The Automation of Knowledge Work

The Automation of Knowledge Work by James Duez, Chairman Rainbird

by James Duez – Chairman of Rainbird

The last few years have seen a number of disruptive technologies come together in a kind of perfect storm – a convergence of several innovations which, until recently, have only been viewed in isolation:

  • Big Data Analysis
  • High-speed Computing
  • The Internet of Things (IoT)
  • Artificial Intelligence (AI)

These technologies have combined to drive a number of disruptive scenarios, the most notable of which is a shift towards the automation of knowledge work.


What is knowledge work automation?

If we look back over the last century, we see the significant automation of physical work. When machines started taking on the heavy-lifting and repetitive tasks, it allowed people to engage in higher-value activities. Over the past two decades there has been a significant acceleration in the automation of physical, usually low-paid work. This automation started in agriculture and quickly spread to manufacturing and other sectors.

We have seen the removal of some staffed supermarket checkouts, replaced with banks of self-service tills supervised by just a single worker. Entire populations have been screened for specific health risks, a feat only achievable because of the advances in technology. These innovations can sometimes seem harsh and will continue to have an impact on employment and the broader economy, but for the most part they have delivered significant increases in efficiency, improved quality and a better society.

This automation has continued to accelerate and is spreading from the relatively simple and repetitive manual tasks to more knowledge-based jobs. Organisations in all sectors are seeking to exploit a range of new automation technologies, in order to reduce costs, raise standards, and in some cases bring new products and services to the market.

In the same way that machines have changed the way we have approached physical work, this new generation of AI-powered technologies are increasingly to be found in the workplace aiding the knowledge worker. Knowledge work automation is rapidly changing that workplace.


The five benefits of Knowledge Work Automation


McKinsey & Company predict that knowledge work automation will be the most significant disruptive technology to influence the world over the next 10 years, second only to the addition of 2-3 billion new mobile internet users by 2025.

The automation of knowledge work is expected to impact the economy by between $5-7tn per annum, the equivalent of between 110m and 140m jobs. These figures do not include any estimate of the value of higher quality output expected through the use of better knowledge tools.

Many of these technologies are with us now. What we are seeing is not the apex, but just the beginning. Despite that, artificial intelligence and other knowledge work technologies are already changing the workplace significantly.


1. Transforming customer contact


Knowledge work tools can reduce costs by helping organisations be more efficient, but they can also substantially raise standards by delivering fast, consistent and high-quality customer service.

Customers already expect self-service, anywhere and anytime with the option for assisted service if necessary. It’s not just organisations that want high-quality automated services, its customers. The move towards automated self-service is accelerating so quickly that Gartner predict customers will manage 85 % of their relationship with the enterprise without interacting with a human by 2020. Knowledge work tools are the technology that will deliver this.

Traditionally, service centres utilise knowledge management systems. These are often simple knowledge bases comprising fixed documents or databases containing question and answer pairs (FAQs). They also use scripts to guide contact centre agents through structured processes, which tend to be based on decision trees. These FAQ and decision tree technologies are quickly becoming outdated and often fall short of customer expectations.

In a recent Forrester report, only 44 % of firms surveyed even had an agent-facing knowledge management system at all. In organisations like these, agents fielding complex questions cannot easily access the content they need to reliably answer customer questions, putting the quality of service at risk. Consumers cited their greatest customer service complaints as:

  • Different customer service agents giving different answers (41 %)
  • Customer service agents not knowing the answer (34 %)

Modern products and services are complex, so call centre agents need to have a wealth of experience and knowledge to be able to handle the breadth and depth of today’s customer issues. Contact centre managers agree that the right knowledge delivered to the customer or agent at the right time is critical to a successful interaction. Done well, this can increase customer loyalty, reduce call handling time, and make the operation more efficient.

2. Achieving operational excellence

Rising volatility and business complexity have recently made operational transformation much more difficult. Another three billion customers are expected to join the connected, global middle-class over the next few decades, so conventional operational models will come under increasing strain.

In a perfect world, a company’s day-to-day operations are managed for peak performance, so the company can maximise its profits while minimising its risks and costs. Companies must become more agile and responsive if they are to succeed and grow. To deliver this they need technologies that help them scale and flex their operation cost-effectively.

A new generation of knowledge-work technologies are enabling expertise to be embedded in business systems. This is leading to more efficient and consistent knowledge-work. Businesses can use these new tools to create systems capable of making nuanced decisions that learn from outcomes and challenges. Traditionally, such systems have required a large team of Business Analysts to understand the requirements, and Software Engineers to encode knowledge in logical rules.

Analytics has a critical role to play in driving a cycle of continuous operational improvement. Failure to understand how well operations are functioning can be fatal for any organisation. Knowledge-work automation tools can record how and why outcomes were reached, helping to drive insight and rolling improvements.

3. Preventing knowledge leakage

Losing someone who holds knowledge can be disruptive for large organisations and devastating for SMEs. The baby boomers in particular have developed a significant amount of personal knowledge of how things work and why. As these workers leave the workforce, their personal expertise and ability to keep things running smoothly will leave with them.

Technology will be critical in bridging the gap created by the loss of these valuable employees. The knowledge workers that are left will face operational decisions that they haven’t faced before, having relied on the veteran employees to handle them up until now.

The knowledge worker of the future will need to rely on knowledge tools to drive immediate decisions, much more so than past generations. The knowledge worker’s need for rapid information is increasing constantly. While most companies are drowning in data, intelligently analysed and reported information, together with a way of accessing the right information at just the right time, is
often sorely missing.

4. Increasing sales

Successful businesses have effective sales people, but these rockstars are limited in number and considerable resources must be expended recruiting, training and retaining them.

Sales teams have been using big data analytics for some time in order to make the sales process more targeted and ultimately, lucrative. New advances in artificial intelligence are already delivering unprecedented insight into customer requirements, frequently revealing purchasing compulsions that customers themselves may not be aware of.

This same technology can also learn what works and what doesn’t, simply by being used. It can even provide a way to ask “What if ...?”, providing commentary on new approaches by looking back at historic sales to determine if better outcomes could have been reached with a different approach.

The combination of big data analytics and modern decision-support is like providing newly recruited sales staff with x-ray glasses and a virtual expert over their shoulder, whispering advice in their ear. This is particularly invaluable with new recruits and where the product and sales cycle is complex, and can enable everyone to get close to the effectiveness of the sales rockstars.

5. Streamlining governance, compliance and risk management

Most sectors now operate under some sort of regulatory framework or the scrutiny of a governing body. The effective management of governance and compliance obligations is increasingly becoming a stress-point, with many large organisations stuck in a costly cycle of breach followed by remedial action. This exhausts critical resources which then cannot be used to proactively avoid such future failures.

Knowledge-work automation tools can be used to structure a framework for the development and application of complex rules and regulations. These can act either as a reference for staff, providing decision-support, or as a control that enforces a strict practice. Governance rules are generally not ambiguous, but they are extensive and complex which makes them difficult to memorise and interpret consistently. Modern knowledge-work technologies are changing this.

It is frequently the data at hand which is ambiguous and contradictory, often sourced from multiple disparate systems. If an expert is capable of making a nuanced judgement in the face of such data, then that same expert can model their knowledge and have that incorporated into a business process management system. This would enable subtle judgements to be made efficiently en masse and at high speed, either in the form of an automated system or decision support tools for the human knowledge worker.


Conclusions

Computers are now becoming capable of doing jobs that it was assumed only humans could perform. They can act on unstructured commands, answering a question posed in plain language and making subtle judgments. It is possible to sift through massive amounts of information to discern patterns and relationships. Computers can learn about concepts, relationships and rules – from the experts, by crunching data or simply by being used.

Interfaces have advanced too, and artificial intelligence software can understand and interpret human speech, actions, and even intentions from ambiguous commands. In short, computers can increasingly do many of the tasks that are currently performed by knowledge workers. Such tools can reduce costs for companies but perhaps more interestingly can also extend the powers of human workers and allow them to off-load tedious detail work.

The ability to innovate is now a top priority for companies everywhere and the speed at which innovation occurs is accelerating. The successful adoption of knowledge work technologies will be of critical importance if businesses are to thrive or even survive in the coming years.

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About Rainbird

Rainbird is an award winning Artificial Intelligence platform that makes your business operations smarter. It can make predictions, recommendations or decisions, by reasoning over a model of human knowledge in the face of available data. More on rainbird.ai

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