How to Prepare for AI’s Impact on Knowledge Work


Knowledge work is critical to business and society. It is the work done by software developers, engineers, scientists, pharmacologists, doctors, lawyers, designers, and other kinds of workers who solve non-routine problems. In contrast to certain kinds of manual labor, knowledge work has seen only minimal automation to date.

How will advances in generative AI increase the productivity of knowledge workers? Will it replace knowledge workers in the same way robots have replaced some blue-collar workers?

We’re exploring how enterprises should leverage generative AI in a series of articles. In previous articles, we’ve discussed AI’s impact on customer serviceproduct creation and supply chains. In this article, we are looking at how generative AI will augment knowledge work.


Will AI Change Knowledge Work?

Today’s knowledge work is a combination of convergent and divergent thinking. It is the kind of work needed to solve problems in a creative way by combining deep and wide knowledge with existing information and data. While early AI technologies have been able to handle recurring and predictive tasks, newer generative AI tools can generate solutions based on broad knowledge in training data – something that can assist knowledge workers in solving simpler problems and allow concentration on the more complex ones.

Generative AI can also reduce the effort of searching for information – a large portion of a knowledge worker’s daily routine. It does so by interpreting natural language queries and delivering more accurate search results, generating report summaries, and simplifying complex topics.

It goes without saying that knowledge workers depend on documented knowledge. But they also depend on tacit knowledge available in the minds of coworkers, including individual experiences and perspectives. With the ability to learn from past experiences, AI can identify experts who could share tacit knowledge for appropriate situations.

AI Augmented Knowledge Worker


ISG Research | NewVantage Big Data and AI Executive Survey 2022

Real-Life AI Use Cases Across Industries

AI for Knowledge Work in Healthcare and Pharmaceutical Industries

AI has a strong track record in the healthcare industry, helping medical practitioners interpret data accurately and efficiently. The latest use cases involve identifying appropriate therapies and treatments for patients. Today, generative AI is learning from data repositories of medical research results to make valid treatment suggestions to practitioners. When practitioners can access the latest and most effective research without investing a lot of time into searching documents, treatments can start earlier and are more likely to achieve intended results for the patient. This also frees practitioners to interact with patients in a more productive way. For less common diseases, generative AI can also help identify experts to improve treatment. Increasing demand for healthcare services is causing greater stress on the healthcare workforce. Thus, it is becoming crucial for healthcare firms to augment the workforce with AI to deliver superior patient care as well as job satisfaction for healthcare workers.

AI for Knowledge Work in the Software Industry

In the software industry and for software development teams in other industries, writing and documenting source code are key activities; improving the efficiency of these tasks can have a significant impact on productivity. Many developers use existing code from the internet to overcome common challenges, but searching for the right code and adjusting these to the individual problem can take time. Unlike a human developer, generative AI can quickly access portions of available code from within an organization or publicly available code. It can make some of the necessary changes and, with proper training, directly apply coding standards for a project or even support the translation of code from one programming language into another. Usage of AI “copilot” frees up developers to focus on solving more complex issues. In addition to helping software developers build products and solutions, AI copilot-like features can support the efficient use of software for end users.

AI for Knowledge Work in the Advertising Industry

The advertising industry relies on creatives and those able to transform creative ideas quickly into marketing content. Marketing professionals are using generative AI to access data about past marketing content and quickly generate new content to produce an initial set of ideas based on the client’s profile. By combining the benefits of predictive AI, which can provide insights about target audience, with the power of generative AI for content production, knowledge workers can reach the right customer at the right time with the right product. This can result in improved conversion rates and enhanced return on marketing investment.

How AI Changes Enterprise Knowledge Management

Improving the availability of knowledge can boost productivity and the quality of work delivered. It also benefits employee satisfaction as people can focus on solving complex problems. As a copilot, generative AI can learn individual knowledge workers’ preferences, leverage internal available knowledge bases, and apply enterprise standards to improve search results dramatically. This increases the level of personalization for knowledge workers and enterprises.

By leveraging the power of generative AI, enterprise knowledge management, which has always been a critical and complicated process, can be brought to the next level. By automatically tagging documents and transforming information into target-group-specific language, generative AI can improve how enterprises use information for a broader number of employees and apply the knowledge to wider parts of the workforce. Furthermore, knowledge management that can tap into tacit knowledge and consider knowledge workers’ skills and experiences helps organizations identify and leverage their experts in a better way.

To achieve the promises of generative AI, enterprises will need to rethink the way they manage organizational knowledge and how they share it with the AI tools. At the same time, organizations will also need to enable their systems to quickly access knowledge copilots and feed the results back into the training data for a self-learning knowledge base.

Navigating Risks While Leveraging Generative AI

With the advent of generative AI, knowledge work is moving into uncharted waters. Enterprises need to empower their workers to understand the limited accuracy of generative AI and the copyright and regulations around the use of AI, while still adopting the technology. Knowledge workers will need an environment with innovative training and development to nurture their complex problem-solving skills as simpler tasks get taken care of by AI.

Every enterprise needs a well-defined enterprise AI strategy that can support a culture that addresses knowledge work and the value of human intelligence with the right set of copilots. This is where a strategic partner network is necessary: the challenges are beyond technology problems. ISG can help you select and source the right partners and develop your AI culture. Contact us to learn more about how ISG can boost your AI initiative, bring your AI strategy to life, and keep your workforce excited.


About the author

Diwahar Jawahar

Diwahar Jawahar

Diwahar is a Principal Consultant at ISG’s Cognitive & Analytics advisory practice in EMEA. He helps clients leverage data, analytics, and AI to become data-driven enterprises. His advisory experience includes data-to-value realization, shaping data analytics & AI adoption strategies, building enterprise analytics capabilities, and sourcing implementation partners or technology vendors.