AI in IT Bid Risk Management
A Powerful Tool (If You Keep It in Check)
AI is making waves in IT bid risk management, helping teams analyse contracts, flag risks, and even predict trouble before it happens. It’s fast, efficient, and never needs a coffee break. But let’s be clear - AI isn’t a replacement for human judgment. If you’re not putting the right checks and balances in place, you might end up trusting a system that doesn’t fully understand the nuances of your deals.
Where AI Can Help Right Now
AI is already proving its worth in a few key areas:
Spotting Patterns in Past Bids - AI can sift through historical data and highlight common pitfalls, like pricing mismatches or risky contract clauses.
Checking Contracts for Red Flags - It can scan RFPs and agreements to catch terms that deviate from company policy or regulatory requirements.
Analysing the Competition - AI can pull insights from publicly available data to give you a clearer picture of competitor strategies and pricing trends.
Drafting Risk Summaries - Instead of starting from scratch, AI can generate initial risk assessments, saving bid teams time and effort.
Yet, while all this sounds impressive—and it truly is—it's important to remember that AI remains fundamentally dependent on the quality and depth of the data fed into it. Garbage in, garbage out, as the old IT saying goes. I recall a situation early in my risk management career, long before AI entered the scene, when an incorrect assumption about a pricing baseline nearly derailed a major bid. Now imagine trusting AI blindly, without the critical scrutiny human experience provides. Terrifying, isn’t it?
The Catch: AI Needs Oversight Here’s the thing - AI is great at processing data, but it doesn’t think the way a seasoned risk manager does. If you’re using AI in bid risk management, you need a formal quality assurance process to ensure its recommendations hold up. That means:
Human Review is Non-Negotiable - AI can flag risks, but a risk professional needs to verify whether those concerns are real or just false alarms. Remember, AI isn't immune to the digital equivalent of jumping at shadows.
Transparency is Key - AI should explain why it flagged something as a risk. If it can’t clearly articulate this, don’t just take its word for it.
Watch for Bias - AI learns from past data, which means it can inherit blind spots. Regular audits are necessary to ensure it’s not over- or underestimating risks based on flawed historical patterns. It’s a bit like teaching a kid: they pick up both good habits and bad, sometimes without you noticing until it's too late.
Test, Test, and Test Again - Compare AI-generated recommendations against actual bid outcomes to fine-tune its reliability. This is more than a box-checking exercise—it's essential due diligence.
Keep Governance in Place - AI should support your existing review processes, not bypass them. Make sure AI-driven insights still go through the usual risk assessment and approval steps. The worst thing you could do is give AI a free pass and end up with unchecked assumptions guiding your bids.
Let's also not underestimate the power of intuition and experience in risk management. An AI can process thousands of data points instantly, but can it sense when something just "feels off"? Probably not. During my years at IBM and Fujitsu, there were countless instances where my gut instinct, informed by experience, caught something crucial—nuances the algorithm would never detect.
Final Thought: AI is an Assistant, Not a Decision-Maker AI can give bid risk managers a serious advantage, but it’s not infallible. Think of it as an extra set of hands - great for heavy lifting but not something you’d leave in charge. If you use AI wisely, with proper checks in place, it can help your team work smarter and faster. Skip the oversight, however, and you might inadvertently introduce more risk than you mitigate.
As exciting as AI advancements are, always remember this simple principle: technology should amplify human judgment, never replace it. Keep your risk management processes robust, sceptical, and above all, human-centric.