired outcomes will be required in the augmented intelligence economy. We are already beginning to see a growing shift towards this, with businesses adopting outcome-based pricing models in service businesses (as opposed to time or quality-based pricing models). We are also witnessing:

• Predictive maintenance models in manufacturing, energy or transportation (as opposed to scheduled maintenance)
• Personalised recommendations that focus on the most relevant products, content or experience in retail and media (as opposed to mass marketing)

Outputs and outcomes represent two fundamentally distinct aspects of business and technology performance, especially in the context of augmented intelligence with AI. These can be defined as:

• Outputs are the direct products or services resulting from activities. They are quantifiable and easy to measure, like the number of items produced or services delivered. In AI, this could be data processed or tasks completed by an algorithm.
• Outcomes, on the other hand, refer to the broader impacts or changes. They are more about the long-term effects and the value added, such as increased customer satisfaction, market share growth, or enhanced employee productivity.

Focusing on outcomes rather than just outputs in an augmented intelligence future is significant because it aligns AI's capabilities with strategic goals and long-term value creation. This shift emphasises AI's role in completing tasks and driving meaningful change, like improving decision-making, enhancing user experience, or fostering innovation. Businesses can leverage AI to create more profound, sustainable impacts by prioritising outcomes and aligning technology with broader organisational and societal goals.

This is why public and private organisations focus on creating the right outcomes. The primary implication is that they must enable their workforce with training, optimised structures and compensation aligned to outcomes. Regulation must also be aligned to support this and guide all parties involved.

While the evolution of this type of business model has its challenges, particularly around collecting, managing, and interpreting outcomes-focused data, these challenges become obsolete with AI. AI is both the driver of this shift as well as the motivation for it: because it enables an outcome-focused business model, many organisations will adopt it into their digital evolution roadmap for this very reason.^{7 8}

• Outcome-based pricing models charge customers a percentage for reaching a business goal like increased revenue or cost savings is becoming more widely adopted as alternative pricing systems⁷.
• Automation and digitisation are changing the business world, offering potential benefits to businesses and end consumers, leading to a shift towards outcome-oriented models8.

The workers’ skills gap: endless opportunities to grow

As the constants are unpredictability and change, the focus in the augmented intelligence economy is not only on harnessing AI for productivity but also on ensuring that the benefits of AI are distributed equitably across society. This should be done considering the different levels of workers required, from entry to mid-levels and senior/expert levels. This scenario sees extensive digital education action plans to ensure more workers have basic digital skills and other complementary competencies. There is also a need to create constant growth pathways for them as the landscape of work keeps changing and evolving.^{5 14}

In addition, AI integrated into robotics, in some cases, will result in increased productivity by fulfilling some tasks better than humans and benefit humans in ways that will make them able to focus on what they are good at²¹.

• AI tools might alleviate the shortage of skilled workers by automating routine tasks and augmenting human capabilities, increasing productivity across various industries⁹.
• Demographic trends, such as baby boomers retiring and young workers not arriving in sufficient numbers, contribute to the critical shortage of skilled workers, highlighting the importance of extensive digital education and leadership development⁹.
• Vision-based cognitive load assessment has the potential to be integrated into the new generation of collaborative robotic technologies. The latter would enable human cognitive state monitoring and robot control strategy adaptation for improving human comfort, ergonomics, and trust in automation. ²¹.

The leadership gap: A new breed will be born

In the AI-driven era, a new breed of leadership is required that transcends traditional hierarchical and matrix structures, emphasising shared responsibility and collaboration across all levels. This is crucial to enable businesses to navigate uncharted territories and respond to the constant evolution of disruption. This distributed leadership model fosters fluidity and adaptability, allowing individuals with relevant skills or expertise to take charge when needed^{15 16 17}.

The new emerging leaders need to see AI as the new talent that needs to be integrated into the new workforce. The human resources discipline and talent management practices must evolve to cater to management, performance, and development for augmenting AI and human intelligence. The challenging impacts²² of AI on human behaviour will be something we will learn as we integrate more with AI.

• Distributed leadership is an approach that emphasises collaboration and shared responsibility among individuals with relevant skills or expertise, fostering a more fluid and emergent property in organisations¹⁵.
• Collaborative leadership can bridge the gap between AI and human collaboration, allowing people and technology to come together and innovate¹⁶.
• AI can enhance human skills and collaboration at work, leading to more personalised learning, improved decision-making, and better communication and project management¹⁸.
• The loss of agency theory states that using AI leads to a loss of human agency.

Radical collaborations: unlocking collective intelligence

Collaboration between the public and private sectors is vital to drive AI research, incentivise adoption, and establish common norms. Radical national, intersectional, and global collaborations, such as the EU AI Act, are essential for addressing challenges and creating a smarter, more unified society. Collective intelligence can provide unique and never-seen-before working environments, partnerships and projects that will enable us to tackle shared environmental, economic, and societal challenges. Such collaborations also offer an opportunity to measure progress towards the UN's Sustainable Development Goals (SDGs). ^{11 12 13}

• The European AI Strategy aims to make the EU a world-class hub for AI and ensure that AI is human-centric and trustworthy, fostering excellence in AI to strengthen Europe's potential to compete globally¹⁰.
• The EU's proposed AI Act represents the first global attempt to horizontally regulate artificial intelligence, with extraterritorial application and demonstration effects for policymakers, affecting the development of AI regulation globally and efforts to build international cooperation on AI¹¹.

Ethical AI, accountability, and transparent regulation: eliminating prejudice

Being intentional about ethical and legal considerations is central to the augmented intelligence economy, aiming to address unwanted biases and discrimination. Regulatory agencies are critical in ensuring transparency and fairness in AI systems and emerging business models. A unified global response is establishing common principles, frameworks, and standards23, which will not only make it easier for businesses to follow but also stop businesses that don’t follow them. AI can overcome prejudice at an individual level and within societal structures, enabling recognition based on talent and social strengths rather than intersectional biases.^{1 3 4 6 7}

• Unwanted AI bias occurs when machine learning-based data analytics systems discriminate against a particular group of people, often due to social stereotypes heavily influencing the training data used by developers¹².
• Transparency in AI has increasingly been highlighted in regulatory development, company policies, and ethical guidelines, focusing on ensuring fairness, security, transparency, and explainability in AI systems¹³.
• The UK government has proposed a new rulebook for AI innovation to boost public trust in the technology, requiring developers and users to ensure that AI is fair, secure, transparent, and explainable¹³.

5 Key questions to ignite the future we want

The future landscape of work is constantly changing. Engaging in conversations around these key questions will increase our collective intelligence and utilise our collective experience. This way, we can learn from each other and correct our course. In short, we can create the future we all desire.

For you

BCS members can read the very latest F-TAG technical briefings and reports.

We invite stakeholders to contribute insights and engage with different entities, including organisations, industries, partners, public offices, educational institutions, and individuals. These questions, formulated in consultation with expert panels and using AI models, facilitate a deeper understanding of the evolving discourse surrounding AI and the future of work. Under each key question, we have provided examples of stimulating questions that can facilitate the conversation.

1. How is work transforming?

How can AI-driven transformations in the nature of work lead to increased productivity, innovation, and collaboration for organisations?

• How can businesses leverage AI to shift their focus from outputs to outcomes, generating greater value and exciting new opportunities for all stakeholders?
• What measures can be taken to integrate AI seamlessly into the workplace, enhancing human capabilities and making work more engaging and fulfilling?
• How can AI be integrated into workplace processes and communication to revolutionise efficiency and collaboration?

2. How do education and training need to change to support the work?

How can innovative educational approaches prepare the workforce for the exciting opportunities AI presents?

• What strategies can transform job displacement due to AI and automation into opportunities for workforce growth and skill development?
• How can AI tools, theories, and topics be integrated into educational curricula to inspire and prepare students for the future workforce?
• What are employers' responsibilities for fostering education and cultivating adaptability towards digital and AI enablement?

3. How do workers' rights need to evolve?

How can AI be used to bridge societal divisions and create a more inclusive and equitable future for all?

• How can ethical, responsible and innovative AI strategy, design principles and implementation best practices enhance the work experience for all individuals?
• How can AI be harnessed to overcome biases and promote fairness and objectivity in the workplace?
• How can AI be used to ensure older workers and citizens remain valuable contributors to the workforce and society?

4. What do we need the regulation to deliver for us?

How can governments and regulatory bodies foster responsible, ethical, and transparent AI development and deployment to unlock new opportunities?

• What is the proper AI governance framework that can enable companies and governments to harness the full potential of AI while ensuring ethical development?
• How can a standard naming convention for AI systems promote transparency and trust in AI applications?
• How can registering AI research and documenting requirements contribute to responsible and innovative AI development?
• How can defining ethical boundaries in AI applications protect society while encouraging innovation?
• Where does the accountability for the use and governance of the use of AI sit within an organisation? Does this rest with the CDIO/CIO, the individuals using AI or the team who trained the AI? Or does it go wider than that to the other parties, such as HR and legal departments, for example?

5. What radical collaborations we have not considered yet?

How can new national, intersectional and global collaboration on AI challenges and opportunities lead to a more equitable, hopeful, and empowered future in the augmented intelligence economy?

• How can we prevent the potential exploitation of rural communities in developing countries by raising awareness and knowledge about the positive impact of AI?
• How can we inspire the general public about the capabilities of AI and its potential to transform lives and societies?
• How can fostering a collaborative environment that embraces AI contribute to a global response focused on creating a brighter future in the augmented intelligence economy?

Summary

Engaging in these conversations should expand our understanding of how AI is advancing and its implications for the future of work. This means exploring innovative new business models, identifying emerging skill gaps, creating opportunities for emerging new leaders, fostering new radical collaborations, and finally shaping a safer, more ethical, and more fulfilling work-life for everyone.

Methodology

The approach in this paper has been a review of existing literature on the subject of AI and the future of work, followed by insightful discussions with experts. We have also crowdsourced the most thought-provoking questions from BCS Fellows and other industry experts to form the guide for the exploration. To practice the augmented intelligence approach, we have engaged OpenAI ChatGPT and Google Bard in forming the questions, as well as analysing and summarising some of the resources we have reviewed.