The era of Artificial Intelligence (AI) is undoubtably here. For those still unfamiliar, AI is a series of algorithms that can be combined to create a matrix of decisions simulating logic and adjusting output based on data. In other words a series of combined programs all with one goal that continues to get more sophisticated with more information input. Another word for AI is machine learning because the algorithms improve as more data is gathered.
The most basic AI can be within the scope of a market matrix best known as retargeting funnel. One can set a simple algorithm program input to identify and auto-respond to people who showed interest in a post or advertising. Simultaneously, one can set a complimentary algorithm to gather data and analyze which demographics are responding most based off of a given data range. As more data is inputed to into the algorithms naturally the success ratio increases. Finally, one can layer another algorithm to take the data learned from both trials to develop a retargeting campaign to maximize advertising spend and only target those with the highest probability of interest.
This methodology is the core functionality that goes into Facebook’s AI advertising technology and other social media online advertising tools like Google analytics. This same concept is being researched by organizations interested in developing AI for RFPs. According to an article by Maurice Harary, Forbes council member, this may be the new wave in research and development in the AI industry.
AI for RFPs
Still, technology-enabled efficiencies are becoming an increasingly important part of competitive bidding. Prewritten responses should not be automatically added to a response without personal review, but they can save time and money when used strategically. Additionally, automatic tracking of metrics, key performance indicators (KPIs) and other information relevant to your industry and organization is an important tool to keep your bids informed and up to date with the latest information and intelligence.
Curated databases are slowly incorporating machine learning techniques to improve the relevance of search results and help you identify a perfect RFP opportunity. Again, personal attention is needed to fully implement these advantages, but directing your team to input customized search parameters such as geography, contract size, industry type, etc. can lead to a more optimized process of RFP opportunities for your organization to review.Click here read the full story.
The RFP process can be daunting for corporations with costly research and data gathering if done in-house. Firms are lowering cost by outsourcing research to private institutes and organizations. Furthermore new research in automation has made it possible to streamline using AI for RFP processes like key performance indicator (KPI) generation.
Some feel that we are far away from a full sophisticated automation model that completely removes humans from the process from start to finish on a winning bid. On the other hand innovative firms are already implementing high levels of AI algorithms and machine learning to achieve levels of sophistication that are above human logic with new research in quantum computing. Stock markets have almost completely replaced humans when integrating bids, regulations, and market values all into AI algorithms simultaneously computing thousands of transactions per second above human capacity.
Regardless of where you sit on the curve in relation to tech-savviness or regardless of ones options on AI replacing human interactions. One cannot the deny the vast opportunity and cost saving potential in integrating artificial intelligence in the most basic reasoning. Simply stated, organizations can integrate AI for RFPs in order to eliminate arduous tasks along the road to data gathering, identifying most relevant prospects, and analyzing success metrics and KPIs.
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