Effective Strategic Response Management (SRM) — the people, practices, and technology that unlock organization knowledge for profitable growth — brings much-needed efficiency, organization and automation to every response process to help you deliver winning responses to RFPs, RFIs, DDQs, VSQs, and more.
For decades, this process has been labor-intensive, requiring significant manual effort and time. However, response processes are being transformed by the increasing prevalence of Generative Artificial Intelligence (GenAI) to produce first drafts with pre-approved content and Machine Learning (ML) to automate repetitive tasks and leverage workflow intelligence, saving up to 80 percent over manual response times.
But even among these advancements, Large Language Models (LLMs) stand out as real game-changers. These models, which include examples like OpenAI’s GPT-4, are designed to understand and generate human-like responses to manual inputs, making them an essential tool for enhancing communication and response strategies.
In this blog, we’ll dive deep into how LLMs work, explore their role in GenAI and SRM, and discuss the broader applications of AI and ML when it comes to shaping the future of business communications.
Whether you’re new to the concept or looking to deepen your understanding of LLMs, this blog will shed more light on the transformative power of LLMs in SRM.
Understanding Large Language Models (LLMs)
At their core, LLMs are complex algorithms designed to understand and generate human-like responses based on the input they receive. They are essentially incredibly sophisticated text predictors, taking a text input and predicting what will come next with remarkable accuracy.
It’s this remarkable accuracy that makes LLMs invaluable for applications like virtual assistants, automated content creation (e.g., writing a complete first draft of an RFP), and customer support systems that require natural language understanding and generation.
These LLMs are trained by “feeding” them vast amounts of text data from numerous sources, such as books, articles, and websites, which helps LLMs learn the nuances of language, including grammar, context, and even varying levels of common sense reasoning.
In short, by “feeding” LLMs vast amounts of data, they can be trained to respond with increasingly natural responses that mimic the way we communicate, moving further away from the robotic responses of old. This is done through training with neural networks, such as deep learning algorithms, which are designed to mimic the human brain’s structure and function.
When it comes to SRM, LLMs use their ability to naturally generate human-like responses to enhance efficiency and precision by automating many phases of the RFx response process, creating compelling proposals, and ensuring consistent communication. This saves time and improves response quality, ultimately leading to higher customer satisfaction and operational efficiency.
Using LLMs within SRM
LLMs have transformed SRM by automating responses to Requests for Proposals (RFPs) and other business inquiries. Gone are the days of manually crafting responses under tight deadlines, often responding to the same questions.
Instead, LLMs deeply integrate with content libraries and knowledge bases to streamline content discovery, generation, and optimization. Leveraging advanced natural language processing (NLP) and retrieval-augmented generation (RAG) techniques, LLMs can read vast amounts of data to identify relevant content with unparalleled accuracy. Teams can then quickly generate accurate, tailored replies in a fraction of the time, ensuring consistent, high-quality responses and helping businesses win more contracts.
LLMs also enhance the utility of knowledge bases by continuously learning from user interactions and updating relevance models, ensuring that the most pertinent content is surfaced even as data scales or organizational priorities shift.
Empowering teams with LLMs and AI
Responsive was built with AI at its core since its inception, bringing efficiency to content generation and workflows across bids, questionnaires, and trust centers. The Responsive Platform can generate a first draft of an RFP in minutes and refine answers through custom prompts — with source citations from your knowledge base.
Whereas ChatGPT and its like are general-purpose chatbots built that can be used for a wide range of applications, Responsive AI understands the nuances of the content our users work with and provides more relevant and accurate suggestions.
Both systems use the same underlying LLMs to train them for responses. However, unlike ChatGPT, Responsive does not train our LLMs on user data, so users know their data is always secure.
“We were able to reduce the time maintaining our content library by 50% through the elimination of writing/editing tasks involved in each RFP response, and AI Assistant has contributed to our increasing win rate,” said Autumn Wenner, Content Proposal Specialist at Jagger, in a customer story that highlighted the impact and importance of using Responsive AI for an impressive 15x ROI.
Discovering the power of fingertip access to company content
LLMs can also power virtual assistants that provide immediate, accurate responses to complex demands. Responsive Ask unlocks organizational knowledge by giving every member of your team quick and easy access to Responsive AI to find the best answers to real-time customer inquiries throughout the sales cycle, using verified sources with clear source citations.
This automation not only delivers the answers buyers and customers want faster, but also gives back time to field and response teams to focus on more strategic activities, boosting team-wide productivity.
For example, Accruent, a SaaS (Software-as-a-Service) company dedicated to helping customers and clients with their physical space and asset management, was able to complete around 50% of all RFP questions on their own thanks to a restructured content library. The team has become so efficient that they’ve gone from working on five or six RFPs at a time to tackling 15-25 live projects at once.
Accruent’s proposals team now answers 75% of the security questionnaire before they send it to an SME, and they’re completed in half the time. The team’s confidence also increased, resulting in an increase in the number of questionnaires they’re able to submit.
The proposal process at Accruent is so centered on Responsive that people have taken to referring to the proposals team as the “Responsive team.”
“That is probably the biggest compliment we can give the system,” said Jack Pearce, Manager of the Proposal Team.
Future trends and developments in AI for SRM
We’ve talked at length about how AI-driven tools like LLMs have already changed the way businesses think about and incorporate SRM. But where do we see advancements in LLMs and other AI technologies leading us in the future?
Responsive has already seen the impact AI drives for organizations like Netsmart, a software and IT solution provider for community-based healthcare providers outside the hospital setting in the U.S.
After Netsmart built a Content Library in Responsive, user adoption increased by 540 percent, and they submitted 67 percent more proposals within 10 months. Netsmart deployed Responsive AI to accelerate the response time from five minutes to 30 seconds per answer — a 10X improvement — on average.
We can clearly see the current increase in efficiency and productivity AI provides. The US National Bureau of Economic Research said LLMs currently increase productivity by up to 34 percent.
What will that be in 2030? 50 percent? 60 percent? Even higher?
The 2024 State of Strategic Response Management Report shows that 89% of companies polled are already experimenting or using AI. Among those, industry leaders said the most impactful use of AI in the coming years will be in optimizing the use of staff time while operating with the same size team.
Other ways leading companies saw AI impacting SRM in the future include:
- 63% – Automating or simplifying content management by flagging and helping update ROT (redundant, outdated, or trivial) content
- 56% – Quickly and easily drafting and optimizing responses
- 55% – Checking responses for accuracy and compliance, flagging risks along the way
- 52% – Personalizing responses based on customer context and relevant company advantages
- 49% – Establishing project timelines, tasks, and review cycles with minimal user input
Another important discovery in The 2024 State of SRM Report was that most bid and proposal professionals no longer have fears surrounding job security. Instead, they’re optimistic that AI development will create an environment in which they can better thrive by taking care of tedious, repetitive tasks, so that they’re able to deliver even more impact to the business.
From project conception to advanced strategy implementation, AI will remain a partner in shaping creativity in the next decade while human minds remain at the center of the creative process.
Security and data privacy concerns over the future of AI
As we look to the horizon, LLMs and other AI technologies promise to further revolutionize SRM through increasingly sophisticated neural network architectures and improved training algorithms. These advancements will enable LLMs to understand and generate even more nuanced and contextually accurate responses.
However, as AI continues to rapidly develop and spread across the internet and workforce, there are numerous challenges to consider. Chief among those concerns is data privacy and security.
AI systems often handle millions of instances of sensitive customer information a day, and businesses have to implement robust security measures and comply with data protection regulations to safeguard this data.
Responsive has consistently stayed ahead of this challenge by creating a closed ecosystem where no customer data is shared externally, nor is customer data used to train any AI model.
Ethical concerns over the future of AI
There is also the ethical concern of AI bias, where algorithms may inadvertently perpetuate existing biases present in the training data. Companies need to be vigilant in auditing their AI systems and continuously refining them to ensure fair and unbiased outcomes.
For example, Google’s CEO Sundar Pichai admitted that Gemini AI model’s responses showed ‘bias’ when generating AI images of people from historical backgrounds. Google went so far as to pause the AI’s ability to generate images in early 2024, and the CEO said the company was working to fix it shortly after the story gained traction on social media.
No AI tool is perfect, especially when first introduced, but it was a clear reminder that AIs are trained to generate text, images, and videos based on the input they receive. Taking in vast amounts of information, especially from the Internet, without safeguards will lead to inaccurate, misleading, and potentially hurtful representations.
The cost of failing to keep in mind the importance of AI ethics can be great, ranging from regulatory fines to something that’s arguably even more important: the trust of your customers and the public at large. Responsive is committed to ethical AI and Responsive AI is developed and deployed with fairness, accountability, transparency, and respect for privacy.
Final thoughts
The role of AI in transforming SRM is continually evolving. As AI gets smarter, it will handle more complex tasks, make better decisions, and become more proactive, with AI not just responding to inquiries but also anticipating customer needs and autonomously solving problems before they arise.
For Responsive, AI has always been in our DNA since our founding in 2015. This decade of experience with AI has gone hand-in-hand with our tenets of responsible AI design to help create a brighter future where AI in SRM creates seamless, efficient, and highly personalized customer interactions.
By staying ahead of the curve and addressing potential challenges before they arise, businesses can leverage these technologies to transform the way they pursue and win business, while delivering exceptional customer experiences.
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