How AI Can Help VARs Find Opportunities, Navigate & Win – Use Case #1

Introduction

Public sector value-added resellers (VARs) face a host of challenges, from shrinking margins to increasingly complex customer requirements. Traditional approaches to opportunity identification, pricing, and solution development struggle to keep pace. Artificial intelligence (AI) is poised to revolutionize how VARs find opportunities, navigate and win in the public sector.

This comes at an opportune time in the public sector, given the pressure to change driven by the Department of Government Efficiency (DOGE).

For VARs seeking to remain ahead of the curve, now is the time to explore how AI can help. By leveraging AI to identify the most winnable opportunities, enhance product bundling, and optimize pricing, VARs can gain a significant edge in an increasingly competitive landscape.

How can AI surface the most promising leads tailored to unique strengths? Uncover hidden product synergies that competitors overlook? Optimize pricing for every bid, based on real-time market dynamics?

Over the coming weeks, we’ll post a blog mini-series envisioning how public sector VARs can harness AI to transform their business. The mini-series will explore AI use cases to:

• simply find more opportunities in the strike zone
• build winning product combinations
• find the highest winning price

We’ll wrap up with a post the benefits of AI in context of DOGE-inspired change.

First AI Use Case for Public Sector VARs – Simply Finding the Opportunity

Traditional Approach: The manual process of finding opportunities involves scanning known sources and using relationships, which can be inefficient and miss potential opportunities.

An AI-powered opportunity identification solution addresses these challenges head-on. By using advanced natural language processing and machine learning techniques on a vast database of past bids, wins, and related documents, an AI-enhanced system can surface the most relevant opportunities far more efficiently than manual keyword searches and human intuition alone. This allows VARs to quickly home in on the leads that best align with their unique strengths and solution offerings, without getting bogged down in irrelevant or low-probability options. This scaled scanning and qualification process increases not only not only the volume of opportunities a VAR can pursue, but also prioritizes them according to quality, leading to a higher win rate.

Summaries of Opportunities

Given the often extensive volume of documents associated with procurement opportunities, AI can greatly expedite the review process by generating concise summaries. This capability allows VARs to quickly grasp the essentials of an opportunity without the need to manually sift through numerous attachments and detailed documents. By providing summarized overviews, AI helps to save valuable time and resources, enabling faster decision-making and prioritization of promising opportunities.

Comprehensive Opportunity Identification

In addition to analyzing a wider array of data sources, the AI solution can extract key details and context from unstructured text that would be difficult and time-consuming for humans to parse at scale. This includes elements like specific personnel experience, past performance examples, and win themes from prior successful bids. By matching these granular insights against new opportunities, the system can surface leads that closely align with a VAR’s distinct strengths and proven success factors, even if they would be overlooked by traditional keyword searches.

Enhanced Relevance Matching

AI can use natural language processing (NLP) to understand the context and requirements of procurement documents, matching them with the VAR’s unique strengths and offerings. For instance, by finding keywords and phrases related to “cloud security” in procurement documents, AI can help a VAR specializing in cloud solutions pinpoint relevant opportunities, improving the match precision by a certain percentage.

Predictive Opportunity Forecasting

AI can analyze trends and patterns to forecast upcoming procurement requirements before they are formally announced. For example, by monitoring government department budget allocations and spending patterns, AI might predict an upcoming need for infrastructure upgrades, allowing VARs to prepare bids in advance and engage stakeholders early, potentially doubling their chances of winning the contract.

 

**Scott Keough brings over 30 years of leadership experience in B2B SaaS, working with firms serving VARs, integrators, and OEMs. His expertise spans planning, business development, sales, and service delivery for cloud, IT infrastructure, and AI offerings. With a proven record of bootstrapping startups to profitability and positioning them for rapid growth, Scott excels at setting strategy, architecting solutions, building teams, and providing governance and guidance—qualities that uniquely position him to contribute significantly as a board member to firms in the reselling industry. Scott co-founded a procurement SaaS firm where he most recently was instrumental in the acquisition of his firm by a JMI Equity private equity-owned platform company, Unanet.