The 2026 NFL Draft kicked off last night in Pittsburgh. Fernando Mendoza went first overall to the Raiders, the Jets loaded up with three first round picks, and the Rams turned heads by taking Ty Simpson at 13. But for me, the most interesting storyline of draft week had nothing to do with who got picked. It was 49ers GM John Lynch telling reporters that San Francisco has fully incorporated artificial intelligence into its player evaluation process. His reasoning was almost comically casual: if you aren’t using it, you’re already behind.
That quote should land differently depending on your industry. If you work in IT, cybersecurity, or any technical field where AI is reshaping how decisions get made, Lynch just described your situation too. He compared AI to planning a travel itinerary. You ask it for ideas, it gives you good ones, and then you (the person who actually understands what you’re looking for) decide which ones to follow. That framing is more useful than 90% of the AI strategy content I’ve seen from actual technology companies.
Every NFL team is using AI tools for scouting in 2026. Most organizations outside of football are too. The difference is whether your people have the skills to use these tools without creating new risks.
How NFL Teams Are Actually Using AI in the Draft
A Sportico investigation published this week put it bluntly: every NFL team is almost certainly testing AI tools in talent evaluation right now. Computer vision software pulls measurable stats from game film. Microsoft offers teams a chat interface to search through NFL Combine data. ESPN’s national NFL analyst Seth Walder told the publication that coding assistance might actually be the biggest use case, which makes sense when you consider the sheer volume of data these front offices are processing during draft prep.
The Minnesota Vikings’ interim GM Rob Brzezinski drew a distinction that’s worth sitting with. He said analytics allowed teams to gather massive amounts of information. AI actually analyzes it. That’s not the same thing. Analytics tells you what happened. AI tries to tell you what it means and what’s likely to happen next. For NFL draft rooms, that difference showed up in how teams evaluated prospects this cycle in ways that would have been impossible even two years ago.
The NFL’s own Next Gen Stats platform, powered by AWS, has been part of league operations since 2017. But generative AI and large language models pushed things into completely new territory over the past two years. According to ESPN’s reporting on AI and the draft, teams are now asking models to compare prospects within specific scheme fits, project production under certain usage patterns, and flag injury risk using biomechanical data from RFID chips embedded in players’ shoulder pads. A chip in a kid’s shoulder pad feeding data into a model that helps a GM decide whether to spend a first round pick on him. That’s the world we’re in.
The Caleb Downs Problem: When AI Fills the Gaps Humans Can’t
Ohio State safety Caleb Downs ended up going to the Dallas Cowboys at pick 11 last night. He’s a two time All American and won the Jim Thorpe Award as the nation’s best defensive back. But Downs refused to run the 40 yard dash at the NFL Combine and at his pro day, which left teams with a basic question they couldn’t answer through traditional evaluation: how fast is he?
In previous draft cycles, that uncertainty would have been a significant problem. This year, teams used AI to project his speed from game film data points, acceleration curves, and tracking chip information captured during college games. ESPN’s reporting described how AI analysis of Next Gen Stats data can reveal things that raw measurables miss entirely. They cited a prior example where the Miami Dolphins’ Jaylen Waddle ranked low on speed lists because he was running mid range routes and cutting back before reaching top speed. AI identified that his acceleration data showed the potential to go significantly faster than his recorded speeds suggested.
That kind of analysis matters because it reveals what traditional metrics can’t: context. A prospect’s 40 time tells you how fast they run in a straight line wearing shorts. AI analysis of game data tells you how fast they actually play football, which is a fundamentally different question.
The parallel to cybersecurity is obvious. Raw metrics (number of alerts, time to patch, vulnerability counts) tell you something. But context is what separates a useful security program from one that generates dashboards nobody reads. AI is getting better at supplying that context, whether you’re evaluating a defensive back or a network anomaly.
Where AI Still Gets It Wrong
For all the hype, AI’s track record with draft evaluation has some noticeable holes. Last year, several models severely undervalued a compact slot receiver who lacked straight line speed but thrived after the catch. The Buffalo Bills ignored the data, grabbed him late in the first round, and he put up Pro Bowl numbers. Spatial awareness, timing, and competitive toughness are traits that resist clean quantification. The models saw a slow receiver. The scouts saw a football player.
The edge rusher conversation this cycle showed the same tension. ESPN reported that AI analysis of Ohio State’s Arvell Reese, who went to the Giants at pick 5, revealed that he dropped into coverage on roughly half his snaps last season. On the plays where he actually rushed the passer, his efficiency was lower than both Texas Tech’s David Bailey and Miami’s Rueben Bain Jr. So the data says Reese might not reach double digit sacks as easily as those other guys. But the scouts still thought he was the best overall edge player in the class, and the Giants agreed.
Philadelphia 76ers GM Daryl Morey has what I think is the best framing for how organizations should treat AI analysis. He said they treat large language models “almost like one scout.” Not the head scout. Not the general manager. One voice in the room with a particular set of strengths and a particular set of blind spots. That’s a useful mental model for any organization figuring out where AI fits in their decision making.
What the NFL Draft Room Tells Us About Every Other Industry
The 2026 NFL draft room looks almost exactly like every other high stakes decision making environment being reshaped by AI right now. You’ve got humans with decades of domain expertise working alongside models that process information at a scale no person could match. The scouts still watch film. The AI helps them see patterns across thousands of data points they’d never correlate on their own. Neither is sufficient without the other.
This same dynamic is showing up in cybersecurity operations, IT governance and risk management, compliance auditing, and just about every other technical discipline. The organizations getting it right aren’t replacing their people with AI tools. They’re also not ignoring AI because their experienced staff thinks they don’t need it. They’re doing the harder work of figuring out integration, learning where human judgment adds something the model can’t provide, and where the model surfaces insights that humans would miss or take weeks to find manually.
The NFL figured this out faster than most industries because the stakes are public and immediate. A bad draft pick costs millions of dollars and shows up on the scoreboard every Sunday. In IT and cybersecurity, the stakes are just as high (sometimes higher), but the feedback loop is slower. A bad AI governance decision might not blow up for months. By then, the damage is already baked in.
The AI Skills Gap Is the Real Story
Lynch’s casual comparison between AI and vacation planning is telling because it reveals how normalized the technology has become. But normalized doesn’t mean understood. Using ChatGPT to plan a trip to Italy is low stakes. Using AI tools to evaluate personnel decisions, assess security risks, or automate compliance workflows is a completely different situation. The interface might feel similar, but the consequences of getting it wrong are not remotely comparable.
This gap between AI familiarity and AI competency is why the certification industry has moved so quickly over the past year. CompTIA launched SecAI+ to address AI security specifically. ISACA is rolling out AI focused credentials including the AI Audit, Implementation, and Risk certifications (AAIR, AAISM, and AAIA) launching in 2026. The IAPP has its Certified AI Governance Professional (AIGP) certification targeting professionals who need to manage AI risk within legal and regulatory frameworks.
These credentials exist because the industry recognized something that Lynch articulated without meaning to: everyone is using AI, and most people using it don’t have formal training in how to use it without creating new problems. NFL teams can afford to learn through trial and error because a missed draft pick is painful but survivable. In cybersecurity, trial and error with AI tools can mean a data breach, a compliance violation, or a vulnerability that an attacker finds before your team does.
AI Certifications That Actually Prepare You for This Reality
The AI certification space in 2026 is moving fast, and not every credential carries equal weight. If the NFL draft analogy holds, the question isn’t whether to invest in AI skills. It’s which investment gives you the best return for your specific career trajectory.
CompTIA SecAI+ targets security professionals who need to understand AI’s impact on cybersecurity operations. It covers how AI tools change threat detection, incident response, and vulnerability management. If you’re working in a SOC or any operational security role, this is the credential that speaks directly to how AI is changing your daily work. CompTIA built it because Security+ and CySA+ don’t address the AI dimension deeply enough, and the gap was showing up in how prepared new analysts were for actual SOC environments.
ISACA’s AI credential stack is taking a different approach with three separate certifications launching in 2026. The Certified in AI and IT Risk (AAIR) is the one that resonates most with the NFL draft parallel. It’s about evaluating AI risk within enterprise decision making, which is exactly what teams are doing when they decide how much to trust a model’s recommendation versus a scout’s gut instinct. ISACA wants professionals who can assess whether an AI implementation creates more risk than it reduces, which is a question every organization using these tools should be asking.
The IAPP AI Governance Professional (AIGP) sits at the intersection of AI and privacy law, covering the EU AI Act, NIST AI Risk Management Framework, and the growing patchwork of state level AI regulations in the US. This credential makes the most sense for professionals in compliance, legal, or policy roles who need to understand AI governance from a regulatory perspective. As AI tools become standard in enterprise workflows, someone in every organization needs to own the question of whether the way they’re using AI is actually legal and defensible. That’s what AIGP prepares you for.
A note on timing: The AI certification space is still young enough that being early matters. ISACA’s AAIR opened registration on April 15, 2026 and the first exams are expected in Q2. Getting certified before the market is saturated with holders gives you scarcity value, the same advantage that CRISC holders had when that certification was new and fewer than 10,000 people held it. Early movers in AI governance certifications will benefit from the same dynamic.
Frequently Asked Questions
How are NFL teams using AI for the 2026 draft?
NFL teams in 2026 are using AI across multiple areas of draft preparation. Computer vision analyzes game film to extract measurable stats. Machine learning models project player performance within specific team schemes. Biomechanical data from RFID chips in shoulder pads feeds AI systems that flag injury risk and fatigue patterns. Microsoft Copilot and AWS Next Gen Stats are integrated into team operations for data processing. The 49ers, Raiders, and Vikings have all publicly confirmed AI use in their scouting processes.
Can AI replace traditional scouting in the NFL draft?
No. AI in the 2026 NFL draft complements traditional scouting rather than replacing it. Models still struggle with traits like competitive fire, leadership, locker room fit, and motor, the intangible qualities that scouts evaluate through film study, interviews, and in person observation. The Bills proved this last year when they successfully drafted a receiver that AI models had undervalued because his best traits didn’t show up cleanly in data. The consensus among NFL front offices is that AI works best as one input alongside human judgment, not as a substitute for it.
What AI certifications are available in 2026?
The major AI certifications available or launching in 2026 include CompTIA SecAI+ (focused on AI and cybersecurity operations), ISACA’s AI certification stack including AAIR, AAISM, and AAIA (covering AI audit, implementation, and risk), and the IAPP Certified AI Governance Professional or AIGP (addressing AI privacy law and regulatory compliance). Each credential targets a different professional audience. SecAI+ suits security practitioners, ISACA’s credentials suit governance and audit professionals, and AIGP suits compliance and legal roles.
Why does AI in the NFL draft matter for IT professionals?
The NFL draft is a high visibility example of a pattern playing out across every industry: experienced professionals working alongside AI tools to make better decisions faster. The same integration challenges (knowing when to trust AI output, understanding its limitations, building governance around its use) apply whether you’re evaluating draft prospects or security alerts. IT and cybersecurity professionals who understand how to work with AI tools responsibly, and how to manage the risks those tools introduce, are the ones organizations need most in 2026.
What is the NFL Big Data Bowl?
The NFL Big Data Bowl is an annual analytics competition run by NFL Football Operations and powered by AWS. It invites data scientists, from college students to working professionals, to develop innovative approaches to football analysis using real player tracking data and Next Gen Stats. The 2026 competition focused on predicting player movement after the ball is thrown, and the winner was Lucca Ferraz from Rice University. The competition has produced metrics that NFL teams have adopted and incorporated into live game analysis, and it represents one of the clearest examples of how data science skills translate into real world sports applications.
CMO & Certification Guru | Training Camp
Mike McNelis is the CMO at Training Camp, where he combines a passion for technology with a hands-on approach to leadership. Beyond overseeing marketing strategy, Mike is actively involved in the technical side of the business — collaborating with clients, shaping learning solutions, and staying connected to the fast-changing world of IT and cybersecurity. He works closely with companies, government agencies, and individuals to help them achieve meaningful certification and workforce development goals.