1. 8 use cases and their ROI

Forrester’s research highlights the substantial benefits AI can bring when applied to specific, well-defined use cases in document automation. In a survey of 43 global business technology decision-makers, there was strong optimism about AI’s potential to:

• Increase employee satisfaction
• Reduce time spent on manual tasks
• Cut costs
• Enhance customer experience

Forrester identified eight key areas where AI-driven document automation can significantly impact:

Focusing on these use cases enables enterprises to streamline operations, improve efficiency, and achieve better outcomes.

2. Knowledge management (KM) shows the highest transformational value

Incorporating LLMs into KM systems can transform a traditionally cumbersome process into an efficient, impactful function. By leveraging LLMs, organizations can simplify the capture, storage, and retrieval of critical knowledge, making it more accessible and actionable. This leads to faster, more accurate decision-making and significantly boosts overall efficiency.

Deploying LLMs in KM is seen as relatively straightforward, offering substantial benefits with minimal implementation challenges.

3. Data extraction ranks as the second-highest transformational value

Data extraction is the second most impactful use case for ROI in Forrester’s analysis. By integrating AI and LLMs, businesses can automate the extraction of critical information from documents, emails, and forms, significantly reducing manual effort and processing time.

LLMs also enhance legacy systems, expanding their capabilities beyond traditional indexing and data entry – making data extraction a powerful tool for driving ROI.

 4. AI-driven customer communications

Forrester points out that many businesses still use outdated customer communications management (CCM) tools, but AI-driven solutions, including LLMs, are set to revolutionize this area. 

Traditionally, creating and updating templates required specialized skills and considerable manual work. With generative AI, these processes are now streamlined, automating content creation and reducing workloads.

Schneider Electric has already seen benefits by incorporating Templafy into their workflow, simplifying proposal generation, and significantly reducing the time and effort required by their teams.

“In the future, our commercial staff will have less work to do. They will put in a prompt and get the exact offer they need. We use Templafy to pull sections into a template today. Its latest product will have LLM support.”  – Jacques Denis, cybersecurity tendering engineer, Schneider Electric

5. The challenges of document automation – and how to mitigate them

Forrester’s report outlines several key challenges in implementing LLMs for document automation. However, with strategic approaches, these obstacles can be effectively managed to ensure successful adoption and deployment. Here are the main challenges and how to address them.

1. Data security concerns
   
   Businesses are wary of exposing sensitive business data, especially with public AI models. To mitigate this risk, choose a platform that prioritizes enterprise-grade security and privacy. Look for solutions that offer robust encryption, stringent access controls, and adhere to industry standards like ISO 27001 and SOC 3.
   
2. Risk in end-to-end automation
   
   Automating end-to-end workflows introduces risks, particularly in complex areas like outbound communications. Start with lower-risk segments and gradually expand automation to build confidence in the system’s reliability – while maintaining human oversight where it’s needed most.
   
3. Trust and reliability
   
   Building trust in AI outputs is crucial, especially when LLMs are involved in critical decision-making. Forrester suggests starting with less critical use cases to validate AI accuracy and reliability. As the system proves consistent, you can apply it to more complex tasks, ensuring it aligns with your organization’s needs and standards.
   
4. Budget and resource allocation
   
   Implementing LLMs requires significant financial and human resources. Forrester recommends starting with smaller pilot projects that require less investment but clearly demonstrate benefits. This approach helps justify further investment in AI, allowing for scalable growth as your business becomes more comfortable with the technology.
   
5. Internal skill development
   
   A lack of internal expertise is often a barrier to successful AI adoption. Forrester emphasizes the importance of investing in targeted training programs and involving AI experts during the initial stages. Over time, developing a skilled internal team will be crucial for sustaining and expanding AI applications within your organization.