6 Reasons Boards Must Integrate the AI Future of Work into Strategy

AI is here. ?Improved performance and adaptive agility are not.? Why?

It’s increasingly obvious that sustained outperformance needs more than cost-cutting strategies - the way people and technology combine to generate value must also be transformed. ?

The rapid advancement of AI technologies

Artificial intelligence (AI) is rapidly progressing and revolutionising how we live and work. ?The term "AI" covers techniques like machine learning and knowledge-based methods, application areas like computer vision, natural language processing, speech recognition, intelligent decision support, and intelligent robotics, and their specific uses in various fields. ?

There is no clear red line as to what is or isn’t AI versus standard algorithmic processing power - instead it can be seen as a continuum of features characterising what we think of as artificial intelligence and where the “magic” happens:

An AI system is a machine-based system that, for explicit or implicit objectives, infers from the input it receives how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. ?Different AI systems vary in their levels of autonomy and adaptiveness after deployment.

The impact of AI is pervasive, influencing various aspects of our daily lives, from communications, transportation, media, shopping and organising our week. ?Industries ranging from healthcare, education, finance, professional services, logistics, manufacturing and construction, agriculture and retail are witnessing intense transformations driven by AI technologies. ?Knowledge and service workers across domains have had to adapt their workflows to accommodate this new AI-driven reality.

AI's potential to transform business

AI's transformative impact is expected to contribute $15.7 trillion to the global economy by 2030, highlighting its potential across various industries.

AI is a crucial facilitator of organisational transformation, enabling businesses to automate processes, gain insights from data, identify patterns to make informed decisions and create new value propositions to gain a competitive advantage. ?Healthcare, finance, retail, and supply chain management are already benefiting from AI's ability to enhance diagnostic accuracy, detect fraud, streamline logistics and generally optimise operations.

By automating previously manual tasks, AI reduces costs, improves efficiency, and in theory allows employees to focus on higher-value activities. ?Companies that effectively leverage AI often gain a competitive edge by offering innovative, cost-effective solutions and responding quickly to market changes.? It also paves the way for entirely new business models and value propositions that address emerging customer demands and create new markets. ?

BUT (and it’s a loaded "but”) ...

All this potential for transformation and exponential performance improvement is just a promise if boards are unable to foresee and strategically position their business to harness the benefits of a systemic approach to AI, one that requires deeper structural, cultural, process and performance management change.

Tellingly, productivity growth was below 1% from 2010 to 2019 and even fell in four G7 countries in 2022, suggesting that current strategies and models are not up to the job.? Tweaking at the edges and using last years’ budget less 5% for next year is lazy if not irresponsible during this period of transition from one industrial era to the next – the stakes are too high.

However, we all know this is easier said than done against a backdrop of increased global uncertainty due to new geopolitical disruptions, changes in the global economy and looming balance-sheet resets, investor activism, exponential development of AI and other technologies (and potentially rapid obsolescence), and the shift towards renewable energy. ?

Given this, it's no surprise that business leaders are unsure about how to help their companies thrive through their people, and the role AI might play in this.

For these reasons the impact of AI on business operations, and specifically its medium to long term implications for transforming how people create value for the organisation, has significant implications for business strategy.

Boards must consider the critical role that talent, culture, collaboration and creativity will play in driving productivity and strategic competitive advantage an AI-driven world.

Six ways AI is fundamentally transforming your workforce

As AI technologies advance, they are expected to create new fields of work and lead to significant expansion in others.

1. Challenging organisational structure

As AI continues to advance, the organisational landscape will witness several profound transformations:

Role redefinition: Traditional job roles are already evolving and will increasingly give way to positions focused on managing and optimising data and AI systems. ?Data scientists, AI ethicists, data-informed change managers, and automation managers will become integral parts of the workforce, necessitating a revision of roles, responsibilities and group structures.

Flatter hierarchy: organisational structures will further flatten as AI encourages cross-functional collaboration and decentralised decision-making - enabling quicker responses to changing market conditions.

New types of influence and authority: AI is also ushering in a culture of informed data-driven decision-making. ?Tools like Prompta enable executives to reevaluate reporting lines and management roles – the people who understand data insights will be increasingly empowered to influence strategic direction.

Adaptive leadership: Leaders and people managers at all levels will need to be well-versed in AI's potential, limitations, and impacts on ways of working to enable them to make more informed decisions. ?This requires an adaptive approach to leadership focused on continuous learning and change agility.

Recommendations for Directors: Organisational structures often get in the way of strategy execution, blurring roles and responsibilities, slowing decisions, skewing priorities and motivations and complicating cross-functional collaboration.? Twentieth century structures are likely to create an even bigger obstacle to timely strategy execution in an AI-powered future.

2. Reskilling, upskilling and continuous learning

Reskilling and upskilling employees will be crucial as AI transforms the workforce. ?The average half-life of skills is now less than five years, and in some tech fields, it's as low as two and a half years - continuous learning will become vital. ?Leaders will need change management, training, and capabilities development support to adjust to new ways of leading and supporting their people. ?Organisations must invest in reskilling initiatives to improve competitive advantage by identifying internal and market talent gaps instrumental to achieving the board’s strategic objectives - before they become critical.

Recommendations for Directors: A key strategic risk for boards to consider is the predicted skills and capability gap which can derail the implementation of strategic initiatives.

3. Augmenting versus replacing people

The question of whether AI will replace human workers assumes that AI and humans have the same qualities and abilities, which we know is definitely not the case. ?The future of intelligent (knowledge) work lies in the combination of AI and human intelligence, often referred to as "augmented intelligence" (AI3), where AI and humans work in tandem.?

New roles and opportunities will emerge but the adoption process will be messy, and organisational strategy, structure and change appetite will determine the extent to which this is leveraged as a catalyst for reinventing processes and teams to elevate performance, or a low-risk, cost focused implant. ?

This future will demand a workforce with a hybrid skill set, combining technical expertise with future of work qualities like creativity, emotional intelligence, and critical thinking. ?Organisations will need to work on culture and EVP to accommodate this diverse talent pool and team mix.

Recommendations for Directors: Strategic alignment of organisational strategy, future resource capability and EVP are essential to attracting and motivating a highly capable, AI-augmented workforce who can not only implement strategy but assist in recalibrating, responding and repositioning the organisation in a VUCA environment.

4. Demanding an experimental, innovation-led culture

Companies and institutions with a robust innovation culture tend to produce more advanced and efficient AI systems. ?Organisations that foster such a culture encourage continuous learning, creativity, and the exploration of novel approaches, which are critical for advancing AI technologies.? In fact a recent McKinsey Global Survey on digital strategy found a very strong connection between organisations with a culture of innovation and their ability to increase value through technology, especially generative AI.?

Organisations that encourage experimental approaches and cross-disciplinary collaboration are more likely to see significant breakthroughs in AI performance.? Moreover, the integration of diverse perspectives and skill sets within these innovative cultures leads to more comprehensive problem-solving capabilities, further enhancing AI outcomes.

Recommendations for Directors: ?Incumbent organisations already face innovation and speed to market challenges from diverse, nimble competitors founded on experimental, ‘fail-fast’ cultures.? Delaying or under-investing in strategic initiatives focused on cultural overhaul driven by a clear vision from the top of what a culture of innovation looks like will limit the ROI on AI investments.

5. Turbo-charging customer service roles

An AI customer experience leverages machine learning, large language models (LLMs), natural language processing (NLP) and other emerging systems with the aim of delivering faster, more efficient, more personalised and more intuitive customer experiences at scale. ?

Conversational AI in theory enables digital agents to provide natural, human-like conversations with customers anytime, although in practice a lot of these experiences have been poor, resulting in more frustrated customers and more demand to “speak to a real person”.? ?

Increasingly organisations are turning to AI not to replace human agents, but to amplify the effectiveness of human agents, in much the same way as we see AI augmenting knowledge workers in other areas of the organisation.? These “AI coaches” help agents improve support by:

• enhancing agent efficiency and productivity by responding to common queries, freeing human agents to focus on higher value tasks and complex customer issues.
• navigating complex workflows and less-likely situations for a given use case that can break traditional rule-based systems because they are based on foundation models, adapting in real time to perform the specialised tasks required to help an agent bring a process to completion.
• intelligently routing and performing ‘triage’ to analyse incoming conversations, understand customer sentiment, language, and intent, increasing routing accuracy and reducing ticket escalations and resolution times.
• improving quality assurance by targeting customer interactions where real-time coaching is needed and personalise agent learning and training to continuously improve their expertise and effectiveness.?

Recommendations for Directors: ?Leveraging AI to invest in the development of your human agents to significantly enhance their effectiveness in solving customer problems and identifying timely opportunities for product/service/process improvement may yield a better return on investment than seeing AI as a cheaper “always on” replacement for what are perceived as low-skill workers.

6. Redefining performance - enhancing resource efficiency or improving effectiveness?

While AI can technically improves efficiency by constantly pushing up team benchmarks and eliminating waste, it can overlook the circumstances under which better results are achieved. ?For example in the case of food delivery drivers in China, the AI system reduced target delivery times for everyone based on the top performers, ignoring the fact that the new times could only be met by breaking traffic rules. ?

A more strategic view of how AI can optimise resource efficiency in the context of improved effectiveness may avoid such issues and highlight improvements in employee capability that improve outcomes rather than just efficiency. When organisational success measures and employee performance management programs are not aligned, organisational performance suffers.?

Recommendations for Directors: Consider the operational risks of misalignment of strategy with performance measures and motivators, and the better-quality metrics on AI productivity impacts that would flow from redefined measures of performance and value creation.

An AI future of work is not without strategic workforce risks

While the rapid advancement of AI technologies is revolutionising how we live and work, it's crucial to be mindful of the risks. ?The disparity in the capacity of firms and countries to utilise AI will likely intensify the polarization within global value chains. ?Advanced firms like Google and Amazon are more capable of leveraging AI to its full potential and incorporating more nuanced considerations of human rights and worker well-being.

Firms engaged in low value-added production segments tend to be limited by their technological capabilities and resources and employ less sophisticated AI tools that focus on enhancing efficiency, and often operate in countries that lack robust legal protections and social awareness of privacy and labour rights.

Address AI bias and fairness

Addressing bias and promoting fairness in AI poses significant challenges. ?Biases in AI can manifest in various forms, from data selection and model training to interpretation and application, reflecting and amplifying societal inequalities.

Societal and cultural biases are deeply ingrained in the data and algorithms used in AI through human prejudices, leading AI systems to propagate stereotypes. ?Data bias is one of the most prevalent forms of AI bias, originating from the data used to train AI systems - if the training data is not representative of the diversity of the real world, the AI system will likely inherit these biases.

The consequences of biased AI systems can exacerbate social inequalities, such as filtering out qualified candidates from job opportunities due to their gender, race, or background.? Any processing of personal data using AI that leads to unjust discrimination between people will violate the fairness principle in data protection law that includes fair treatment and non-discrimination, balancing different interests. ?

The potential for AI systems used to recruit and monitor employees and manage their performance to entrench biases or reduce diversity in people's mindsets, experiences, and capabilities is well-documented. ?This can diminish organisational resilience and the ability to adapt to change.

Recommendations for Directors:? Transparency and explainability are crucial for building trust in AI systems, enabling stakeholders to understand how and why decisions are made.

Ensure workforce data privacy and security

Central to AI governance is the concept of data transparency and compliance, ensuring that data collection, processing, and usage adhere to regulatory requirements and ethical standards. ?AI systems' unique characteristics, such as requiring large datasets and relying on third-party frameworks and code libraries, can make compliance with data protection law's security requirements more challenging.

For example, the GDPR does not explicitly restrict AI applications but provides safeguards that may limit what organisations can do, particularly regarding lawfulness and limitations on purposes of data collection, processing, and storage. ?An effective AI governance framework plays a crucial role in facilitating better data-driven decisions by aligning AI initiatives with ethical boundaries and societal norms, ensuring that data-driven decisions are effective and socially responsible.

Recommendations for Directors:? Consider whether existing policies relating to confidentiality of workforce data may need to be clarified in the context of the use of AI, such as prohibiting submitting sensitive, confidential, or proprietary information into externally provided generative AI services.

Implement monitoring and control systems with caution and care

The integration of AI systems into higher skilled workforces at the top of the industry value chain may be less immediately noticeable or disruptive to employees on the surface, however the consequences can still be unintended or even detrimental. ?

For example, the comprehensive control and monitoring of work processes through AI potentially enables firms to utilise all sorts of data against workers, especially when disputes arise.

However most large firms - and those in highly developed and regulated economies - tend to be highly conscious of the potential legal and reputational impacts of AI misuse against employees, whether intended or not. ?Given the generally high educational background of employees and their stronger position in the job market, global corporates are understandably cautious about implementing overly intrusive control measures that might undermine the innovative, collaborative culture they need.

However, in less advanced or regulated markets the mechanism behind AI systems is often not transparent to workers – exacerbating the pre-existing information asymmetries between employers and employees, which amplifies existing power imbalances. ?While AI holds the potential to enhance work management efficiency, it often prompts companies to intensify surveillance of their employees to feed data-dependent algorithms.

An organisation where employees start to assume they are subject to mandatory surveillance and constant monitoring risks rising levels of stress-related absenteeism, lower performance and higher disengagement.

AI can also be used to enforce employee discipline, for example by flagging irregularities or inconsistencies in behaviour and then locking people out of their employee account, essentially preventing them from working.? This poses a significant legal and reputational risk as decisions made by AI can be incorrect and even controversial. ?

Recommendations for Directors:? Consider the implications of AI use in offshore and outsourced parts of the value chain that potentially open the door for AI to be used in ways that exploit workers and exacerbate conditions for an already vulnerable workforce.?

2 steps boards can take now to reduce AI-FoW strategic risk

1.? Integrate workforce considerations into AI governance

An AI governance framework provides organisations with a structured approach to navigating the ethical considerations of AI, building trust and confidence in AI technologies among users and stakeholders and ensuring that the benefits of AI are realised responsibly, equitably and aligned with societal values and norms.

Directors must review the need to develop or update the organisations’ guidelines and ethical standards for the development and use of AI in relation to your workforce - the typical lenses of accountability and risk management, ethics, transparency, bias, and fairness, are a good place to start.? Check whether relevant jurisdictions have regulations or guidelines for the responsible development and management of AI systems to ensure alignment with broader stakeholder expectations.

2. Ask these 6 questions about AI and FOW potential

A key responsibility of any executive or non-executive director is to ask questions and challenge potential consensus-driven decisions or endorsements. ?As part of your regular strategic review process, ensure AI-future of work considerations are integrated, particularly with respect to resource allocation, implementation risk and critical success criteria.? This will make it far more likely that the benefits of AI investments are substantially realised, and a competitive edge is created by amplifying workforce performance potential.

1.??????? How do we see the company in-relation to AI evolution, versus where it wants to be?

2.?????? What problems are we solving for our customers and how can AI help our people solve problems better and faster?

3.?????? Could our people identify potential customer problems or new needs before the customer even becomes aware of them?

4.?????? What might this mean for our value creation chain and the type of people we need and what we need them to excel at?

5.?????? Can AI help us recruit and onboard better, and provide a better overall employee experience?

6.?????? Can AI help us rethink performance management and development to better link job performance with valued outcomes and identify high-potential people?

A key role of the board is to assess organisational risk, which can be even more complex within the AI landscape. ?When technology is moving fast, it’s difficult to strategise with certainty, so embrace experimentation, take a holistic approach to workforce-related opportunities and risks, and remain flexible so solutions are not locked in too early.

Savvy boards understand that AI can be far more than an efficiency machine and lower-cost employee replacement.? AI presents significant strategic opportunities for amplifying your workforce contribution by transforming the way people and technology combine to generate value.?

By leveraging AI in the 6 key areas I’ve outlined, you can improve speed, agility, resilience, and competitive advantage – precisely where these capabilities are most needed in your organisation – your culture, people and work process.