Too Many AI Frameworks? Let’s Get Back to Basics

By David Linthicum

The artificial intelligence (AI) landscape has evolved remarkably in recent years, with new solutions, tools, and insights emerging at a breakneck pace. AI is no longer a fringe technology—it’s a strategic asset underpinning businesses across industries. However, with this rapid growth comes a problem that’s often overlooked: the proliferation of so-called AI frameworks. Every consultancy, technology vendor, and even individual organizations seem eager to contribute their version of "The AI Framework," promising a structured path to AI success.

Don't get me wrong—having frameworks to help organizations better understand and integrate AI into their operations isn’t inherently a bad thing. In fact, it's essential for businesses venturing into this complex domain to adopt disciplined approaches. The problem lies in the sheer volume of frameworks we’re seeing today. From Microsoft’s AI Maturity Model to PwC’s AI Augmentation Spectrum, and from Gartner’s Autonomous Systems Framework to MIT's Human-in-the-Loop Model, it feels like we’re drowning in an avalanche of methodologies competing for attention (See Figure).

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Make no mistake, promoting frameworks is a way for many organizations to claim authority or relevance in the rapidly expanding AI space. But instead of clarifying the path forward, this influx of frameworks is only adding to the noise and confusion. Enterprises are left scratching their heads, wondering which framework they should adopt—or even questioning if they need to adopt one at all.

Frankly, I believe this framework frenzy needs to stop. The energy spent creating increasingly convoluted models for AI adoption would be better directed towards addressing the basics—the harder, messier, and less glamorous aspects of enterprise success with AI.

A Saturated Market of AI Frameworks

To highlight the scale of this problem, let’s consider the diversity of frameworks currently on offer:

• Microsoft's AI Maturity Model tracks organizational progress through stages of Assisted, Augmented, and Autonomous Intelligence.
• PwC’s AI Augmentation Spectrum defines six levels of human-AI collaboration, from “Advisor” to “Self-Learner.”
• Deloitte’s Augmented Intelligence Framework categorizes AI's role in driving productivity into Automate, Augment, and Amplify modes.
• Gartner’s Autonomous Systems Framework breaks work into Manual, Assisted, Semi-Autonomous, and Fully Autonomous categories.
• MIT’s Human-in-the-Loop Model champions the idea of keeping human oversight in the loop during critical AI processes.

Each of these approaches has merit in its own way, but the sheer abundance of distinct methodologies inundates decision-makers in enterprises attempting to formulate long-term AI strategies. Instead of clarity, they find complexity. Rather than inspiring bold action, these frameworks sow doubt and delay.

Adding to the confusion is the fact that many of these frameworks overlap in scope, terminology, and goals. Enterprises are left asking questions like: Which framework best fits our organization? Should we be focusing on “maturity,” “augmentation,” or “productivity”? Is human oversight a guiding principle, or do we fully empower AI? These questions are not only distracting, but they also pull organizations away from confronting the practical challenges of successfully implementing AI.

The Framework Hype Machine

This wave of frameworks has become part of the broader AI hype machine. Some consultancies and vendors develop these frameworks not out of necessity but as a marketing tactic to boost their thought leadership profiles. By introducing new lingo or diagrams into the conversation, they attempt to position themselves as orchestrators of the AI revolution.

But here’s the inconvenient truth: frameworks don’t solve the hardest problems facing enterprises in AI adoption. Here's a scarier reality for these organizations to grapple with: most large enterprises still struggle with fundamental tasks like maintaining clean, actionable data. Many leaders don't even understand what data they have, where it resides, or how it's being used. Security and governance—the cornerstones of successful AI integration—are often afterthoughts rather than core priorities.

So, do we really need another framework to "revolutionize" AI adoption? Hardly. What we need is a back-to-basics approach that helps enterprises tackle the unglamorous but critical challenges that determine success or failure when adopting AI.

What Enterprises Should Really Be Focusing On

Here’s the truth: achieving success with AI doesn’t require yet another shiny framework. What it requires is a foundational understanding of the building blocks that make AI work in the real world. These are the areas where enterprises need to focus their energy:

Data Management and Strategy

AI is only as good as the data it’s trained on and fed. Yet many organizations fail to implement robust data management strategies. Data silos still plague enterprises, leading to fragmented and incomplete information that hinders AI’s potential. Enterprises must put data governance, data hygiene, and data integration front and center in their AI strategies. If you don’t know what your data is, don’t expect AI to tell you.

Security and Privacy

As highlighted by the growing threat of shadow AI (unauthorized AI usage within organizations), enterprises are woefully underprepared to address the security implications of AI adoption. Sensitive data used to train or run AI systems can be inadvertently shared, misused, or exposed. Without strong security frameworks and compliance systems in place, even the most advanced AI implementation risks becoming an expensive liability.

Governance

Governance isn’t just about setting guardrails for AI usage; it’s about creating systems of accountability and transparency that ensure AI operates ethically and aligns with business objectives. With regulatory scrutiny of AI growing worldwide, organizations must take governance seriously—from how AI models are trained to how decisions made by AI are monitored and audited. Governance shouldn't be an afterthought; it should be the foundation on which AI strategies are built.

Talent and Skills

Much has been said about the growing skills gap in AI, but it cannot be overstated. In many cases, enterprises don’t have the necessary expertise on staff to effectively design, implement, or maintain AI systems. Sporadic training programs won’t cut it. A deliberate and sustained effort to cultivate in-house AI talent is required to navigate the complexity of AI technologies.

The ROI Challenge

Organizations often invest heavily in AI without a clear understanding of how it will drive return on investment (ROI). To truly succeed, enterprises must measure the impact of their AI initiatives, focus on what drives tangible business outcomes, and treat AI as a strategic enabler rather than a silver bullet.

Cut the Hype and Focus on What Matters

The current obsession with generating new frameworks feels like a distraction from the real, pressing issues enterprises face with AI adoption. What organizations need isn’t more theory—they need practical guidance to solve their most persistent problems.

Instead of trying to create the next viral framework or outdo competitors in complexity, those at the forefront of AI—consultants, vendors, researchers—should prioritize helping businesses get the basics right. Your AI might be augmented, autonomous, or amplified, but if your data is a mess or your security policies are lax, it won’t matter. Fancy charts and buzzwords won’t shield you from the consequences of poorly executed AI implementations.

Let’s stop chasing hype and focus on what makes AI successful in the real world. Enterprises aren’t asking for frameworks to tell them what AI ought to do. They’re asking for clear answers to how AI can work—securely, efficiently, and with meaningful results. Yes, AI frameworks have their place, but let’s not mistake the map for the territory. The hard work of achieving AI maturity starts, and ends, with the basics.