While asking small organizations to protect themselves against well-resourced nation-states and cybercriminal enterprises seems practically unfair, there are several basic steps that organizations can take to strengthen their cyber defenses. Government entities have created lists of impactful no-cost and low-cost steps that organizations can take to strengthen their cyber resilience, such as CISA’s guidance to small and medium-sized businesses, FBI’s Winter SHIELD initiative, Australian Cyber Security Centre’s Essential Eight, and NIST’s Small Business Cybersecurity Corner.

The marketplace for solutions and services for small and medium-sized organizations has also grown over the last few years. More vendors are targeting tools specifically to these audiences, including managed detection and response, network anomaly detection, email security, endpoint protection, secure backup, identity and access management, and vulnerability management offerings that are simpler to deploy and maintain than traditional enterprise platforms. These tools are often easier to use, geared towards organizations who may not have deep technical expertise, and are priced more economically than larger enterprise solutions. In many cases, security features that were once available only to large organizations are now bundled into common business software and cloud services, making it more practical for smaller organizations to improve baseline security without building a large internal team. This includes network detection capabilities that have traditionally been out of reach for smaller organizations. Purpose-built commercial tools are now making that same class of detection more accessible, without the operational complexity that once made enterprise network detection and response (NDR) impractical for teams without dedicated security staff.

Utilization of AI has also made monitoring networks, identifying suspicious activity, and prioritizing potential remediation actions easier. When evaluating AI solutions for your organization, prioritize tools that ensure data privacy, offer user-friendly interfaces, and maintain transparency by clearly explaining the reasoning behind their recommendations. For network monitoring specifically, look for tools whose AI establishes a baseline of what normal traffic looks like for your specific environment rather than relying solely on static threat signatures. Threats like lateral movement, data exfiltration, and command-and-control activity produce behavioral patterns in network traffic that rules-based tools can miss. AI that learns your network’s normal patterns can surface those anomalies earlier and with more relevant context. Also consider whether the tool processes data locally or sends traffic to an external platform for analysis, as this has direct implications for data privacy and regulatory exposure. Organizations should also consider how AI tools handle sensitive business information, whether they integrate well with existing systems, and whether there is appropriate human oversight to validate alerts and recommended actions before taking major operational steps.

I often say that there is a role in cybersecurity for everybody, and we need everybody to play their role. Whether you are a user or responsible for cybersecurity in your organization, there are steps you can take every day to build your resilience against cyberattacks. Do not look at the issue as a light switch that is either on or off, but rather as a bank of dimmer switches where you are moving at least one switch in the upward direction daily. Over time, you will build resilience across your enterprise and make it harder for our adversaries to conduct attacks and minimize the impacts of attacks if they do occur.

Nitin Natarajan, Former Deputy Director, CISA

Guy Howard, Chief AI Engineer, IntelliGenesis LLC

The post Cybersecurity Is Not a Light Switch appeared first on IntelliGenesis LLC.

Drawing on twenty years of Navy service as an aerospace experimental psychologist and his tenure as chief of staff for the Advanced Distributed Learning Initiative, Phillips examines the structural barriers standing between the Department’s serious frameworks, scholarship programs, and selection tools and a truly integrated cyber career system.

He walks through why personnel selection, qualification management, assignment, performance measurement, and retention all need to be treated as parts of the same problem, and he lays out concrete recommendations to support Cyber Command 2.0 and the goals voiced at the recent Cyber Workforce Summit 2.0.

Read the full article on War on the Rocks to see his proposed path forward.

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COLUMBIA, MD — IntelliGenesis LLC®, a provider of advanced AI/ML cybersecurity, training, and data-driven solutions, today announced that its DryDock platform has achieved “Awardable” status through the Platform One (P1) Solutions Marketplace.

The P1 Solutions Marketplace is a digital repository of post-competition, readily awardable pitch videos that address the Department of Defense’s most significant challenges in hardware, software and service solutions. All awardable solutions have been assessed through complex scoring rubrics and competitive procedures and are available to Government customers with a Marketplace account.

DryDock is a generative AI platform developed by IntelliGenesis that automates the analysis and contextualization of software vulnerability exploitability within containerized environments. Rather than producing a list of vulnerabilities ranked by severity score alone, DryDock determines whether a specific CVE is actually exploitable within a given container, configuration, and operational context, and provides structured, evidence-based justification for each finding tied directly to authoritative source data.

The platform integrates CVE ingestion, software bill of materials correlation, source code reachability analysis, exploit intelligence, and structured justification into a single automated workflow. Internal testing within the company’s IG Labs division has produced full contextual analysis of container images in under five minutes, a process that can take analysts hours or days when performed manually.

“DryDock was built on a principle we apply across everything we develop: that AI-driven analysis must include traceability and auditability by design, not as an afterthought,” said Angie Lienert, President and CEO of IntelliGenesis. “Security teams across the DoD are facing more than 100 new vulnerabilities published every day. The bottleneck is not finding them. It is determining which ones are actually exploitable and documenting why. DryDock eliminates that bottleneck and gives decision-makers the evidence they need to act quickly and with confidence.”

IntelliGenesis was recognized among a competitive field of applicants to the P1 Solutions Marketplace whose solutions demonstrated innovation, scalability, and potential impact on DoD missions. Government customers interested in viewing the video solution can create a P1 Solutions Marketplace account at https://p1.dso.mil/marketplace.

About IntelliGenesis

IntelliGenesis LLC® delivers advanced AI/ML cybersecurity, training, and data-driven solutions powered by mission-focused experts. A veteran-led, woman-owned firm founded 18 years ago from national security AI labs, the company protects U.S. defense and intelligence networks through proprietary products including CYBERSPAN® for AI-powered perimeter defense and KrakenAI for edge-deployed generative AI. Through its IG Labs® division, IntelliGenesis transforms cutting-edge research into operational capabilities, while IntelliCademy provides customized mission training and simulation systems to enhance warfighter readiness. Learn more at intelligenesisllc.com.

About the P1 Solutions Marketplace

The P1 Solutions Marketplace is a digital repository of post-competition, readily awardable pitch videos that address the Department of Defense’s most significant challenges in hardware, software and service solutions. All awardable solutions have been assessed through complex scoring rubrics and competitive procedures and are available to Government customers with a Marketplace account. Government customers can create an account at https://p1.dso.mil/marketplace.

Media Contact:

Neal Stein

Technology PR Solutions

Office: (321) 473-7407

nealjstein@techprsolutions.com

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COLUMBIA, MD — IntelliGenesis LLC®, a provider of advanced AI/ML cybersecurity, training, and data-driven solutions, today announced it has been awarded a contract by an Intelligence Community customer to deliver AI training and professional development services in support of mission-critical national objectives. The contract expands the company’s role in building practical, mission-driven AI capabilities across the defense and intelligence workforce.

The award reflects a growing recognition across the national security community that successful AI adoption requires more than tools and technology. It requires a workforce that understands how to apply AI effectively, responsibly, and in mission-relevant contexts. IntelliGenesis will deliver training ranging from foundational AI literacy to advanced AI/ML applications aligned to operational needs.

Through IntelliCademy, its training and development arm, IntelliGenesis has built a proven track record of designing and delivering customized mission training and certification programs aligned with DoD cyber workforce standards. The new contract builds on that foundation, extending the company’s reach into AI-specific workforce development for national security customers.

“This award validates what we have long believed: that the AI-enabled workforce is just as critical as the technology itself,” said Angie Lienert, President and CEO of IntelliGenesis. “The mission demands more than deploying new tools. It demands people who understand how to apply them responsibly and effectively in operational environments. We are proud to support that effort.”

As a mid-tier defense and intelligence contractor supporting national security customers across the country, IntelliGenesis brings a unique combination of operational experience and technical depth to workforce development. The company’s training programs are informed by real-world mission requirements and delivered by practitioners who have built and deployed AI and cybersecurity solutions in classified environments.

IntelliGenesis protects U.S. defense and intelligence networks through proprietary products including CYBERSPAN® for AI-powered perimeter defense and KrakenAI for edge-deployed generative AI. The company’s IG Labs® division transforms cutting-edge research into operational capabilities, while IntelliCademy provides customized mission training and simulation systems to enhance warfighter readiness.

For more information about IntelliGenesis and its AI training capabilities, visit intelligenesisllc.com.

About IntelliGenesis

IntelliGenesis LLC® delivers advanced AI/ML cybersecurity, training, and data-driven solutions powered by mission-focused experts. A veteran-led, woman-owned firm founded 18 years ago from national security AI labs, the company protects U.S. defense and intelligence networks through proprietary products including CYBERSPAN® for AI-powered perimeter defense and KrakenAI for edge-deployed generative AI. Through its IG Labs® division, IntelliGenesis transforms cutting-edge research into operational capabilities, while IntelliCademy provides customized mission training and simulation systems to enhance warfighter readiness. Learn more at intelligenesisllc.com.

Media Contact:

Neal Stein

Technology PR Solutions

Office: (321) 473-7407

nealjstein@techprsolutions.com

The post IntelliGenesis Awarded Contract to Deliver AI Training and Professional Development for Intelligence Community Customer appeared first on IntelliGenesis LLC.

I/ITSEC draws approximately 20,000 visitors each year and serves as the premier platform for DoD to advance warfighting readiness through innovations in training, simulation, and joint collaboration. The conference features roughly 200 scientific papers and presentations organized across six technology areas, along with keynotes and meetings with senior military, government, and industry leaders.

IntelliGenesis’s Hank Phillips, Director of Strategic Growth for R&D, serves as chair of the Human Performance, Analysis, and Engineering (HPAE) subcommittee, one of six committees responsible for shaping the conference program. The conference is organized by the National Training and Simulation Association (NTSA) with the support of more than 100 volunteers from government, industry, and academia.

Phillips and the I/ITSEC leadership team recently completed an initial review of more than 400 abstract submissions at a meeting in Fort Worth, Texas. That pool will ultimately be narrowed to approximately 200 presentations, papers, and panel discussions for the conference.

“Having a seat at the table in selecting and curating the content for I/ITSEC gives IntelliGenesis a unique vantage point on where military training and simulation technology is headed,” said Phillips. “It also reflects the depth of expertise our team brings to the human performance space.”

For more information about I/ITSEC 2026, visit iitsec.org.

The post IntelliGenesis Plays Key Role in Shaping I/ITSEC 2026, the DoD’s Premier Training and Simulation Conference appeared first on IntelliGenesis LLC.

COLUMBIA, MD — IntelliGenesis LLC, a provider of advanced AI/ML cybersecurity, training, and data-driven solutions, today announced it has achieved Cybersecurity Maturity Model Certification (CMMC) Level 2 certification. The milestone validates IntelliGenesis’s compliance with the 110 security controls defined in NIST SP 800-171 and demonstrates the company’s commitment to protecting Controlled Unclassified Information (CUI) across its defense and intelligence programs. 

CMMC Level 2 certification requires organizations to implement and document comprehensive cybersecurity practices that protect sensitive government information shared with defense contractors. The certification is assessed by an independent, accredited third-party organization (C3PAO) and is becoming a prerequisite for DoD contracts involving CUI.  

The certification ensures that IntelliGenesis’s partners and government customers can confidently collaborate with the company on mission-critical programs requiring secure handling of sensitive data. As a mid-tier defense and intelligence contractor supporting national security customers across the country, IntelliGenesis has made cybersecurity a foundational priority across every aspect of its operations. 

“Achieving CMMC Level 2 certification demonstrates our commitment to safeguarding Controlled Unclassified Information and strengthening cybersecurity across the Defense Industrial Base,” said Angie Lienert, President and CEO of IntelliGenesis. “We build and deploy technology that protects some of the most sensitive networks in the country. Our customers and partners need to know that the security standards we apply to our own operations are just as rigorous as the ones we help them achieve. This certification confirms that.” 

“Earning this certification required our entire team to demonstrate that cybersecurity is embedded in how we operate every day, not just how we document our policies,” said Jeremiah Jensen, Chief Operating Officer of IntelliGenesis. “That consistency is what our government customers count on when they trust us with sensitive mission data.” 

IntelliGenesis protects U.S. defense and intelligence networks through proprietary products including CYBERSPAN® for AI-powered perimeter defense and KrakenAI for edge-deployed generative AI. The company’s IG Labs® division transforms cutting-edge research into operational capabilities, while IntelliCademy provides customized mission training and certification programs aligned with DoD cyber workforce standards. 

For more information about IntelliGenesis and its cybersecurity capabilities, visit intelligenesisllc.com. 

About IntelliGenesis 

IntelliGenesis LLC delivers advanced AI/ML cybersecurity, training, and data-driven solutions powered by mission-focused experts. A veteran-led, woman-owned firm founded 18 years ago from national security AI labs, the company protects U.S. defense and intelligence networks through proprietary products including CYBERSPAN® for AI-powered perimeter defense and KrakenAI for edge-deployed generative AI. Through its IG Labs division, IntelliGenesis transforms cutting-edge research into operational capabilities, while IntelliCademy provides customized mission training and simulation systems to enhance warfighter readiness. Learn more at intelligenesisllc.com. 

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But there’s a structural problem underneath many of these efforts: Leaders often lack a reliable way to determine whether their workforce is actually prepared to execute.

For years, agencies have relied on job titles, levels, and certifications as signals of capability. Those signals worked when roles were narrower and requirements were simpler. AI changes that equation. Today’s AI-related roles require a blend of strategy, governance, risk management, compliance awareness, and operational execution. A generic credential or title rarely captures that complexity.

As a result, organizations risk staffing AI initiatives based on incomplete information. The consequences show up quickly: stalled projects, uneven adoption, misaligned training investments, and leadership roles filled without validated competency.

This is exactly why IntelliGenesis built IntelliCademy.

IntelliCademy is designed to close the gap between workforce credentials and mission performance. Instead of adding another badge to an already crowded landscape, IntelliCademy aligns training directly to defined work roles under the Department of Defense’s Cyber Workforce Framework (DCWF), including roles within the Data/Artificial Intelligence Cyberspace Workforce Element.

Take the AI Transformation Leader certification. It maps to DCWF roles such as AI Innovation Leader (902) and AI Adoption Specialist (753), but it goes further than role alignment. The program emphasizes applied exercises and demonstrated proficiency. Candidates are required to show they can lead, govern, and operationalize AI initiatives, not simply recall terminology.

That distinction matters.

AI adoption is no longer theoretical. Agencies are deploying real systems into mission environments. Leaders need confidence that their AI leads can align strategy to operations, manage risk appropriately, and drive implementation across complex organizations. That level of confidence does not come from memorization-based testing. It comes from competency mapped to real-world tasks.

IntelliCademy was built around that principle: training that reflects the work, assessment that reflects execution, and certification that signals measurable capability.

As AI continues to reshape federal missions, workforce qualification systems must evolve with equal rigor. Organizations that invest in competency-based development will execute faster and with greater clarity. Those that rely on vague signals will struggle to keep pace.

To learn more about IntelliCademy and upcoming AI certification offerings, visit intellicademy.ai.

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Dr. Walker, a retired U.S. Navy Medical Service Corps Commander, brings more than 20 years of experience leading AI and R&D programs across the Department of Defense, including roles at the Defense Health Agency, Joint AI Center, and Office of Naval Research. At DHA, he served as Deputy Branch Chief for Science & Technology Portfolio Management and Program Director for SBIR/STTR programs, driving enterprise-wide AI adoption and modernization initiatives.

In his new role, Dr. Walker will lead IntelliGenesis’ strategic innovation portfolio, guiding AI/ML product development, dual-use technology acceleration, and advanced R&D strategy.

“Pete has a rare mix of operational credibility and deep technical leadership in AI and analytics,” said Angie Lienert, President and CEO of IntelliGenesis. “He knows how to take advanced work and make it mission-ready, which is exactly where our customers place the highest value. We’re excited to have him help guide our innovation strategy across AI/ML cybersecurity, training, and data-driven solutions.”

“What attracted me to IntelliGenesis is that they’ve been doing mission-focused AI work since before it was a buzzword,” said Dr. Walker. “This is a company built by veterans who understand that technology only matters if it works in the field. The opportunity to help shape the next generation of AI-powered defense capabilities was too compelling to pass up.”

Dr. Walker has authored more than 100 peer-reviewed published articles in AI, machine learning, and health informatics. He is best known for leading Project Salus, a DoD analytic platform deployed during the COVID-19 pandemic that provided real-time modeling of disease spread and vaccine distribution, informing senior DoD leadership, FEMA, and DHS during the national crisis.

About IntelliGenesis

IntelliGenesis LLC delivers advanced AI/ML cybersecurity, training, and data-driven solutions powered by mission-focused experts. A veteran-led, woman-owned firm founded 18 years ago from national security AI labs, the company protects U.S. defense and intelligence networks through proprietary products including CYBERSPAN® for AI-powered perimeter defense and KrakenAI for edge-deployed generative AI. Through its IG Labs division, IntelliGenesis transforms cutting-edge research into operational capabilities, while IntelliCademy provides customized mission training and simulation systems to enhance warfighter readiness. Learn more at intelligenesisllc.com.

Media Contact:

Neal Stein

Technology PR Solutions

Office: (321) 473-7407

nealjstein@techprsolutions.com

The post IntelliGenesis Appoints Dr. Peter B. Walker as Chief Innovation Officer appeared first on IntelliGenesis LLC.

Large Language Models (LLMs) like GPT or LLaMA often struggle with logic because they are trained to continue patterns they’ve seen in data. In effect, that means they’re very good at saying “yes” to familiar relationships, but not nearly as good at knowing when to say “no.” For example, once a model learns that smoking increases the risk of lung cancer, it might wrongly conclude that the absence of smoking guarantees the absence of lung cancer, or that having lung cancer must mean a person was a smoker. 

Putting LLM Logic to the Test 

To see how this plays out in practice, the team put several well-known LLMs through a simple test. They pulled together widely accepted cause-and-effect statements from medical and environmental science, such as “High blood pressure implies increased risk of stroke,” and then generated eight logical variants for each one by flipping or negating parts of the statement. For each version, the models were asked if the statement was correct.  

The models were generally good at recognizing the original correct statement. However, they struggled with the twisted ones, especially when negations were involved or cause and effect were reversed. In other words, they were much better at agreeing with likely-sounding conclusions than at rejecting ones that do not follow. Even the bigger, more powerful models fell into these logical fallacy traps. Being large helped, but did not  fix the core logic problem. 

The Solution: Dual Reasoning 

The paper proposes a simple but powerful solution: a dual-reasoning training framework. Alongside teaching a model what is true, it also needs to be taught what is  not true. That means the model is rewarded when it supports a valid conclusion and penalized when it treats an invalid conclusion as if it were true.  

This approach draws on findings from cognitive science, which show that people learn just as much from disconfirmation and counterexamples as they do from confirmation. By training models not only to affirm valid statements but also to actively reject invalid ones—to effectively learn to say “no”—AI systems become more resilient, trustworthy, and aligned with human logic. 

Why This Matters 

For AI to play a meaningful role in high-stakes decisions, it needs to be more than a sophisticated autocomplete that tends to agree with itself. This requires checking its inferences against contradictory evidence, recognizing when a rule has exceptions or rests on weak premises, and being able to say, “No, that does not follow,” when the logic breaks. 

The dual-reasoning framework outlined in this paper is an early step in that direction. At IntelliGenesis, we are proud to be at the forefront of research that builds AI that is not just powerful, but also principled and safe for critical applications in science, healthcare, and beyond. 

Read the full paper on arXiv. 

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In August of this year, MIT generated a lot of controversy with its report, The GenAI Divide, which dropped a shocking statistic: “Just 5% of integrated AI pilots are extracting millions in value, while the vast majority remain stuck with no measurable P&L impact.” In other words, 95% of AI projects “fail” in some capacity. Likewise, data from other recent research and analysis tell a similar story: 

• RAND found that over 80% of AI projects fail. 

• S&P Global says 46% of AI projects are abandoned before production. 

• McKinsey reports that over 80% of firms have not achieved bottom-line impact from generative AI. 

• Gartner’s 2025 Hype Cycle for Artificial Intelligence notes that less than 30% of leaders report satisfactory returns from their AI investments.  

However, the consensus across the research used for this article is that these failures aren’t a problem with the technology, but a fundamental misunderstanding of what AI is, what it can do, and what it takes to make it succeed.  

The “Vehicle Problem”: We Don’t Even Know What We’re Talking About 

One of the biggest hurdles in AI is the ambiguity of the word itself. In their book AI Snake Oil, Princeton researchers Arvind Narayanan and Sayash Kapoor draw a comparison to an imaginary world where the word “vehicle” is used to describe everything from a bicycle to a rocket ship.  

“Conversations in this world are confusing. There are furious debates about whether or not vehicles are environmentally friendly, even though no one realizes that one side of the debate is talking about bikes and the other side is talking about trucks.” 

In fact, even the research is often vague about what “AI” means and a large share of the survey results presented in this article focus specifically on generative AI. As a result, “AI” has become synonymous with generative AI tools like ChatGPT for many people, creating the impression that this type of AI can solve any business problem. However, other types of AI, such as predictive AI, computer vision, and expert systems are often better suited to specific organizational challenges than generative AI. For instance, the generative AI that writes marketing copy is very different from the predictive AI used to forecast equipment failures.   

Hype vs. Reality 

The “Vehicle Problem” highlights the confusion surrounding any emerging technology, which leads to a disconnect between a new technology’s promise and its business reality. Gartner’s Hype Cycle provides a popular framework that’s used to visualize the path that technology products travel from their initial conception to their eventual (although not always) commercial viability and mainstream adoption.  

Gartner Hype Cycle for Artificial Intelligence 2025 

Source: https://www.gartner.com/en/newsroom/press-releases/2025-08-05-gartner-hype-cycle-identifies-top-ai-innovations-in-2025  

The Five Phases of the Hype Cycle 

The Innovation Trigger phase is reserved for the truly novel next-generation AI concepts that are still primarily in research labs. This phase generates excitement and high expectations among early adopters and is characterized by high-risk, early proof-of-concept stories and prototypes. 

Hype is greatest during the Peak of Inflated Expectations, when there is intense media attention, as well as widespread public excitement and high expectations for a new technology. However, hype isn’t always bad because it generates the interest and awareness that’s necessary to foster innovation. But, as the name suggests, it also leads to unrealistic expectations, overinvestment, and short-term thinking. This phase sees some success stories, but a lot more failure. 

The next phase is the Trough of Disillusionment, or the reality check phase. This is where the excitement wears off as many implementations fail to deliver on their initial expectations. The companies that progress from here are the ones that build the operations that make results repeatable.  

The Slope of Enlightenment follows, where the real-world benefits of the technology become more widely understood and implemented. Finally, technologies arrive at the Plateau of Productivity, where returns are predictable and mainstream adoption takes hold.  

The Gartner Hype Cycle helps to explain why so many AI projects fail as they move through the life cycle phases, especially during the transition from the Peak of Inflated Expectations into the Trough of Disillusionment.  

The Top Reasons for AI Failure 

Across the research, we found the most common causes of AI deployment failures can be attributed to at least one of these business domains: leadership, data readiness, and engineering/infrastructure. 

1. Leadership-Driven Failures 

The single biggest driver of AI failure, cited by nearly all (84%) of the AI practitioners interviewed in the RAND study is a lack of clear leadership and strategy. At the Peak, the pressure to “do something with AI” is immense. Executives launch initiatives without a clear strategy or an understanding of the business problems that AI can actually solve. 

The end result is a model that provides no business value because it can’t solve the problem, it solves the wrong problem, or it optimizes the wrong metric. Likewise, the MIT study found that leaders often focus on the wrong priorities, chasing visible wins in sales and marketing, while the high-ROI, back-office automation opportunities that deliver true process-level transformation are ignored.  

Another major issue is the underestimation of the time, cost, and complexity involved with data preparation, model training, and integration. According to McKinsey’s State of AI report, the management activity that has the most significant impact on an organization’s bottom line when deploying AI projects is tracking  well-defined Key Performance Indicators (KPIs), yet fewer than one in five are tracking these metrics. 

Perhaps this is because, as OECD’s cross-country report on AI adoption in enterprises found, many managers have a “plug-and-play conception of adoption, expecting AI to be a commodity technology they can easily integrate into core business processes.” This disconnect creates what the MIT report calls a “pilot-to-production chasm,” where enthusiasm gets pilots started, but a lack of strategic patience and focus prevents them from being integrated into core workflows.  

Leaders don’t need to be AI experts, but they must understand that successful AI implementation requires a top-down vision, a clear roadmap, and well-defined KPIs. Leaders should work closely with technical teams to define an explicit, enduring business problem and be ready to redesign their workflows and processes around AI. 

2. Data-Driven Failures 

After leadership failures, data is the next biggest culprit in disappointing AI deployments. According to the RAND report, much of the problem lies with poor data quality. Organizations often lack sufficient, fit-for-purpose data and often rely on misaligned legacy datasets collected for compliance or basic record keeping that don’t have the context needed to train an effective model.  

The OECD report adds that many businesses have not historically collected the right information and what data they do have is often siloed, fragmented, or stored in incompatible formats. This issue is often exacerbated by managers who don’t understand what data is needed in the first place, and are afraid of sharing critical data with AI systems due to competitive or legal concerns.  

Gartner reports that 63% of organizations lack suitable data practices and predicts that 60% of AI projects will be abandoned through 2026 simply because they are not supported by AI-ready data. Data problems usually surface in the Trough. This is why Gartner puts so much weight on evolving data management for AI as part of climbing the Slope of Enlightenment in their AI Hype Cycle. To succeed, a project must be supported by a dataset that is a complete and faithful representation of the specific use case, complete with every pattern, error, and outlier needed to properly train and run the model.  

3. Engineering & Infrastructure Failures 

Even with strong leadership and good data, AI projects can still fail due to poor technical implementation and inadequate infrastructure, which are generally exposed during the Trough phase. 

Internal engineering teams often lack the capability to build adaptive systems, which the MIT report calls the “learning gap.” This is the absence of a persistent memory that allows a system to absorb feedback, remember user preferences, and improve over time. Until enterprise tools close that gap, the report argues that user trust and core workflow impact will be limited.  

In addition to the learning gap, a shortage of specialized skills is also a persistent challenge, cited across many of the reports. The deficit goes beyond just data scientists to include machine learning (ML) engineers, who are needed to deploy and maintain models, and data engineers, who build the data pipelines. Without the talent to turn theoretical models into production-ready systems, project remain stuck in the proof-of-concept stage.  

Finally, underinvestment in infrastructure is another major roadblock to successful AI projects. The RAND report notes that organizations consistently fail to invest in the operational infrastructure (MLOps) needed to deploy, monitor, and maintain AI models in production. S&P Global’s report on AI infrastructure reinforces this, finding that 37% of organizations feel their infrastructure limitations prevent them from keeping pace with AI advancements. The cost of this infrastructure is also a major factor, with the same report noting that budget has become the number one driver of project failure, cited by 35% of respondents. The specialized hardware required, such as GPUs and high-performance storage, is expensive and can strain IT budgets, leading to projects being abandoned before they deliver value. 

Beyond the Technology: Building the Foundation for Success 

Technology is rarely the bottleneck to successful AI deployment on its own. The research says the fix is mostly organizational and operational: leadership that owns a business KPI and a roadmap, data that’s fit for the job and governed, and engineering/infrastructure that make reliability and cost visible every day. By moving beyond the hype, understanding the different “vehicles” of AI, and committing to the hard work of organizational change, companies move onto the Slope of Enlightenment where leaders can begin to unlock the transformative potential they’ve been promised with AI. 

Lead by Learning: “Introduction to AI for Managers” 

IntelliGenesis’s IntelliCademy training division developed Introduction to AI for Managers to guide leaders through the persistent challenges of AI and toward successful deployments. This executive-level course focuses on demystifying AI for non-technical decision-makers, covering the very topics that lead to project failure: understanding AI fundamentals, establishing governance, aligning projects with mission needs, and differentiating between hype and realistic capabilities. 

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