What is generative AI?

Generative AI (GenAI) refers to a subset of artificial intelligence designed to create text, images, audio, video, and even code by learning from patterns in large datasets. Unlike traditional AI systems that focus on classifying or predicting data, generative AI produces new content based on what it has learned during training.

These models encode statistical representations of their training data and then generate original outputs that resemble, but do not copy, the examples they have seen. This capability allows GenAI to produce results that feel novel and contextually relevant.

Commonly recognized tools that leverage generative AI include conversational systems like ChatGPT, image generators such as Midjourney, and developer assistants like GitHub Copilot.

How generative AI works

Generative AI systems rely on advanced machine learning (ML) techniques, particularly deep learning and transformer architectures, to create new outputs rather than merely analyzing or classifying existing data. These models are trained on large datasets of text, images, video, and audio, which allows them to recognize patterns and relationships across vast amounts of information.

After they’ve been trained, GenAI systems generate results in response to user inputs (questions, descriptions, or requests, often called “prompts”) by producing relevant outputs that mirror the style and structure of the training data while remaining original.

For example, a manufacturer might prompt generative AI to draft a technical specification or summarize change request documentation with context such as the affected parts and compliance requirements, and the system would create a unique output that meets the user’s needs.

Types of generative AI models

There are several types of generative AI models on the market today, each optimized for different applications and use cases. The most popular models include:

• Large language models (LLMs): Designed for text-based tasks such as drafting content, summarizing documents, or answering questions
• Diffusion models: Specialize in generating highly detailed images by iteratively refining visual noise into recognizable patterns
• Generative adversarial networks (GANs): Consist of two neural networks that compete to create increasingly realistic images, videos, or synthetic data

While many generative AI models are initially trained on fixed datasets, they are often finetuned over time with new data, feedback, or reinforcement learning methods that improve the accuracy of their outputs and reduce errors and biases.

Benefits of generative AI

From accelerating content creation to improving collaboration and onboarding, here are a few benefits that highlight where GenAI can deliver meaningful value across the business.

• Enhanced creativity and innovation

  • GenAI supports innovation by quickly producing high-quality content, such as concept visuals and draft documentation, to help jumpstart the creative process. This can aid teams in exploring more possibilities early on, reducing the friction that often slows down ideation.

• Increased productivity and efficency

  • By automating routine tasks, GenAI frees up time for teams to focus on more strategic and value-added work. This efficiency boost is especially impactful in fast-moving product development environments.

• Personalization at scale

  • GenAI makes it possible to deliver tailored content (e.g., articles, documentation, recommendations) at speed and scale. Adapting outputs to specific roles and use cases supports more relevant interactions without the overhead of heavy manual customization.

• Democratization of content creation

  • With GenAI, advanced creative tools are no longer limited to technical experts. Anyone across the business can generate compelling content, closing the gap between vision and execution for everything from product documentation and training materials to customer communications and marketing assets.

• Better knowledge capture

  • By transforming unstructured data into searchable content, GenAI can help organizations retain and repurpose institutional knowledge. Raw materials like meeting notes, engineering comments, and legacy documentation become more accessible, supporting faster onboarding and preserving key insights across teams.

• Reduce development costs

  • Generative AI cuts content creation costs by reducing the human effort needed to produce first drafts, designs, or code snippets. It also automates support tasks, using chatbots and virtual assistants to handle common questions and lower support workload. In addition, AI-driven suggestions and checks help catch errors early, minimizing rework and improving overall quality.

Common generative AI examples

Generative AI is already improving how teams plan and execute their daily work. Here are a few examples that illustrate some of today’s most practical and impactful applications:

• Text generation: Producing written content such as articles, summaries, reports, or website copy
• Image creation: Generating visuals from text descriptions or reference inputs, often used for concept art, product mockups, or design iterations
• Video production: Creating short videos or repurposing existing videos with minimal manual editing, supporting everything from training content to promotional media
• Coding assistance: Helping developers by writing boilerplate code, translating between programming languages, or identifying and fixing bugs
• 3D modeling and simulation: Designing virtual components and assemblies for use in engineering and product development

Generative AI vs. other AI approaches

While “AI” is often used as a blanket term, not all AI technologies work the same way. The table below compares generative AI with traditional and predictive approaches to highlight their distinct purposes, outputs, and underlying techniques.

Aspect	Generative AI	Traditional AI	Predictive AI	Agentic AI
Purpose	Creates new data or content	Analyzes, classifies, and organizes	Predicts future trends or outcomes	Takes action to achieve goals autonomously
Examples	Documentation drafting, code generation	Fraud detection, image tagging	Sales projections, churn prediction	Automating tasks, executing workflows
Outputs	Text, images, code, music, video	Labels, insights, analytics	Forecasts and risk scoring	Event triggers, decisions, task completions
Techniques Used	LLMs, GANs, diffusion models	Decision trees, clustering, regression	Time series models, supervised ML	Reinforcement learning, LLMs, memory systems

When to use generative AI

In the past few years, GenAI has rapidly evolved from experimentation to real-world adoption, with analysts forecasting widespread use across both creative and technical domains. At the same time, Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027, underscoring both the promise of AI technologies and the challenges of implementing them responsibly.

The key to striking this balance lies in setting realistic expectations for what AI can deliver today while still exploring its potential to transform the way work will get done tomorrow. With the right blend of practicality and ambition, generative AI can act as a powerful force multiplier for businesses that want to:

• Create content and documentation faster with higher quality
• Help teams with coding, documentation, or brainstorming
• Prototype concepts or designs more efficiently
• Provide personalized experiences or outputs at scale
• Help the broader organization understand technical information

Generative AI use cases

Here’s a closer look at some of the ways in which GenAI is being applied across business and technical domains:

• Generating customer support responses

  • GenAI can create relevant replies to common customer questions, dramatically improving response times. With human oversight in place, support teams can manage higher inquiry volumes while focusing their attention on higher-value issues.

• Designing ad creatives and visuals

  • AI tools can instantly produce polished graphics based on natural language prompts. This speeds up creative testing while reducing manual work so teams can focus more on refining their best ideas and scaling campaigns.

• Assisting software developers with coding tasks

  • AI-driven coding assistants can generate basic code, suggest fixes, and even translate between programming languages. By automating repetitive tasks, these tools accelerate development cycles, allowing developers to focus on more strategic engineering work.

• Summarizing technical documentation

  • GenAI can condense complex engineering change requests, requirements, or test results into concise summaries, making critical information easier to digest and share across teams.

• Exploring product design variations

  • By generating multiple design alternatives within defined parameters, GenAI helps teams evaluate options quickly, speeding up the iteration process and reducing the time it takes to reach a viable solution.

Potential challenges of generative AI

GenAI offers incredible opportunities, but adoption isn’t always straightforward. The same capabilities that drive efficiency and innovation can create risks if left unchecked. Organizations need clear governance, transparency, and human oversight to unlock value responsibly.

The following challenges illustrate why a thoughtful approach is essential:

• Data privacy and security: Sensitive information may inadvertently appear in generated outputs if models are trained on or exposed to proprietary data
• Bias and fairness: GenAI can reproduce or even amplify biases that exist in its training data, leading to outputs that are unbalanced or discriminatory
• Accuracy and hallucinations: Outputs appear to make sense but can be factually incorrect, incomplete, or misleading due to limitations in the training data
• Copyright and IP issues: Legal ownership of AI-generated works remains uncertain in many regions, raising questions about authorship, licensing, and reuse
• Cost and infrastructure: Building, training, and fine-tuning large-scale models requires significant resources, raising questions about sustainability and scalability

Driving smarter, faster innovation

One of GenAI’s most cutting-edge applications is when it is applied within  governed product lifecycle. Combined with an AI-ready digital thread, generative models can help validate requirements, accelerate documentation, and make product data more accessible through natural language search. This empowers teams to iterate more quickly and unlock insights that fuel better decisions.

By connecting GenAI to a resilient, governed platform like Aras Innovator®, organizations can scale these capabilities responsibly, preserving data quality, context, and traceability while driving innovation.

GenAI in PLM: Where it delivers value (and how to do it safely)

Generative AI becomes truly transformative when it’s wired into a governed digital thread, the connected backbone of product data spanning requirements, design, simulation, manufacturing, quality, and service. In a PLM context, GenAI can surface the right information at the right time, draft first-pass deliverables, and accelerate decisions without breaking traceability or IP controls.

High-value use cases across the lifecycle

• Requirements and MBSE: Draft, deduplicate, and clarify requirements; highlight gaps and downstream impacts; turn model artifacts into role-specific summaries.
• Concept and detailed design: Generate design rationales, compare alternatives, and auto-produce variant descriptions based on configured options and constraints.
• Simulation and test: Create scenario summaries, propose test steps from requirements, and explain discrepancies between expected and observed behavior.
• Change and configuration management: Auto-draft ECR/ECO problem statements, impacts, and stakeholder notifications using linked parts, documents, and effectivity rules.
• Manufacturing Process Planning (MPP): Generate first pass work instructions and inspection plans from EBOM/MBOM context; flag missing tooling, skills, or compliance references.
• Quality and reliability: Triage CAPA/NCRs with cause hypotheses and similar-issue lookups; summarize field feedback into actionable corrective/preventive actions.
• Technical publications and service: Turn engineering change content into technician-ready procedures and parts lists; propose service bulletins grounded in the latest configuration.
• Supplier and value chain collaboration: Draft RFx packages, compliance declarations, and handover checklists that reflect approved specifications and standards.
• Digital twin and in-service: Explain telemetry anomalies in plain language, propose next-best actions, and link recommendations back to the configured as-maintained item.

Why PLM context matters

GenAI is only as good as the data and governance around it. Inside PLM, you gain the context (part, version, and effectivity), controls (permissions, watermarks, and audit trail), and traceability (links across the thread) that generic AI tools lack. This is how you scale AI safely across teams and suppliers.

Guardrails & capabilities to look for

• RAG over governed data: Retrieval-augmented generation that pulls only from approved, access-controlled PLM sources.
• Traceability by design: Every AI-assisted output carries links back to the originating items (requirements, parts, changes, simulations).
• Human-in-the-loop: Review, redline, and approval steps embedded in standard PLM workflows.
• Policy and IP protection: Fine-grained permissions, supplier scoping, redaction, and audit logging.
• Operational metrics: Evaluate accuracy, coverage, latency, and cost per artifact; route low-confidence answers to experts.
• Extensibility: Connectors to CAD/ALM/ERP, support for multi-domain BOMs, and options to evolve toward agentic task automation when ready.

Getting started with Aras

1. Pick 2–3 quick wins in existing workflows (e.g., ECR drafting, CAPA triage, WI first drafts).
2. Connect to your digital thread so GenAI can cite governed sources—not ad-hoc files.
3. Embed in PLM tasks rather than launching yet another tool; keep users in context.
4. Measure & iterate with clear acceptance criteria and safety thresholds.

Explore how Aras approaches AI in PLM and the digital thread in these resources: our overview of generative AI in PLM, a webinar on genAI for product development, and how genAI capabilities evolve toward agentic automation.

With Aras Innovator®, you can pair generative AI with a resilient, governed platform, preserving data quality, context, and traceability while accelerating innovation across the lifecycle.