DOGE will have an enduring impact on public sector contracting. Fortunately, the AI revolution in federal contracting is here, and forward-thinking VARs have a once-in-a-generation opportunity to get ahead of the curve.

Increased Efficiency and Transparency Requirements

DOGE-driven mandates often emphasize greater efficiency and transparency in government contracting. The AI-driven solutions described in this series, especially related to pricing optimization, could see a heightened relevance due to such mandates. AI’s ability to provide transparent, data-backed pricing and bundling strategies could directly align with DOGE’s efficiency and compliance standards, positioning AI-enabled VARs as preferred partners in federal procurement.

Enhanced Data and Reporting Standards

If DOGE strengthens data management and reporting standards, VARs leveraging AI would have a distinct advantage. The AI tools described—particularly those utilizing Natural Language Processing (NLP) for summarizing procurement documents and predictive analytics—would inherently meet or exceed these data transparency and reporting benchmarks. Such AI systems would also facilitate the rapid adaptation to new DOGE-driven reporting requirements, potentially reducing compliance costs and improving speed-to-market for VARs.

Streamlined Procurement Processes

If DOGE drives streamlined procurement processes, VARs can reduce cycle times and complexity. The series highlights AI’s capability to identify opportunities efficiently, analyze procurement documents swiftly, and predict emerging requirements. These capabilities are perfectly aligned with a streamlined procurement landscape, potentially increasing the number of viable opportunities identified by VARs and enabling quicker response times to new procurements.

Shifts Toward Outcome-based Procurement

DOGE often encourages outcome-based procurement strategies. Emphasis on predictive pricing, optimized product bundling, and identifying relationship affinities in this series positions AI-enabled VARs well for outcome-oriented contracts. VARs utilizing AI to anticipate client requirements and align their offerings with expected outcomes could gain substantial competitive advantages under DOGE’s strategic shifts.

Heightened Emphasis on Competitive Fairness

If DOGE introduces initiatives aimed at enhancing competitive fairness and reducing entrenched incumbent advantages, the AI-driven opportunity identification and relationship affinity analysis outlined could be particularly beneficial. AI tools could help VARs understand how past awards might be influenced by relationships versus objective performance, allowing them to compete more strategically and fairly under the new DOGE directives.

The future belongs to those who harness the power of AI to work smarter, move faster, and compete more effectively.

**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.

Traditional Approach: Traditionally, VARs have relied on human analysis to estimate a winning price or margin. This process is time-consuming and often based on anecdotal experiences and historical knowledge, which may not accurately reflect current market dynamics.

AI-Enhanced Approach: AI can significantly enhance the pricing strategy process by analyzing vast datasets of past award information, current market trends, competitor pricing, and more. This approach allows for the identification of best pricing strategies that maximize the likelihood of winning bids at the best possible margins. Machine learning algorithms use factors such as time of year, buying history of the agency, and competitor behavior to predict winning prices with higher accuracy.

Precision in Predictive Pricing

AI’s analysis of historical bid data, incorporating variables like the purchasing patterns of specific agencies and seasonal budget fluctuations, enables more effective strategies. For instance, analysis might reveal that bids within 2-5% of the historical average are a certain percentage more likely to win, helping VARs to fine-tune their bids.

Cost Optimization

AI can pinpoint cost-saving opportunities within the supply chain, improving margins without compromising competitiveness. For example, optimizing logistics based on AI insights could reduce delivery costs by a certain percentage. AI can also analyze opportunity data to gain access to bigger OEM discounts by finding promising OEM partner program candidates for a given VAR based on the VAR’s existing capabilities, complementary OEM programs, and award trends.

Competitive Gaming Insight

Understanding competitive trends becomes powerful with AI. If a competing VAR is seen to be on a losing streak, AI can predict their likelihood to price aggressively in future bids. Conversely, winning streaks may show a competitor’s pricing confidence, guiding strategic pricing adjustments. For instance, AI might suggest pricing strategies to outmaneuver a competitor expected to bid low to break a losing streak.

Opportunity Identification Based on Relationship Affinities

AI can effectively highlight opportunities historically awarded to specific bidders, hinting at possible relationship-driven decisions. For example, if a product has consistently been awarded to the same bidder, this might indicate a special relationship. AI can advise whether to compete aggressively for such bids or focus efforts elsewhere, based on patterns that suggest whether these trends are based on performance, relationships, or other factors.

**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.

Traditional Approach: Finding the most advantageous combinations of products often relies on the VAR’s knowledge of available OEM programs, certifications, and discounts. This process is inherently limited by the VAR’s specific experiences and relationships.

AI-Enhanced Approach: AI can analyze historical data on past awards involving various combinations of products to find patterns and combinations that have been successful. By using information about OEM programs, certifications, and available discounts, AI can recommend combinations that offer competitive advantages. Moreover, AI can uncover hidden synergies between products that may have been overlooked, thereby providing VARs with unique bundling opportunities that can outmatch competitors.

Data-driven Bundle Optimization

AI can analyze vast datasets to show which product combinations have historically led to successful bids. To illustrate, it might find that combining cybersecurity software with specific hardware increases the win rate due to complementary functionality, enabling VARs to create more compelling bids.

Dynamic Discount Leveraging

By understanding specific details of OEM discount programs and how they have been successfully applied, AI can suggest the most effective combinations of discounts and products. An AI system might identify a strategy that leverages end-of-quarter OEM discounts in conjunction with bulk purchase discounts, effectively reducing costs for certain product bundles.

Certification and Program Advantage Utilization

AI can identify opportunities where a VAR’s specific certifications or program memberships give them a distinct advantage, such as offering exclusive products or additional services. This could lead to situations where, for example, the combination of AI insights with membership in a specific OEM’s platinum program allows a VAR to offer a unique combination of products and services, increasing the bid’s attractiveness by a certain percentage.

**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.

The Legacy of Separation

In most organizations today, AI and cybersecurity are still managed in silos. Cybersecurity remains predominantly the responsibility of the IT function, with Chief Information Security Officers (CISOs) leading efforts to guard networks, infrastructure, and sensitive data. Meanwhile, AI development is often decentralized, with various business units—such as marketing, operations, or finance—initiating their own pilot projects or proofs of concept to drive automation and analytics.

This separation was tolerable in the initial stages of AI adoption. However, as AI systems mature and proliferate across customer-facing platforms, internal operations, and decision-making engines, they introduce entirely new attack surfaces that cybersecurity teams are ill-equipped to monitor without strategic alignment. In turn, AI solutions themselves are vulnerable to a new breed of threats that exploit not hardware or software, but logic, data, and model behavior.

AI Expands the Attack Surface

Unlike traditional IT systems, AI-driven solutions are built upon models that learn and evolve over time. This dynamic nature introduces unique vulnerabilities outside the scope of conventional cybersecurity frameworks:

• Algorithmic Bias: AI models trained on unrepresentative or biased data can make discriminatory decisions, posing reputational and legal risks. Malicious actors may even manipulate training data to introduce bias deliberately.
• Model Drift: Over time, an AI model’s performance can degrade as real-world conditions shift. Attackers can exploit this drift, feeding the model with misleading data until it begins to make incorrect predictions or recommendations.
• Identity and Access Management: As AI tools become embedded in customer experiences—think chatbots, recommendation engines, or automated loan approvals—they require access to sensitive data. Poorly secured APIs, identity mismanagement, or insufficient encryption can open the door to data theft or manipulation.
• Data Poisoning: Threat actors may introduce corrupted data into AI training sets to subtly skew outcomes, causing models to behave unpredictably or unsafely once deployed.

These issues extend beyond the firewall and cannot be contained solely within the traditional purview of IT security. They require a rethinking of how AI and cyber functions collaborate and co-evolve.

Cyber Needs AI to Defend at Machine Speed

Conversely, cybersecurity itself is becoming increasingly dependent on AI to address the scale and speed of modern digital threats. Human analysts can no longer keep up with the volume of security incidents, the sophistication of threat actors, or the velocity at which attacks unfold. Cyber defense must operate at machine speed—detecting, analyzing, and responding to threats in real time.

AI and machine learning play a pivotal role in enabling this transformation:

• Pattern Recognition and Anomaly Detection: AI models can detect deviations from baseline behavior across users, applications, and systems—flagging threats that would otherwise go unnoticed.
• Threat Simulation and Intelligence: AI can simulate attacks to test system defenses, as well as aggregate threat intelligence from disparate sources to inform proactive defense measures.
• Automated Response and Remediation: Advanced AI systems can trigger containment protocols, revoke access, or quarantine compromised systems autonomously – shrinking response times from hours to milliseconds.

This AI-enabled cyber defense ecosystem is essential to keep pace with threats. However, without aligning AI development practices and security protocols, the same AI systems intended to protect the enterprise may themselves become targets.

The Case for Strategic Convergence

To address these intertwined challenges, companies must stop viewing AI and cyber as functional silos and parallel tracks and instead pursue an integrated strategy that unites them from design to deployment. This involves three critical shifts:

• Secure-by-Design AI Development – AI systems should be architected with cybersecurity in mind from the start. This includes:
  • Implementing model explainability to understand and audit decision-making processes.
  • Designing robust data governance frameworks to ensure training data is accurate, protected, and free from manipulation.
  • Embedding privacy and access controls directly into AI workflows and APIs.
  • Stress-testing models under adversarial conditions to understand failure modes and vulnerabilities.
• AI-Powered Security Operations – Security operations centers (SOCs) should be equipped with AI-driven tools that:
  • Continuously monitor system behavior using machine learning.
  • Detect complex threats that span multiple vectors or timeframes.
  • Automate triage, escalation, and resolution processes using intelligent playbooks.
• Unified Governance and Compliance – Governance frameworks must evolve to encompass both AI and cyber. Boards and executives must establish:
  • Common policies governing AI model development, deployment, and usage.
  • Shared accountability between CISOs, Chief Data Officers, AI leaders, and business unit leaders.
  • Clear audit trails and compliance mechanisms for both cyber regulations (e.g., GDPR, HIPAA) and AI ethics.

Executive and Board-Level Engagement

Achieving this convergence is not merely a technical challenge; it requires cultural and organizational transformation. Left to their own devices, cyber and AI leaders may continue to operate in functional silos, hampered by competing priorities and unclear mandates. That is why active involvement from the board and executive leadership is essential.

Boards must ensure that AI and cyber risks are understood as enterprise-level risks, with implications for reputation, regulatory compliance, and operational resilience. They should demand regular reporting on AI model integrity and cybersecurity posture and hold leadership accountable for cross-functional collaboration. Boards may also have to consider committee changes to add or modify mandates to strengthen oversight.

CEOs and CFOs must allocate resources accordingly—not just funding individual projects but investing in shared infrastructure, talent, and governance models that enable secure and scalable AI deployment.

A New Digital Imperative

In a digital-first world, scalability cannot come at the expense of security, and security cannot slow the pace of innovation. The only way to achieve both is through the integrated evolution of AI and cybersecurity.

AI systems will continue to redefine customer experience, business intelligence, and operational efficiency. But these systems must be trustworthy, protected, and resilient. Meanwhile, cyber defense must keep pace with machine-speed threats—requiring the very AI capabilities being developed across the enterprise.

The organizations that thrive in the next phase of digital evolution will be those that stop treating AI and cybersecurity as separate priorities—and instead, embrace them as co-dependent pillars of a smarter, safer, and more scalable future.

**Steve Hill has more than 38 years of experience in technology-enabled change and business transformations. He is known for driving sustainable and profitable growth through industry-leading innovation and strategic investments. Steve was the very first Vice Chairman for Innovation & Investments at KPMG and across the entire Big Four.  Following his role as Global Head of Innovation, Steve recently retired from KPMG with his most recent position as Regional Advisory Leader, One Americas. He is now focused on bringing his expertise in innovation, investments, digital transformation, cyber and generative AI in service to corporate boards of directors.

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.