Everything a CTO needs to know about AI

Over the last few weeks, we’ve had numerous conversations with CTOs about their AI strategy. We hear use cases around AI for developer productivity to creating customer-facing value to building models to general business operations. While the applications vary widely, one constant remains: there is significant information asymmetry regarding AI that currently exists in our industry.

This guide offers CTOs a concise resource for confidently navigating the AI ecosystem, starting with understanding the very basics.

Let’s jump in!

Understanding the key AI terms

Artificial Intelligence (AI)

AI is the broad umbrella term encompassing any technologies that enable machines to mimic human intelligence–whether that’s solving problems, recognizing patterns, making decisions, or learning from data.

CTOs should invest in this if:

• Your business handles significant unstructured data (text, images, audio)
• You need to automate complex cognitive tasks requiring human-like judgment
• Your competitive landscape includes AI-enabled challengers
• You have mature data infrastructure and engineering excellence

When to skip: If your challenges are better solved with traditional software, or you lack foundational data infrastructure, focus on building out other capabilities while monitoring AI developments in your industry.

Machine Learning (ML)

ML is a subfield of AI. Instead of hard-coded rules or specific programming, ML systems learn patterns from data. This happens through labeled or unlabeled data, where the system will identify correlations or structures and use these patterns to make decisions. This is a shift from telling a computer exactly how to solve a problem to letting it figure out the approach by examining examples.

ML powers specific applications like recommendation engines, predictive maintenance, and fraud detection systems.

CTOs should invest in this if:

• You have specific prediction, classification, or pattern recognition problems
• Your data is structured and available in sufficient quantities
• You need systems that continuously improve with more data
• Your business value comes from data-driven insights and automation

When to skip: If your challenges don’t involve pattern recognition or prediction, or if you lack quality datasets, traditional algorithmic approaches may be more appropriate and cost effective.

Deep Learning (DL)

Deep learning is a subset of machine learning. It uses multi-layered neural networks (often called ‘deep neural networks’) to automatically learn increasingly abstract representations of data at each layer. This approach yields what we’d consider ‘state-of-the-art’ results in areas like computer vision (image recognition) and natural language processing.

What makes it ‘deep’? The depth refers to having many layers of neurons (dozens or even hundreds), allowing the network to learn extremely complex patterns.

Neutral networks are the core architecture that powers deep learning. They’re directly inspired by the structure of the human brain, involving layers of interconnected nodes (neurons) that adjust ‘weights’ based on training data. Weights are numerical parameters that determine how strongly each input or neuron in a previous layer contributes to the neuro’s output in the current layer. Think of weights as the knobs the training process tunes to make the network’s predictions more accurate.

CTOs should invest in this if:

• You work with highly complex unstructured data (high-resolution images, video, audio)
• Your problems have resisted solution through traditional ML approaches
• You have access to substantial computing resources (GPUs/TPUs)
• You can acquire or have specialized AI talent on your team

When to skip: If you lack the specialized infrastructure, massive datasets, or AI expertise needed for deep learning, start with simpler ML approaches that require fewer resources and less specialized knowledge.

Neural Networks

As mentioned above, neural networks are the specific computational architecture underlying deep learning. Unlike other ML algorithms (decision trees or SVMs), neural networks use interconnected layers of artificial neurons that process information through weighted connections, mimicking brain structure.

CTOs should invest in this if:

• You need to evaluate AI vendor capabilities with technical precision
• Your team is building custom AI solutions requiring architectural decisions
• You’re assessing specialized AI talent requirements
• You need to understand the hardware implications of your AI strategy

When to skip: For most CTOs, a high-level understanding is sufficient unless you’re directly involved in building custom neural network solutions. Focus on business applications rather than implementation details if you’re primarily leveraging existing AI platforms.

Reinforcement Learning

Reinforcement learning is a specialized branch of machine learning where an agent learns to make optimal decisions by interacting with its environment. Unlike supervised learning, where a model learns from a labeled dataset, an RL agent is not given the ‘correct’ answers in advance. Instead, it explores different actions and observes the consequences in the form of rewards or penalties. Over time, the agent refines its strategy to maximize reward.

CTOs should invest in this if:

• You need to optimize complex systems with many interacting variables
• Your problems involve sequential decision-making under uncertainty
• You can create simulated environments for training before real-world deployment
• You’re developing autonomous systems that must adapt to changing conditions

When to skip: RL represents an advanced AI capability with significant implementation complexity. Unless your specific business challenges align with sequential decision optimization, this should be a later-stage consideration after success with more straightforward ML approaches.

Agentic systems and autonomous agents

‘Agents’ or ‘agentic systems’ are software entities that can autonomously execute tasks, make decisions, and even coordinate with other agents or services to achieve broader objectives. They’re essentially Large Language Models (LLMs) operating in a loop, designed to overcome the limitations of standalone LLMs.

While ChatGPT provides a familiar and accessible example of an LLM, agents can take this foundation further by adding a critical feedback loop. These systems perceive their environment, make decisions based on the context, and take action to accomplish a specific goal without constant human interaction.

The ELI5 version: Think of them as a robot helper in a computer. It looks around at what’s going on in its environment, figures out the best thing to do next, then does it. If the results are good, it remembers that and does it again.

Agents can play a critical role in the SDLC through:

• Automating complex workflows: Agents can automatically orchestrate tasks across multiple systems, freeing developers and operations teams from repetitive labor.
• Adapting decision-making: By continuously learning from feedback, agents can optimize processes and proactively suggest improvements.
• Rapidly prototyping: With agent frameworks, you can integrate specialized AI components (like computer vision or NLP) without building everything from scratch.

AI is just another strategic technology investment

The truth is, AI won’t solve every problem. Some applications are genuinely transformative—automating tedious workflows, boosting developer efficiency, or uncovering previously hidden business insights. Others are merely shiny distractions, demanding huge budgets with uncertain ROI.

Your first step is getting crystal-clear on your organization’s goals. Are you trying to reduce operating costs? Differentiate your product in a competitive market?

Your second step is acknowledging your unique technical landscape. As mentioned in the ‘when to skip’ sections above, if your engineering culture hasn’t yet mastered things like DevOps or continuous delivery, diving into advanced AI might introduce more chaos than value.

AI should extend an already stable foundation–not serve as a band-aid for deeper engineering or process issues.

What changes when moving from classic software to AI-forward systems

Most CTOs have led traditional software teams, where developers explicitly program every workflow, condition, and all business logic. AI fundamentally changes this paradigm as AI models can discover patterns and relationships from data rather than following predetermined instructions. This shift introduces several important considerations:

• From probabilistic to deterministic systems: Unlike traditional software that produces consistent, predictable results for the same inputs, AI systems generate probabilistic outputs that may vary. This shift requires engineering practices that transform inherently non-deterministic AI into reliable, production-grade systems. This includes implementing confidence thresholds, fallback mechanisms, and addressing explainability challenges—especially with deep learning models that often function as ‘black boxes’ for their reasoning. The engineering challenge lies in creating deterministic guarantees from probabilistic foundations.
• Prototyping is easy, production is hard: While initial AI models are straightforward to build, scaling them reliably is challenging. Similar to SRE practices, the real work begins after deployment. AI systems need continuous monitoring, refinement, and robust evaluation frameworks to handle edge cases and performance degradation in production environments. The engineering complexity lies in maintaining consistent performance across diverse real-world conditions that weren’t visible during early prototyping.
• Innovation happens at the intersection of AI understanding and customer-centricity: AI blurs traditional team boundaries. Engineers with product intuition can identify opportunities invisible to non-technical staff, while product managers need deeper technical understanding since AI capabilities directly shape viable use cases. The most innovative applications emerge at this intersection where technical expertise meets business context, requiring tighter integration between previously siloed teams.

For many organizations, adopting AI isn’t just about acquiring new tech; it’s about evolving development practices to accommodate new processes, roles, and feedback loops. This requires rethinking how you structure teams, allocate resources, and measure success in your engineering organization.

Discovering AI opportunities with the Tech Radar approach

Once you have a handle on the basics, you can move onto incorporating strategic frameworks to make decisions related to AI initiatives. The Tech Radar is one way we recommend evaluating what makes the most sense for your organization.