Engineering & Construction | Delivery Leadership

Build vs. Buy Assessment Prevents Millions Lost on Out-of-the-Box Solution 

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Project Evaluation Prevents Loss on Out-of-the-Box Solution

Houston-based National Engineering, Procurement, and Construction company relies heavily on responding to RFPs and bid effectively to win new business. However, the bidding process for these RFPs is exceptionally detail-driven, frequently including project estimates down to the exact nail count. 
Historically the company utilized a series of specialized people with in-depth industry knowledge and complex Excel spreadsheets to create project estimates in response to RFPs. The RFP response process required an overhaul, the company partnered with AIM Consulting to determine a better course of action to either purchase an out-of-the-box solution or build a custom application to align specifically with their needs.

Key Results

  • Produced a Multi-Department Data Flow impacting 15 Departments
  • Produced a Roadmap with Phased Approach from Current RFP Response Posture to an Automated Posture Leveraging Artificial Intelligence (Included Series of 13 Core Milestones)
  • Recommendations Concluded a Savings of Over $3M Over 3 Years
  • Produced a Build vs. Buy Assessment (which included total cost of ownership and business alignment of building a custom web application versus licensing a bundle of Salesforce products focused around Salesforce CPQ)

The Situation

The organization had spent a decent amount of resources researching and demoing out-of-the-box solutions, specifically with IBM Watson and the Salesforce suite of products including CPQ and Einstein. The team was excited about the potential for when they received an RFP, analyzing historical data and then leveraging a natural language processing tool to generate an estimate.
Unfortunately, this is easier said than done. While the team had a vision in place, natural language processing technology and the organization’s internal processes hadn’t quite advanced entirely to those levels. As a result, AIM Consulting would need to analyze both off-the-shelf solutions and educate the company on a feasible solution to leveraging natural language processing.

The Results

AIM Consulting recommended a custom-built application with a roadmap to properly leverage natural language processing to respond to RFPs. This solution offers a scalable vision that tackles bid estimation to RFPs and would result in savings in excess of $3 million over the next three years in licensing and operational fees of products that may not align with business need or otherwise provide expected value. Additionally, the recommendation rationalized avoiding licensing costly AI tools until the team had a means of capturing historical data in place via web application. These AI products would sit on the shelf until they had the captured data to train the models.
Creating a roadmap helped highlight this recommendation, presenting a detailed project timeline and limiting spend based on incomplete information. AIM Consulting advised, when ready, the team should consider Azure ML since it is already a part of their tech stack and would have redundancies with other AI tools.

Project Timeline

AIM’s Approach

AIM Consulting conducted an assessment first, starting by analyzing historical forecasts and interviewing stakeholders involved in producing estimates for RFPs. AIM Consulting determined an off-the-self solution wouldn’t suit the needs of the business development teams’ use of artificial intelligence.
AIM Consulting reviewed in-depth the product offerings of Salesforce and IBM, participating in the analysis, product demos, and pricing. Then focused inwards, assessing corporate IT ecosystems, architecture standards, artificial intelligence footprints, and reviewing internal applications and previous implementations.
AIM Consulting’s build vs. buy assessment resulted in the recommendation to avoid licensing AI tools/software (IBM Watson, Salesforce Einstein) for their specific project since their team was focused on building out their historical data (web application) first.

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