What Exactly is this AI Thing? And What Does it Mean for the World of Proposals?

Artificial Intelligence (AI) is a hot topic in the proposal field right now. In the last year or so, the Association of Proposal Management Professionals (APMP) has hosted multiple webinars on AI at both the international and chapter levels. At Bid and Proposal Con 2019, there were even back-to-back sessions dedicated to the topic—and I was honored to share some of my own insights as part of in Karthik Koutharapu’s panel, “Leveraging AI for Persuasive Proposal Writing.”

From the audience and panelist reactions to the various questions, it’s clear that many of us have some angst about the potential negative effects on the industry. But some of us are more optimistic about the ways AI may be able to improve our work-life balance. In this article, I provide an overview of AI and present some of the ways I am hopeful that it will improve our industry.

What is Artificial Intelligence (AI)?
AI is the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. AI often revolves around the use of algorithms.

But what is an algorithm? An algorithm is a detailed, step-by-step instruction set or formula for solving a problem or completing a task. Algorithms are everywhere! A recipe for making food is an algorithm, the process of folding a shirt or a pair of pants is an algorithm, and even the logical reasoning you use when you play tic tac toe is an algorithm.

In computing, an algorithm is a set of instructions that a computer can execute. Many AI algorithms are capable of learning from data. This branch of AI—which is based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention—is called machine learning.

Machine learning algorithms can enhance themselves by learning new heuristics (i.e., strategies, or "rules of thumb," that have worked well in the past). Machine learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future. Simply put, machine learning algorithms work by building a mathematical model based on some sample data, known as "training data," to make future predictions or decisions.

How is AI Currently Being Used in Proposals?
As demonstrated by the number of Bid and Proposal Con 2019 vendors already boasting AI capabilities, many companies are already leveraging AI to automate and enhance the proposal development process. Currently, most of these tools are focused on automating Question and Answer type proposals (largely commercial applications) and language analysis (cross-cutting applications). In the paragraphs to follow, I highlight four proposal tools that leverage AI.

IBM’s Watson and Tone Analyzer Application Programming Interfaces (APIs). As we learned from Karthik at Bid and Proposal Con 2019, IBM uses the AI power of Watson and Tone Analyzer APIs to develop more effective Executive Summaries. This proprietary tool uses linguistic analysis to detect and score three types of tone from the proposal content: emotion, social tendencies, and language style. This tool allows users to upload content to quickly analyze and revise the language for improved client impact.  

VisibleThread. VisibleThread offers language analytics and AI solutions that are used by 7 of the top 10 U.S. Government contractors. Companies use VisibleThread to increase efficiency, quality check proposals, and drive more compliant content. Some of the most useful features of VisibleThread include: compliance gap analysis, readability and single tone of voice analysis, theme discovery and analysis, and concept tracking.

reDock. reDock uses AI and machine learning to automate content and data search tasks. The tool enables users to find quickly and easily the most suitable information across all of a company’s data repositories and software systems. This search engine filters through existing documents to find profiles, corporate references, past answers, and/or relevant approaches and presents the right section of information, rather than the entire document. This eliminates the time-consuming effort of sifting through hundreds of pages of proposal text for the right content.

RFPIO. RFPIO offers an intelligent answer library and auto-responses powered by AI. With RFPIO, a company maintains their content in a repository called the Answer Library. When a user searches for the right content or contributors for a project, the tool makes a recommendation based on previous activity.

What are Some of the Challenges with AI and Propsals?
Although AI tools are beginning to enhance our capabilities, to be more effective, AI must become a more seamless part of our established processes. Currently, the available AI tools are not particularly flexible, and they don’t integrate well with our existing tools and workflows. Additionally, as AI tools evolve, our companies will need to be prepared to provide the legwork necessary to enable these tools to access accurate training data.

Not Seamlessly Integrated. Currently, most of the common AI language analysis tools require an extra step to perform the desired tasks. The existing proposal process must be stopped, documents must be uploaded, and then the desired analyses can be performed. This is because most of these tools are not integrated into each company’s existing proposal management and version control tools (e.g., Privia, SharePoint, Virtual Proposal Center (VPC)). To be more user friendly, and to increase the usefulness, these tools need to be better integrated into the existing tools being used for proposal development so that the analyses and added functionality are seamless. 

AI Requires Accurate Training Data. This challenge is multi-part. First, the AI tools will need access to the company’s data, so this will require integration effort on the part of IT staff. And the more historical data the company has, the more time and effort this is likely to take. Next, the AI tools need to understand how effective the previous content was. If companies use their customer relationship management (CRM) tools effectively to maintain accurate records of proposal content, customer debriefs, and any resulting contracts—then this step may not be too difficult. However, if companies store this information in disparate places using tools that don’t communicate well, this task will become more complex, difficult, and time consuming. Finally, to make sure that the AI continues to learn and predict effectively, companies will need to make sure the data remains up-to-date moving forward.

What is the Future for AI in Proposals?
Although AI has already started to impact existing proposal processes, we have really only begun to scratch the surface of its true potential in the field. Because of its unemotional capabilities to make predictions, AI has huge potential in supporting bid/no-bid decision so companies can more effectively target the opportunities with the highest probabilities of win. In addition, with its capability to quickly analyze massive amounts of data to make predictions and decisions, AI can likely help improve price analysis, price-to-win, and price development processes. Further, AI seems to have huge potential to support the selection of proposed products and solutions.

Will AI eventually replace all of us and independently write the proposals for our companies? I think most of us agree that this will never happen in our lifetimes. However, I do believe that AI tools of our future will ultimately empower our proposal teams. But to be more effective, AI must become a seamless part of our established processes—it must be flexible and integrate with our existing tools and workflows. We are not there yet, but we are getting there. Leveraging AI effectively in the future, I believe we will increase the efficiency of our overall proposal process by focusing on the right opportunities, at the right time, with the right strategies.

Written by Ashley Kayes, CP APMP
Senior Proposal Consultant, AOC Key Solutions, Inc. (KSI)


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