Seven ways digital workplace teams support the rollout of generative AI

March 13, 2024 by

Generative AI continues to be a major focus for digital workplace teams as organizations start to grapple with how best they can use AI to maximize productivity, improve processes and gain a competitive edge. In fact, in our annual digital workplace predictions for 2024, at least eight out of the ten predictions relate to generative AI, or are impacted by generative AI. It is going to have a major impact on the digital workplace going forward.  

We think digital workplace teams have a huge role to play in the success of generative AI. There are huge challenges in managing risks and supporting users, and there is a likely to be a steep learning curve as we assimilate experiences across multiple business functions and lines of business, learning as we go. To get the very best out of generative AI we need stewardship, governance, foundational work, active management, a connected and holistic approach, a spirit of innovation and, quite probably, a level head. These are all qualities that digital workplace teams exhibit, and their experience in rolling out, managing, supporting and improving enterprise technology provides a hugely valuable precedent. 

A new DWG member research report, Generative AI: Unlocking value and managing risk inside organizations, uncovers some of the practices and use cases that digital workplace teams can consider to get the best out of generative AI, while also overcoming some of the associated challenges.  

While the full report is for members only, you can download a report excerpt, and in this post we’ll touch upon seven ways featured in the report that digital workplace teams can support the rollout of generative AI.  

1. Keeping an eye on the landscape 

AI is moving incredibly fast. New products and services are coming, with Microsoft Copilot at the head of the queue, and there will be further large language models (LLMs) from major providers, with OpenAI and Google continuing to improve and refine their offerings. The regulatory landscape is also likely to change, with legal test cases in flight and legislation either to be introduced or fast approaching, such as the EU AI act. New practices will emerge across the digital workplace profession too, as well as internal lessons learned around what works and what doesn’t. 

With things moving so fast, it will be imperative to keep an eye on the landscape to navigate risks and seize the relative opportunities. Digital workplace teams can play an active role by ensuring they keep up to speed with market and regulatory developments, as well as best practices across the industry. However, horizon scanning for updates and learning is likely to need to be a cross-functional effort, with IT, HR and legal teams also required to keep an eye on developments. Organizations that are in the know and up to speed are likely to be the ones who will gain value from generative AI earliest – and before their competitors. 

2. Contributing to strategy and governance frameworks 

In this member report, we cover the associated risks of generative AI in detail. These are extensive and fall into four main areas: 

  • Privacy, security, IP and regulatory: from protecting private data and intellectual property (IP) to increased risk of cybercrime.  
  • Content-related risks: accuracy, a lack of transparency, and more. 
  • People and organizational issues: impact on current roles, processes, learning, and more. 
  • Ethical aspects: from inherent bias to unethical use. 

Organizations need to have the right strategies, policies and approaches in place to not only reduce these risks head on but also maximize the considerable opportunities that generative AI presents. As we head into the AI era, having a coordinated, informed and intentional approach will become increasingly important. Digital workplace teams have a significant contribution to make in guiding strategy and policy, based on their role and existing experience of viewing technology and its use from both the organizational and user points of view.  

3. Prioritizing use cases and preparing pilots to support learning 

The potential for generative AI is both deep and wide, covering many different processes, scenarios and business areas. In the DWG research, we cover six specific use case areas that are of particular interest to digital workplace teams: 

  • Content generation and content management. 
  • Knowledge management (KM). 
  • Findability. 
  • Chatbots and digital assistants. 
  • Employee experience and HR-related processes. 
  • Automation and app development. 

There is so much that can be done, and it’s sometimes hard to know where to start or which use cases to prioritize. It’s also still very early days and there is going to be a significant learning curve as both digital workplace teams and users get to grips with this fast-evolving technology. 

Digital workplace teams can play a valuable role in: 

  • helping to prioritize which use cases to focus on 
  • establishing and setting up different pilots 
  • defining formal processes and frameworks to evaluate use cases 
  • reviewing, analysing and sharing any learnings. 

All these elements can prove very valuable towards the successful use of generative AI.  

4. Supporting users 

A key activity for digital workplace teams is to support users in getting the best out of the tools at their fingertips. This manifests itself in different ways including training, guidance, ‘What tool when’ frameworks, managing networks of champions, and more.  

Users need guidance and support on using generative AI to both minimize associated risks and achieve optimal results, for example on the best way to express prompts and then refine them, or being aware of risks around privacy and data accuracy. Digital workplace teams have a proven track record in user support, and savvy teams will be able to extend the programmes, structures and resources they already have in place to cover generative AI.  

5. Measuring success 

How do we measure the success of generative AI? Up to now, this has perhaps been a topic that hasn’t been explored as much as some others, but will increasingly come into focus as teams seek to track the success of generative AI pilots and the impact of solutions such as Microsoft Copilot.  

To some extent, measurement will cover existing reporting for the different areas in which AI is being applied, such as search, as well as user satisfaction, but there may be other metrics that emerge too. With many digital workplace teams already knee-deep in digital workplace measurement and analytics, this is an area where they already have considerable insight and experience. 

6. Gather and act upon user feedback 

At this early stage, it will also be important to learn about the actual, real-world experiences of users as they work with generative AI. Were they able to achieve what they wanted to do? What are some of the pain points? What kind of support do they need? Are there any unintended consequences?  

Digital workplace teams are often adept at collecting and acting upon user feedback to help launch and improve different tools. Sometimes they will have formalized processes, support structures and even user groups that can be leveraged and mobilized to gain essential user feedback about using generative AI. In turn this can: 

  • complement analytics to get a better overall understanding of how generative AI is being used 
  • help track success 
  • help define the generative AI product backlog and roadmap 
  • improve approaches to support, training and change management. 

7. Establishing and utilizing information management foundations and frameworks 

Generative AI has the potential to revolutionize various different areas including: 

  • search and findability 
  • document creation 
  • chatbots 
  • content tagging 
  • analytics and reporting 
  • automation of simple processes 
  • and many more. 

But currently none of these are ‘plug and play’ or work with any depth straight out of the box. They all need foundational work to be carried out in order to enable any meaningful value and reduce associated risks.  

Some of the work required is similar to the data, information and knowledge management practices that digital workplace teams are already familiar with in their work, for instance in improving search, such as: 

  • creation and management of taxonomies 
  • development and execution of data and information standards 
  • ongoing testing, measurement and refinement 
  • associated change management and training. 

2024 will be the year of generative AI 

Generative AI has certainly been the subject of much media hype and overinflated expectation, and perhaps its immediate impact has been exaggerated. Is everyone really going to be spending all their time in Copilot by the end of the year, for example? But over time – and relatively quickly –  its influence on many areas of the digital workplace, including findabilty and search, knowledge management, the digital workplace tools we use, content generation, analytics, and more, is likely to be both deep and wide. Digital workplace teams need to act now in order to help organizations prepare for the future in an informed, agile and sustainable way to get the very best out of generative AI. The journey has already started. 

Related resources

Generative AI:
Unlocking value and managing risk inside organizations

Download the report excerpt >>

Categorised in: Artificial intelligence and automation, Metrics & measurement, Strategy & governance

Steve Bynghall

Steve Bynghall is a freelance consultant, researcher and writer specializing in the digital workplace, intranets, knowledge management, collaboration and other digital themes. He is DWG’s Research and Knowledge Lead, a benchmark evaluator and research analyst for DWG.

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