The integration of Artificial Intelligence (AI) technologies is reshaping traditional workforce management practices and unlocking new avenues for optimising employee performance.

So let’s dive into the transformative impact of AI on workforce optimisation, unravelling how intelligent algorithms, data analytics, and automation are revolutionising the way businesses manage their employees.

Exploring real-world applications and success stories, we’ll explore how AI is enhancing productivity, fostering employee wellbeing, and driving organisational success…

In the knowledge economy, AI adoption is being driven at employee level

Most  adoption of AI in organisations has been driven by innovative early-adopters, individual employees keen to  automate or augment parts of their job.

These “early AI adopters” have been using generative AI tools for a while now – for example, good recruiters have been writing or improving job adverts using tools like Chat-GPT. In general, such innovation should be applauded and supported by enterprise IT teams, or enterprises will find it nearly impossible to realise the efficiency gains that futurists and analysts believe is possible in white collar roles.

Research shows that around 30% of some white-collar roles could be done by generative AI. In comparison, the  same research showed that less than 1% of the task (by time)  involved in many blue-collar jobs could be done by generative AI.

Many of the most affected white-collar roles comprise repetitive tasks – such as scheduling appointments or answering and directing calls – that could be easily replicated using AI.

In white collar spaces, we can see AI optimisation in areas such as data analytics for decision making (through data visualisation, forecasting, etc).

AI adoption should be “top-down,” rather than “bottom-up”

Many organisations in the white-collar space have allowed task-level optimisation to be done by employees – rather than by design from key decision makers. It’s a “bottom-up” approach that relies on individual ingenuity and/or diligence to drive the organisation’s adoption of AI.

It also means a large percentage of the tools that are adopted (no matter how widespread within the org), are generative-AI tools; ChatGPT being the most notable, but also tools like Luminar AI for photo editing, and TabNine for coding.

But AI isn’t just, and shouldn’t just be, about individual knowledge workers using generative tools to make their own work easier. AI can, and should, be an organisation-level tool, used to orchestrate and allocate work effectively and fairly to the most relevant people.

This would require a substantial shift in thinking within the knowledge economy. It would require more of these organisations adopting a “top-down” AI mandate – i.e. using limited algorithmic management systems and recommendation engines to support the allocation of work, and having a clear policy for its rollout, governance, and ongoing use.

The payoff could be huge – harnessing the power of AI systems to understand, match, and balance a knowledge-based workforce against ever-changing business demands, rules, and teams.

Ownership and governance of org-level AI policy: the rise of the “Chief AI Officer”

Many businesses also do not have an AI policy that’s separate from a data policy. So without anyone looking at the different ways AI can and should be used within an org, adoption of AI tools is likely to continue to be driven by individual employees, rather than being driven from a “top-down” approach.

We may see the rise of dedicated roles like “Chief AI Officer” that specialise in answering questions like this; Chief AI Officers will be charged with advocating for safe and productive AI practices.

As Daniel Hulme, who is both Satalia CEO and himself Chief AI Officer for WPP told CNBC: “The first thing for a chief AI officer is to understand technologies, and how are they best applied to solving problems, frictions, that exist across an organisation.” And according to an oft-cited Forbes article, the Chief AI Officer (or CAIO) will be expected to:

• Partner with business leaders on AI initiatives.
• Orchestrate the design, creation, testing, and deployment of AI technologies with technology leaders.
• Design, test, and deploy AI policies with legal, risk, and compliance objectives in mind.
• Measure the business and financial impact with the finance and operations teams. CAIOs must deliver quantifiable results.

The emergence of CAIOs mirrors a trend seen in the past with the rise of Chief Data Officers (CDOs) several decades ago. Just as organisations recognised the need for specialised oversight and strategic direction in managing and leveraging data assets, the growing significance of AI technologies necessitates dedicated leadership roles.

This latest evolution underscores the continuous organisational response required to effectively harness technology’s potential. As with the establishment of CDO roles, those forward-thinking organisations introducing CAIO positions signify a proactive approach towards maximising the benefits of emerging technologies while mitigating associated risks.

Those organisations have grasped the imperative for adaptability and innovation in today’s rapidly evolving business landscape. They will likely be the first to reap the benefits of org-wide AI adoption, driven by a dedicated AI champion in the CAIO role.

Organisations know their workforce problems – but aren’t aware AI could be the solution

Industries that are largely knowledge-based – like tech and professional services – have much higher rates of employee dissatisfaction, burnout, and turnover than other sectors.

One of the leading issues driving these challenges may be the systems  used to match knowledge workers to projects – which typically rely on individual skill profiles – skills they can prove from past-experience – making it hard for people to grow their careers. Even well meaning employers struggle to define how much ‘stretch’ to build into resourcing objectives.  It’s a delicate balancing act that existing systems, processes, technology, and practices have increasingly failed to manage.

While traditional analytics tools used for workforce allocation find patterns, they fail to account for millions of changing variables in the way AI can. It leads to common problems like:

Mismatched resource allocation

Employees may find themselves underutilised or overwhelmed due to workforce allocation systems and practices that fail to match their skills effectively with project demands. Inefficiency and frustration are the common results, impacting overall productivity.

Rigidity and inadaptability

White-collar organisations are commonly hindered by inflexible workforce allocation systems that struggle to adapt to evolving business needs or industry trends. This rigidity stifles innovation and agility, and may create silos that prevent teams from effectively responding to changing circumstances.

Opacity and accountability

Employees often operate within a fog of uncertainty around resource allocation decisions – most are made at local and departmental levels. Such  opaque processes lack transparency and accountability. This leads to confusion and undermines trust within the organisation.

Employee disengagement and attrition

Outdated allocation practices fail to consider employee preferences, development opportunities, and work-life balance, leading to disengagement and high turnover rates. This drains organisational morale and hampers long-term growth.

Skills stagnation and obsolescence

Organisations risk falling behind competitors when allocation systems fail to identify emerging skill requirements or provide avenues for employee upskilling. This results in a workforce ill-equipped to meet evolving challenges and opportunities.

Failure to address these challenges can lead to detrimental effects on organisational performance, including increased absenteeism, elevated staff turnover, and reduced competitiveness in the market.

And while AI is not currently being roundly embraced to solve these issues for knowledge-based work, that’s not the case in other industries…

White-collar orgs can…and should…take inspiration from the blue-collar space

In recent years, the blue-collar sector has seen a notable rise in the use of AI for work allocation, outstripping its white-collar counterparts in efficiency and innovation. This trend is particularly evident in environments such as warehouses, where AI algorithms optimise and streamline complex logistical operations.

Unlike traditional white-collar settings, where AI often assists with administrative tasks, blue-collar industries utilise AI for real-time decision-making in dynamic environments. By harnessing machine learning algorithms, these industries achieve precision in task allocation, inventory management, and resource optimisation, resulting in enhanced productivity and cost savings. As the demand for agility and adaptability continues to increase, the blue-collar space is leading the way – and where well-known organisations like Amazon are setting an example that could serve as inspiration for knowledge work…

Example case: Amazon

Amazon harnesses the power of artificial intelligence (AI) and sophisticated algorithms to improve workforce task allocation, resulting in streamlined operations and mutual benefits for the company and its employees. Within Amazon’s extensive network of fulfilment centres, AI-driven systems play a pivotal role in managing tasks such as order picking, packing, and shipping.

By employing predictive analytics for demand forecasting, Amazon can accurately anticipate future product demand, ensuring efficient resource allocation to meet customer needs. This optimised approach not only enhances operational efficiency for Amazon but also provides employees with a more structured and predictable workflow, reducing stress and uncertainty in their roles.

Moreover, Amazon’s workforce management systems dynamically assign tasks and schedule shifts based on real-time data and demand fluctuations. By considering factors such as order volumes, inventory levels, and employee availability, these systems optimise staffing levels to ensure timely order fulfilment. This leads to improved work-life balance for employees, as shifts are allocated more efficiently, allowing for greater flexibility and predictability in their schedules.

In addition to human workers, Amazon integrates robotic systems, including automated guided vehicles (AGVs) and robotic arms, within its fulfilment centres. AI algorithms coordinate the activities of these robots alongside human workers, automating repetitive tasks and enhancing overall productivity. This collaboration between humans and machines not only increases operational efficiency for Amazon but also frees up employees to focus on more engaging and value-added activities, promoting job satisfaction and professional development.

Furthermore, AI-driven performance monitoring tools track employee productivity and provide real-time feedback, enabling managers to identify areas for improvement and provide targeted support. This fosters a culture of continuous learning and development within Amazon, empowering employees to enhance their skills and performance over time.

Beyond the fulfilment centres, Amazon’s AI algorithms optimise delivery routes for its logistics network, minimising delivery times and reducing operational costs. This not only improves customer satisfaction but also reduces the workload and stress on delivery drivers, enhancing their job satisfaction and wellbeing.

Overall, Amazon’s strategic use of AI in workforce task allocation leads to improved operational efficiency, customer satisfaction, and employee wellbeing. By leveraging AI-driven insights and automation, Amazon creates a work environment that promotes productivity, flexibility, and growth for its employees, while maintaining its position as a leader in the e-commerce industry.

AI can drive employee wellbeing and work-life balance

AI-driven solutions are playing a crucial role in promoting employee wellbeing and maintaining a healthy work-life balance.

AI-powered analytics monitor employee workload, stress levels, and job satisfaction – and some businesses are now leveraging this data to:

• Implement policies that prioritise employee mental and physical health, leading to higher job satisfaction, reduced burnout, and improved retention rates.
• Ensure work is distributed fairly and optimally, and in accordance with an agreed design by senior management.
• Ensure the risks of high work volumes are understood when staffing projects or taking on new client-work. 

It is first important to understand the different impacts of AI as a tool and as a concept in the workplace. There are so many AI powered tools that are becoming increasingly available for individuals to utilise in aiding their everyday person or work-life. However, similarly to how it can help individuals, AI stands in a position where it can put an individual at risk with their professional role. As mentioned previously, AI puts many blue collar and white collar roles at risk of becoming obsolete, and this impacts people’s mental well-being as they are never sure when they are going to be impacted by the quickly changing environment.

Many AI tools leverage historical data to fuel predictive analytics, enabling early interventions that prevent the repetition of past mistakes or obstacles. This approach serves to minimise potential future challenges, overwork, and other issues that may arise. While there aren’t currently tools directly monitoring and managing employee stress levels, the use of data for predictive analytics in work management indirectly contributes to stress reduction and enhanced job satisfaction.

The focus on better work management extends beyond the operational realm. Executives are increasingly prioritising tangible results that demonstrate improved employee satisfaction through the implementation of AI-powered resources. This dual emphasis on efficient operations and enhanced employee well-being underscores the holistic impact of AI in the workplace. As organisations leverage predictive analytics to optimise workflows and anticipate challenges, they simultaneously contribute to a healthier work environment, aligning with the overarching goal of fostering job satisfaction and well-being among employees.

Although, there are programs like EAPs to help manage:

• Predictive Analytics for Early Intervention 
• Personalised Well-being programs
• Real-time monitoring and feedback
• Workload Optimisation
• Remote Work Adaptability
• Continuous Feedback loops
• Enhanced Employee Assistance Programs (EAPs)
  

  Example case: Leading global accountancy firm

  Satalia worked with a leading global accountancy firm whose consultants play a central role in the company’s ability to offer high-quality service – and build lasting relationships with clients.

  However, this means their knowledge employees are both their most important asset and their largest cost base.
  Their scheduling process was largely manual and undertaken by a full-time team of 300 resource managers..

  That’s why the client approached Satalia to solve the problem using AI. The system we developed uses AI to optimise the allocation of 4,000+ auditors to over 8,500 clients. It integrates with 20 data sources, and accounts for over 60 constraints across the business, including client demand, travel, regulations, profitability, employee skill sets, preferences and diversity.

  The immediate results included a 5% reduction in staff handovers, saving the company millions in lost productivity.

  On average, each consultant gained the equivalent of four weeks’ additional working capacity per year. And there was a 14% decrease in employee travel time; importantly, the system also ensured a much fairer approach to work allocation; consultants are better matched with clients based not only on geography, but also experience and skill level – better for clients, and better for staff.

  As Satalia’s AI solution has been integrated into the client’s core systems, it continues to deliver similar results and value on an ongoing basis.

Workforce AI can free up capable people to do more valuable and rewarding work

According to a study by the McKinsey Global Institute, up to 800 million jobs worldwide could be displaced by automation and AI by 2030. And a survey by the World Economic Forum revealed that 54% of workers are concerned about losing their jobs due to automation. This projection underscores the importance of preparing for the impact of AI on the workforce through reskilling, upskilling, and policies that support job transition and creation.

In the knowledge economy, AI can pave the way for the repositioning of these roles towards more meaningful and rewarding responsibilities. The adoption of automation tools can make people more productive in their role, and give them time to focus on human connections and needs more fully. For example, many banks and financial institutions use chatbots that can answer a high percentage of customer service questions. This frees up agents to spend proper time on edge cases that require human expertise and a personal touch.

This shift might not only benefit enterprises by enhancing overall efficiency but could offer employees the opportunity to upskill and explore roles that alight with their evolving capabilities. For example, in routine data entry roles, where the repetitive nature of tasks might be susceptible to automation, the adoption of AI-driven data entry tools can free up human workers from mundane, time-consuming tasks. This opens the door for them to redirect their efforts towards tasks that require creativity, critical thinking, and problem solving–areas where human skills excel and AI currently lacks finesse.

Huge data sets, limited off-the-shelf solutions, and personal bias all hinder operational efficiency and employee satisfaction

C-suite executives who are charged with overcoming inefficiencies in workforce management also have to contend with an overwhelming volume of disparate data points when seeking long-term solutions (e.g. software or training). This often leads to the hasty selection of the first option they find, rather than the most suitable one; in the rush to streamline operations and enhance efficiency, organisations may opt for the solution that appears adequate, without thoroughly evaluating alternatives – budget constraints, technological compatibility, and specific organisational needs can all also be potential limiters.

The result is often going with a “good enough” off-the-shelf solution for workforce management – i.e. one which isn’t calibrated to the specific needs of their organisation.
The other problem with off-the-shelf solutions is they are not prescriptive. They can crunch data, but simply produce graphs which managers then have to interpret and implement. An AI system, on the other hand, can take the same types of data and can prescribe recommended actions, not just supply information. Unlike generic solutions, AI-driven systems possess the capability to adapt and evolve in response to dynamic organisational needs and changing circumstances. They can identify patterns and trends that may not be apparent to human decision-makers, enabling organisations to make data-driven workforce optimisations that enhance overall performance. In an era where senior management are overwhelmed by tasks, AI can support by making this type of decision-making far simpler.

Leaving these key decisions around the allocation of work in the hands of these senior managers – without the right systems to support them – can also exacerbate issues related to bias, favouritism, and misconceptions. Without AI-driven systems to objectively assess and allocate tasks based on merit and proficiency, managers may unfairly assign work according to personal biases or subjective opinions. This can lead to unequal opportunities and disparities in workload distribution; senior managers may rely on misconceptions or limited knowledge about employee skill sets. This can hinder efforts to create a fair and meritocratic work environment, affecting both organisational efficiency and employee morale.

AI has a much larger, more valuable role to play in personalised learning and development

While machine learning is used extensively in EdTech and Learning platforms to match people to courses or content, most learning happens on-the-job.

To foster continuous learning, upskilling, and reskilling and ensure that the workforce remains adaptable and competitive in the face of evolving job roles and industry demands. There is a case for using AI to deliberately stretch employees to work in new teams, or on new types of project. This challenge is to do this within hindering quality of delivery, and quantifying the value of the informal learning of deliberate stretch assignments.

Satalia’s approach – recently rolled out for a client – is a great example of this. Satalia Workforce Allocation algorithms were designed to ensure only one person within a work team is “stretched” on a project at any given time – i.e. outside of their core comfort zones, learning new skills or gaining valuable new knowledge. The rest of the team on that project remain within their comfort zone. This means:

• Everyone on the team gets their turn to learn through “stretch” assignments
• Only one person “stretching” at a time means there’s no risk to the project being delivered on time

• Every team member gets hands-on experience, learning while doing – compared to training sessions which take whole teams off their work at a time

This type of approach moves the idea of work allocation from a largely passive state to an active one.

Future trends and ethical considerations

Looking ahead, AI-driven trends in workforce optimisation, such as predictive analytics for talent acquisition and employee sentiment analysis, promise to reshape HR practices. Predictive analytics enables organisations to forecast hiring needs accurately, while sentiment analysis provides insights into employee satisfaction and engagement. These advancements streamline recruitment, improve talent retention, and foster a positive work environment, enhancing organisational performance and competitiveness.

Transparency, inclusivity, and fairness

Ethical considerations surrounding AI in the workplace will increasingly emphasise the importance of transparency, fairness, and inclusivity. As AI technologies become more integrated into white-collar environments, organisations will need to prioritise the development and implementation of ethical AI policies. These policies will dictate how AI systems are designed, deployed, and managed to ensure that they uphold principles of fairness, transparency, and accountability.

Organisations will need to ensure that AI systems are trained on diverse datasets and evaluated for biases to prevent discriminatory outcomes. Regular audits and monitoring will likely be necessary to identify and address any biases that may arise over time.

Inclusivity will also be paramount in the deployment of AI technologies in white-collar spaces. Organisations must consider the diverse needs and perspectives of their workforce and ensure that AI systems are accessible and equitable for all employees. This may involve providing training and support to employees to help them understand and interact with AI systems effectively.

Overall, by prioritising ethical considerations in the deployment of AI technologies for work allocation, organisations can build a more inclusive, transparent, and equitable workplace environment that maximises the benefits of AI while minimising potential risks and pitfalls.

Surveillance and the surrounding ethical questions

We could see some white-collar organisations attempt to adopt specific practices from the blue-collar space in order to give their AI systems more data to work with. One such instance could be the use of computer-vision monitoring systems in the workplace – for example, using a computer’s webcam or office CCTV to see when people are at their desks when they shouldn’t be (e.g. working overtime). While this practice is relatively straightforward in spaces like restaurants or retail, it is much harder in knowledge work workspace.

While the implementation and rollout of such a system in an office environment is technically possible, would it undermine trust? Would skilled, knowledgeable professionals react negatively to the feeling of “being watched”? Can you really equate time spent at a desk with time spent working productively? We may see some white-collar organisations test these dilemmas in the coming years.

Greater collaboration between humans and AI

The transformative potential of AI in optimising the workforce lies in cultivating workflows between humans and artificial intelligence. In short, humans and AI working in collaboration. A future where intelligent technologies empower employees, nurture their talents, and enhance overall workplace satisfaction.

But as AI starts doing the more monotonous work, it begs the question: what should humans be doing instead? It’s not just a practical puzzle; there are ethical dilemmas too. The right balance will need to be struck between human work and machine work, while closely considering what that balance means for the future of work both within an organisation and wider society.

Demographic shift in knowledge work and changing attitudes to AI

In an era of enormous demographic changes, the rise of AI-driven work can’t come quickly enough. Many Gen Zs are at the start of their knowledge work careers, and Gen Alphas will follow in the early 2030s. Both generations grew up as natives of technology that came along later in life for Gen X, Gen Y, and Boomers – like the iPhone.

This type of technology is full of AI features – but they’re well hidden by seamless, hyper-simplified user interfaces; AI is essentially “baked in” to the user experience, running in the background powering apps etc. This has already shaped the expectations of these younger generations – they will expect to enter a workplace where they can get an answer to any question they are looking for. They will expect help on navigating their career , getting feedback on their work, and much more at the touch of a button – something that most enterprise technology  cannot offer at this point.

However, with data-rich AI tech like digital twins on the horizon, the workplace is gearing up to be a whole new ball game for upcoming generations. Younger generations would then benefit from having a digital doppelgänger that knows their career options  better than themselves. Digital twins could create virtual versions of real-world scenarios, whether it’s mapping out career paths or optimising work processes. Younger workers could interact with their twin through a chat bot-like interface – getting personalised advice, helpful nudges, and a smoother ride through their professional journeys in a way they’re used to.

Advances in technology like AI will also continue to affect the type of work young people seek out – with AI increasingly automating routine tasks, these digital natives will likely gravitate towards roles that prioritise empathy, creativity, problem-solving, and adaptability. They may seek professions that offer opportunities for continuous learning and innovation – and that use tech to support this process. Traditional job structures and industries may undergo significant transformations to accommodate these shifting preferences, paving the way for a more dynamic and tech-driven workforce landscape.

Migration patterns will also change how work is structured. Countries like Italy, Japan, and South Korea are running out of young workers. Governments and industries will likely need to adopt policies and practices that attract and retain young talent, embracing remote work, flexible schedules, and diverse recruitment strategies to address “brain drains.” Workforce AI technology could help solve some of these challenges; AI-driven recruitment tools could help attract talent from outside usual markets, attracting overseas talent to replace workers lost to other countries. Predictive analytics can forecast future labour shortages, prompting proactive talent retention strategies. And AI-powered workforce analytics could offer insights into employee preferences, enabling tailored flexible work arrangements.

At the other end of the generational spectrum, industries like healthcare will need the assistance of technology like AI to  adequately support a greying population; the WHO predicts the world will be short of 10 million care workers by 2030, and so supporting existing workers through the use of AI technology is paramount.

Example case: Home-visit scheduling for the NHS Foundation Trust

Satalia collaborated with the North London Foundation Trust, addressing the intricate challenge of scheduling home visits within the NHS while also prioritising benefits for NHS staff. Our aim was not only to allocate the right healthcare professionals to patients efficiently, but also to enhance the working experience of NHS staff  – reducing time spent on the road and maximising their ability to help patients.

With support from an Innovate UK grant, we implemented Satalia Field Service within the Trust. Despite the complexities involved in deploying AI within healthcare settings, we tailored and integrated the system in less than two weeks, showcasing immediate scheduling enhancements. The outcomes were transformative for both patients and staff:

• A 33% increase in team utilisation, indicating improved resource allocation and service delivery.
• NHS staff benefited from an average reduction of 12.7% in travel time, resulting in less time spent stuck in traffic and more dedicated time with patients.
• Routes were optimised, leading to an average reduction of 12.9% in travel distance, enhancing efficiency and lowering operational costs.

By seamlessly integrating Satalia’s field service solution into the Trust’s operations, ongoing benefits and value persist, ultimately elevating patient care and significantly improving the working experience for NHS staff.

Moving towards an AI-powered future of work

By embracing AI-driven workforce management solutions, businesses can create a harmonious balance between technological innovation and human-centric practices. They can foster a workplace environment where employees thrive and organisations achieve success that would be out of reach using off-the-shelf technology, or no technology at all.

However, it’s important to note there will never be a “one-size-fits-all” solution to the above. Each organisation’s needs are unique, and so any AI workforce solution must be customised and configured. That’s where Satalia sees the future of AI products; not fully bespoke, not “off the shelf,” but somewhere in between. Satalia builds AI systems to understand, match, and balance your workforce against ever-changing business demands, rules, and teams.

While traditional analytic tools find patterns, our AI prescribes the best available daily actions among millions of variables. We then integrate the output of our AI systems with the digital tools you already love to use.

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