Harnessing AI for Proposal Writing: A Deep Dive into Best Practices and Potential Pitfalls
In today’s tech-centric world, we see an increasing drive towards automation and the utilization of artificial intelligence (“AI”) across all sectors. One of the burgeoning areas is the use of generative AI in drafting proposal responses in government contracting.
Generative AI is a form of machine learning to create text or other media. The AI learns patterns and structure based on its input training data and then generates new data that has similar characteristics. But, like all technological advancements, it comes with its own set of challenges.
Three Primary Requirements for the Effective Implementation of AI:
- Vast reems of historical input data for the machine learning model to train the AI;
- Clean, accurate, and current data to yield proper insights; and
- IT infrastructure to support AI processing
The AI Wave in Contract Proposals
With the rapidly growing digital ecosystem, it seems inevitable that AI has found its way into proposal writing. Comparing this transition to the arrival of self-driving cars gives a good perspective. Both promise futuristic solutions, but the challenges they face are very present.
You wouldn’t trust a self-driving car to navigate a complex, unpredictable terrain without the necessary roadmaps and rules in place. Similarly, blindly relying on AI to draft proposals without current input data and human intervention could lead to myriad issues.
Best Practices to Effectively Utilize AI:
Current Date – Ensure the data input in the machine learning model is clean, accurate, and current. Remember the maxim, “Garbage in, Garbage out.”
Human Review – Maintain human intervention to keep in the loop for review and approval of any content generated by AI. This ensures accuracy and adherence to the proposal requirements in line with the company’s objectives.
Understand the Tool – The more proprietary an AI tool, the greater the potential risks of unknown responses. Contractors should have a clear understanding of how their chosen AI system works and what goes into its “black box” thinking.
Protect Intellectual Property – For ‘off-the-shelf’ AI, always check what the AI tool does with your proprietary input data. Questions arise such as whether the proposal content will be used to fine tune the AI tool further or if it might inadvertently end up in a competitor’s proposal.
Consider the Overall Value – Determine if the time saved using AI outweighs the time spent vetting its output, as well as the additional risks and costs associated with its use.
Potential Pitfalls Utilizing AI:
Legal Regulation and Clauses – The nature of federal contracts includes numerous clauses based Federal Acquisition Regulations (“FAR”). While AI is ‘learning’ it might overlook or misunderstand a clause, which could have significant legal repercussions.
However, this challenge can also be a potential long-term advantage. As AI tools develop and learn, they can better cross-check and ensure that no crucial clauses are missing from the proposal.
False Claims – There’s the danger of AI inadvertently writing about capabilities of a service or product that the company doesn’t actually offer, leading to potential legal complications.
Stale Data – Over time, some generative AI tools may deviate from their original programming, magnifying errors with each cycle, as well as data decay from old data not purged from the system.
The federal government has taken action to work with industry to develop a Blueprint for an AI Bill of Rights to help guide the design, development, and deployment of AI to protect the American public. The goal is to develop safe and effective AI systems.
While the integration of AI in proposal writing might be the future, it needs to be approached with caution. Just as we wouldn’t let an AI-driven vehicle on the road without thorough testing and safety measures, we shouldn’t blindly trust AI to draft proposals or run our businesses without significant human oversight.
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