Past Technologies

The year was 1995, the year that Microsoft released the Windows 95 operating system, a major milestone in personal computers. Windows 95 gave nontechnical people the opportunity to use inexpensive computers for personal and business purposes. It changed the way we worked and played. In addition, it made Bill Gates the wealthiest man alive from 1995 to 2017, now the second wealthiest after Jeff Bezos.

That same year (1995), Life Cycle Technology (LCT) developed and released the Proposal Solution System (PSS). It was exciting and an honor to be the lead computer

Technology advancements disrupt how companies and proposal development teams work.
programmer/analyst for PSS. The tool included one of the first Request for Proposal (RFP) parsers (shredders). However, the parsing process required the difficult practice of selecting parsing pattern parameters (e.g., #.#) prior to parsing. The parsing process often involved multiple parses until the correct pattern parameters were selected.

Nevertheless, PSS supported drag-and-drop requirements into a Compliance Matrix and Storyboards. The result was a huge time saver when working on large RFPs. PSS was only on the market for about three years, and the demise of the product was caused by various factors. Then again, PSS was so far advanced that it would be many years later before other proposal development tools would have similar capabilities.

Today’s Technologies

Today’s technological advancements provide the opportunity to improve productivity in proposal development and quality of proposals. And there are several different categories of proposal tools and solutions on the market from which to choose (e.g., Opportunity Identification, Proposal Management, Analysis Tools, Automation).

This article focuses more on Analysis Tools and Automation since this is my area of expertise, and less on Opportunity Identification and Proposal Management solutions.

Technology advancements disrupt how companies and proposal development teams work. The constant pressure to release more winning proposals is neverending, all while striving to control cost. This pressure forces management and teams to seek out better processes and technologies. Often there are some team members who are unwilling to adopt some of these new tools. There are a variety of reasons for slow acceptance: learning curve (not having time to learn), changes the proposal process, unknown risks, believing that manual process is better, and others.

Today’s proposal development tools provide automation and auto-assist capabilities. Automation completely generates your artifact (e.g., compliance matrix), while auto-assist includes automation but allows you to manipulate the results prior to generating your artifact. On the other hand, automated artifacts typically necessitate manual clean-up and modifications prior to release. Here are some examples of automation and auto-assist capabilities: generating a proposal outline, compliance matrix, responsibility matrix, requirement matrix, storyboards, and others.  More often, automation and auto-assist tools are used on the front-end of the proposal process.

There is a diverse collection of analytics tools for business and proposal development, such as tools to help with the Bid/No Bid process by identifying the RFPs concepts, legal risks, and requirements. An example of an analytic tool for the back-end of proposal development is a readability test that measures the readability level of a draft proposal and other documents using Flesch Reading Ease. Other back-end tools are currently available for ensuring that all requirements have been addressed and for measuring risks. Over the past few years we have seen fantastic advancements in proposal software tools.  They have continued to add greater capabilities, ease of use, and flexibility.

Future Technologies

As with my experience with PSS in the 1990s, being the first doesn’t always make you the winner. Proposal teams realize that adopting technological solutions too quickly invites risks that can impact their company’s bottom line. But not adopting soon enough gives advantages to their competitors who utilize tools and solutions that allow them to submit more and better proposals at a lower cost.

Many of these Future Technologies are currently being developed. Three software technologies that will have the most significant impact on (near) future business and proposal solutions are Natural Language Processing (NLP), Machine Learning (ML), and Artificial Intelligence (AI).  These technologies already exist in small measure in various proposal solutions and tools.  A few companies have tried to push the limits of these technologies too quickly, resulting in poor performance.

Natural Language Processing (NLP) is a subfield of Artificial Intelligence (AI). In the proposal world, NLP focuses on understanding content. This is huge! This means that the software can identify what the content is. NLP is already providing capabilities in proposal software tools, such as concept identification. My company is currently developing NLP-related tools, so my discussion in this area is limited. But I can say that I am extremely excited.

Machine Learning (ML) is also a subfield of AI. What makes ML powerful is that this technology improves over time without instructions from users. The current down-side is that when ML has small datasets, the performance is typically unacceptable. This is a real issue for proposal development, particularly for small companies. However, ML has its place in certain areas where processes are repeated often or where ML can be joined with other technologies to improve performance.

As technology advances, it disrupts existing processes.

Artificial Intelligence (AI) can be scary because we don’t really understand the impact of a Strong AI if or when it is developed. Strong AI is where the machine’s intelligence is functionally equal to humans’. Meanwhile, the term AI is over-used as a marketing tool in an attempt to make people believe that the technology or product is more advanced than it really is. For example, IBM Watson is a Question Answer (QA) computer system using NLP and a huge database.

Of the three technologies listed above, NLP appears to be the most promising for the short-term. NLP will make your proposal development tasks easier and quicker, but will not replace your expertise. We will continue to see companies move more toward ML, particularly with proposal management solutions where large datasets exist. And finally, we will continue to see companies use the term AI loosely to sell their software. However, no one knows if or when a Strong AI will be developed and how it will impact the world.

Summary

As technology advances, it disrupts existing processes. In other words, the way we do things today will be different as we use newer tools. Technology gives us greater capabilities to do more in less time. Nevertheless, at times it can cause us to feel separated from the intimate relationship with tasks of our past. For example, sometimes I miss the feel of drawing on a drafting table, but last year, I gave away my drafting table because I never used it.

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