AI is another piece of the enterprise tech stack, far removed from a distant R&D project and now a practical system component underpinning everything from customer service to forecasting models. But many product and technical leaders are in the dark about the process of building these systems.
This post explains things like what AI development actually is (from machine learning and deep learning to today’s LLMs, RAG pipelines, and vector databases). Whether you’re in a build-vs-buy phase, testing out new AI tech, or scoping your first use case, it’s the context to do it right.
AI development is a way to design, train, and deploy software systems that exhibit the behavior traditionally mentioned as human: perception, reasoning, prediction, decision-making, and understanding language. Artificial intelligence is a catch-all term that more accurately describes a stack of technologies like a pyramid, each with its own function and implementation path.
Source: https://www.devprojournal.com/software-development-trends/devops/what-are-the-most-commonly-used-software-development-tools/
Think of AI as the goal, ML as the method, and DL as the scaling mechanism. Traditional ML methods (e.g., decision trees, SVMs) require feature engineering and perform well on structured data. Deep learning, by contrast, excels at modeling unstructured data (text, images, audio) at scale, reducing the need for manual feature extraction.
Another crucial difference: Applied AI is a name for the domain-specific, task-driven systems that are going into production now. Such systems are trained to accomplish specific tasks in tightly circumscribed domains—detecting fraud, summarizing contracts, forecasting demand—and they do so with measurable precision and business impact.
Artificial General Intelligence (AGI), the general AI, on the other hand, still exists as a conjecture. It’s a term for systems that exhibit human-level reasoning and learning abilities in a broad set of domains. AGI is outside the range of enterprise strategy, not because it’s not an interesting phenomenon, but because we can’t yet build it, and it’s not yet a useful commercial thing.
Source: https://www.vecteezy.com/vector-art/22400143-artificial-general-intelligence-linear-logo-minimalist-style-agi-icon-depicts-physics-and-technology-showcasing-ai-brain-powered-machine-learning-vector-eps-illustration
For business leaders, it’s time to focus on applied, production-ready AI. These systems are designed to coexist with your current solutions, work under constraints such as latency and privacy, and provide ROI through automation, augmentation, or optimization.
The present AI stack is built as a stack of building blocks (each responsible for one layer of functionality: representation, retrieval, generation, and orchestration), useful for deployment into existing factory systems. Here is a summary of the core technologies every business leader should know.
LLMs are deep learning models with large-scale text-based corpora for the purpose of knowledge generation, classification, and manipulation of language. Architecturally, they are based on transformer networks, an innovation that enables models to handle long-range dependencies in text and parallelize training at scale.
Models like GPT-5 (OpenAI), Claude (Anthropic), LLaMA (Meta), and Mistral are trained using self-supervised learning objectives, typically next-token prediction across billions of parameters and tokens. Training occurs on a distributed compute infrastructure, using techniques like mixed-precision training, gradient checkpointing, and reinforcement learning from human feedback (RLHF) to refine performance.
RAG is a hybrid architecture that combines an LLM’s generative ability with an external retrieval system (typically, a vector database). Before generating a response, the model retrieves relevant information from a knowledge base (e.g., internal documents, policy PDFs, structured data), embeds it into the prompt, and only then produces an output.
Why it matters:
LLMs are limited by their training cutoff and don’t “know” proprietary data. RAG allows enterprises to build AI systems that are both current and domain-aware without retraining the base model.
Notable tools include LangChain, LlamaIndex, Weaviate, Pinecone, FAISS.
Vector databases are specialized systems used to hold and fetch high-dimensional embeddings: numeric representations of unstructured data like text, images,or audio. Rather than searching for exact keywords, they help AI systems find information based on meaning. For instance, if a user asks, “How do I cancel my plan?” the system can retrieve past support tickets that mention “ending a subscription” or “closing an account”—even if those exact words weren't used. Two pieces of content with “subscription cancellation” in them, even if the words are used differently in an exact word match, will exist close enough in the space that you’ll be able to retrieve them based on intent.
Source: https://weaviate.io/blog/what-is-a-vector-database
The main functionality of vector databases is their capability to handle semantic search at scale, even in low-delay use cases. This is done through the use of Approximate Nearest Neighbor (ANN) algorithms, such as HNSW or IVF, used to speed up similarity search within very large sets of vectors without making an exhaustive comparison. This makes vector DBs a critical backbone in RAG pipelines.
For enterprises, vector databases unlock intelligent access to internal knowledge silos, be they support tickets, legal documents, product specs, or conversation logs. Leading solutions like Pinecone, Weaviate, FAISS, and Qdrant offer APIs that integrate directly into AI stacks, often alongside orchestration frameworks like LangChain.
Prompt engineering is the practice of crafting and structuring inputs to LLMs to elicit the desired output behavior. While LLMs are powerful, their responses are sensitive to how instructions are framed, what context is given, and in what order.
Example:
A generic prompt: "Summarize this document,” may yield vague results. A refined prompt: "Summarize this document for a financial analyst looking for quarterly risk factors," produces domain-specific value.
Advanced Prompt Techniques: Chain-of-thought prompting, zero-shot reasoning, output templating, and system instructions.
While LLMs handle text generation, LangChain, Haystack, and similar orchestration frameworks act as the “middleware” that connects models, tools, APIs, databases, and user-facing applications.
These frameworks enable:
If LLMs are engines, orchestration frameworks are the runtime layer that wires together input sensors, control logic, and output interfaces.
What the AI does:
Machine learning models, which are usually time-series models or sequence predictors that employ the transformer architecture, study behaviors in historical data, seasonality, supply chain behaviors, and external signals (such as weather or market trends etc.), estimating inventory demand at various SKU, location, or warehouse levels.
What problem it solves:
Understocking leads to lost revenue. Overstocking increases holding costs and waste. AI-powered systems cut out much of this mismatch—McKinsey’s analysts report forecasting errors were cut by 20-50%, reducing the dissatisfactions of lost sales and lost products by up to 65%, falling warehouse costs by 5-10% and slashing costly admin by a quarter to over a third
Tools typically used:
What the AI does:
Generative models, such as diffusion models or GANs, are used to produce 2D and 3D visual assets, including product mockups, textures, concept art, and scene compositions. By conditional generation, we can guide the outputs with prompts, sketches, or templates.
Source: https://docs.midjourney.com/hc/en-us/articles/33329329805581-Modifying-Your-Creations
What problem it solves:
Manual asset generation is a long and costly process, especially for product design and game development pipelines. According to Gitnux, AI reduces iteration time: 68% of design agencies report a project turnaround reduction of at least 30%; 45% note a decrease in manual workload
Tools typically used:
What the AI does:
Large language models and behavior trees are used to drive more dynamic non-player character (NPC) dialogues, reactions, and decision-making. Some approaches include RAG for real-time story context retrieval and for continual learning of behavior.
What problem it solves:
Static NPC scripting limits replayability and immersion. AI-driven NPCs can respond contextually to player actions, enabling more lifelike social dynamics, procedural storytelling, and emergent gameplay without exponential authoring overhead.
Tools typically used:
What the AI does:
Transformer models perform extractive or abstractive summarization of dense documents—contracts, financial reports, policy manuals—distilling pages of content into key clauses, risk statements, or executive summaries.
What problem it solves:
Professionals spend hours reviewing complex, repetitive documents. AI summarization speeds up due diligence, reporting, and compliance workflows while reducing human error in repetitive parsing tasks.
Tools typically used:
What the AI does:
ML models trained on sensor data (vibration, temperature, electrical signals) detect patterns leading up to equipment failure. Models predict the Remaining Useful Life (RUL) of machinery and recommend proactive interventions.
What problem it solves:
Unexpected equipment failures cause costly downtime. Traditional maintenance schedules are either too rigid or too late. Predictive systems enable condition-based maintenance, improving uptime and resource planning.
Tools typically used:
Despite powerful tools and a well-defined use case, AI development in the enterprise is hardly frictionless. Following are the most typical technical and operational traps—along with actionable tricks to derail them before they sabotage success, confidence or ROI.
Artificial intelligence models are only as good as the data they are trained or grounded in. There can be imbalanced distributions, noises, missing values, and inconsistent formats, which make the accuracy of model performance unreliable. There also exist silent failures in production settings due to drift in the data, i.e., the distribution shift in data over time (e.g., seasonality, user behavior changes). According to Gartner, poor data quality costs organizations an average of $12.9 million annually.
Source: https://www.trootech.com/blog/ai-development-cost
How to avoid it:
Overfitting means a model can perform well on the training data but badly on some new, unseen inputs. This can be particularly problematic in high-dimensional data or when you work with small datasets, two of the key characteristics in business domains where data is proprietary.
Overfitting models can deceive teams with high validation accuracy that then quietly underperform in the field. In high-stakes fields such as health care or finance, this can result in regulatory risk or operational breakdown.
How to avoid it:
AI systems, especially those relying on LLMs or RAG pipelines, often involve multiple steps of API calls, retrieval, and processing. This introduces latency which may not be ideal for real-time applications such as chatbot, virtual assistants, fraud detection, etc.
Forbes mentions that 40% of people give up on digital interactions that last longer than three seconds, and companies that implement sluggish AI systems must overcome internal resistance and declining usage.
How to avoid it:
Enterprise AI systems often process sensitive data: PII, financials, contracts. Improper handling during training, inference, or logging can create attack surfaces, violate regulations (e.g., GDPR, HIPAA), or expose businesses to legal risk.
How to avoid it:
Now AI is not confined anymore to research labs or innovation teams and is present in production systems, business processes, and strategic planning. Business leaders need to know about the nuts and bolts of AI development not only for the sake of intellectual curiosity but also for the purpose of operations. From the reasoning behind retrieval-augmented pipelines to the subtleties in prompt engineering, being able to think about how these systems operate enables teams to scope more useful use cases, steer clear of common pitfalls, and evaluate build vs. buy decisions clearly.
This is just the superficial layer, though. From LLM fine-tuning and multi-agent systems to multi-modal architecture and vector-native applications, there is much that would be game-changing if scaled, and each deserves its own briefing.
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