What did sales just sign us up for? -How AI helps large software deals

In selling and configuring Enterprise software, the devil often lies in the details. Take a look at any software contract, and you’ll find a complex web of requirements, capabilities, and expectations meticulously documented. Yet, the journey to create this document is often strewn with challenges—unraveling video call recordings, deciphering RFP responses, navigating internal capability documents, and parsing through countless emails with prospects. Thankfully, AI with private LLMs and RAG generators are transforming this daunting task, making it more efficient and deliver solutions that work for and delight customers.

The Challenge: A Multitude of Information Sources

From initial contact to contract signing, software companies engage in numerous forms of communication with their prospects. Video call recordings capture discussions and negotiations, RFP responses showcase tailored proposals, internal capability documents highlight strengths and limitations, while emails with prospects weave a narrative of the relationship and evolving requirements. Each medium captures essential information, but interpreting and integrating this data into a cohesive contract and actionable implementation plan can be overwhelming.

AI: The Digital Interpreter

Artificial Intelligence, particularly Natural Language Processing (NLP), can revolutionize how software companies handle and interpret vast amounts of data from various sources combined in a RAG (retrieval-augmented generation) file.

• Video Call Recordings: AI-powered transcription services can convert spoken words into text, making it easier to search, analyze, and extract key points from conversations. NLP algorithms can identify and highlight crucial topics, agreements, and action items.

• RFP Responses: AI can analyze RFP documents to identify and match requirements with internal capabilities. This ensures that the proposed solutions align well with what the company can deliver, reducing the risk of over-promising and under-delivering.

• Internal Capability Documents: AI can cross-reference internal documents with client requirements, ensuring that all promises made during the sales process are feasible and supported by existing capabilities.

• Emails with Prospects: Through sentiment analysis and keyword extraction, AI can help understand the tone and priorities of the client, ensuring that the final contract is aligned with their expectations and concerns.

The Role of Retrieval-Augmented Generation (RAG)

While AI excels at data interpretation, the RAG allows you to select and combine data sources used by the LLM to produce the insights. The benefits of the RAG include:

• Data privacy and security: A privately hosted RAG combined with a private LLM ensures that your data stays out of the open AI ecosystem.

• Better results: Controlling what data sources are used reduces hallucinations, IP infringement, and using outdated or conflicting sources on the internet.

• Combined insights: While many systems offer standalone chats and AI insights, there is value to be unlocked when sources from across the company are pulled together to create insights that are hard to draw out when looking at one data source at a time.

Writing the Contract Properly

By leveraging AI and a RAG, software companies can create contracts that are not only comprehensive but also precise. AI ensures that all client requirements are captured accurately from various data sources, while the RAG pulls together all of the sources so they can be used together in documenting these requirements. This dual approach minimizes the risk of miscommunication and ensures that all parties are on the same page.

Informing the Implementation Team

A well-documented and accurate contract is only the first step. The implementation team needs clear and actionable insights to execute the project successfully. AI can generate summaries and action points from the contract, they can also use chatbots connected to the RAG combining all of the project knowledge to date to ask questions and get detail on the decisions that have been communicated and the progress to date. This reduces the burden on the sales team answering questions, on the customers not having to revisit decisions they have already made and communicated, and the implementation team in gathering, converting, and synthesizing the pre-work to kickoff the project.

Handoffs and Go-Live

At the end of the implementation, the AI tools continue to play a role in keeping everyone aligned. All of the work that the implementation team has done with the customer including training, workflow design, integrations, and basic setup can be captured and combined with the standard product knowledge to create a customer specific knowledge source (RAG) connected to AI. Customers will be able to chat with the knowledge if they have questions, training can be built in real-time to assist them in understanding how their specific deployment of the technology works. The implementation team can leave the AI solutions behind with the customer and the internal customer support teams to assist them in answering any setup questions. This helps everyone understand how the solution is supposed to work.

Conclusion

In an industry where precision and clarity are paramount, AI and leveraging internal knowledge with a retrieval-augmented generation (RAG) tool offer a powerful combination. By transforming how software companies interpret and integrate data from multiple sources, they ensure that contracts are meticulously crafted and implementation teams are well-informed. As we continue to embrace these technologies, the question of "What did we just sign up for?" will be met with confidence and clarity, paving the way for successful and satisfying customer engagements.