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Where technology companies are investing
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How technology companies distribute AI spend across capability types
AI that sees, hears, and reads. Extracting meaning from documents, images, audio, and video.
AI that thinks and decides. Analyzing data, making predictions, and drawing conclusions.
AI that creates. Producing text, images, code, and other content from prompts.
AI that improves. Finding the best solutions from many possibilities.
AI that acts. Autonomous systems that plan, use tools, and complete multi-step tasks.
Data-driven insights to guide your AI strategy. Understand market maturity, identify high-ROI opportunities, and assess implementation risk.
Most adopted patterns in technology
Each approach has specific strengths. Understanding when to use (and when not to use) each pattern is critical for successful implementation.
Generative AI is a family of models that learn the statistical structure of data (text, images, audio, code, etc.) and then sample from that learned distribution to create new content. These models are typically built with deep neural architectures such as transformers, diffusion models, and GANs, and can be conditioned on prompts, examples, or structured inputs. In applications, generative models are often combined with retrieval systems, tools, and business logic to ground outputs in real data and workflows. Effective use requires careful attention to safety, reliability, governance, and alignment with domain constraints.
Canonical solution label for systems focused on AI safety governance, safety validation, policy enforcement, assurance workflows, and simulation-backed safety operations.
Canonical solution label for systems centered on SOC workflows, enrichment, alert correlation, SOAR decisioning, and analyst-assist operations rather than a single low-level model family.
Top-rated for technology
Each solution includes implementation guides, cost analysis, and real-world examples. Click to explore.
AI Coding Quality Assistants embed large language models into the development lifecycle to generate, review, and refactor code while automatically creating and validating tests. They improve code quality, reduce technical debt, and harden security by catching defects and vulnerabilities early. This increases developer productivity and accelerates delivery of reliable enterprise software with lower maintenance costs.
This application area focuses on systematically collecting, analyzing, and disseminating intelligence about evolving cyber threats, with a particular emphasis on how attackers are adopting and weaponizing advanced technologies. It turns global telemetry, incident data, and open‑source observations into structured insights on attacker tactics, techniques, and procedures, including emerging patterns such as automated phishing, malware generation assistance, disinformation, and AI‑orchestrated attack chains. It matters because security and technology leaders need evidence‑based visibility into real‑world attacker behavior to shape strategy, budgets, and controls. Instead of reacting to hype about “next‑gen” threats, organizations use this intelligence to prioritize defenses, adjust architectures, and update policies before new techniques become mainstream. By making the threat landscape understandable and actionable for CISOs, boards, and policymakers, cyber threat intelligence directly reduces breach likelihood and impact while guiding long‑term security investment decisions.
This AI solution covers AI copilots and debugging agents that generate, review, and refine code directly in developers’ environments. By automating boilerplate, suggesting fixes, and improving test coverage, these tools accelerate delivery cycles, reduce defects, and let engineering teams focus on higher-value design and architecture work.
This application area focuses on tools that assist software developers by generating, modifying, and explaining code, as well as automating routine engineering tasks. These systems integrate directly into IDEs, editors, and development workflows to propose code completions, scaffold boilerplate, refactor existing code, and surface relevant documentation in real time. They act as an always-available pair programmer that understands context from the current codebase, tickets, and documentation. It matters because software development is a major cost center and bottleneck for technology organizations. By offloading repetitive coding, speeding up debugging, and helping developers understand complex or unfamiliar code, automated code generation tools significantly improve engineering throughput and reduce time-to-market. They also lower the barrier for less-experienced engineers to contribute high-quality code, helping organizations scale their development capacity without linear headcount growth.
This application area focuses on systematically evaluating, validating, and improving the quality and correctness of software produced with the help of large language models. It spans automated assessment of generated code, test generation and summarization, end‑to‑end code review, and specialized benchmarks that expose weaknesses in model‑written software. Rather than just producing code, the emphasis is on verifying behavior over time (e.g., via execution traces and simulations), ensuring semantic correctness, and reducing hallucinations and latent defects. It matters because organizations are rapidly embedding code‑generation assistants into their development workflows, yet naive adoption can lead to subtle bugs, security issues, and maintenance overhead. By building rigorous evaluation frameworks, test‑driven loops, and quality benchmarks, this AI solution turns LLM coding from an unpredictable helper into a controlled, auditable part of the software lifecycle. The result is more reliable automation, safer use in regulated or safety‑critical environments, and higher developer trust in AI‑assisted development. AI is used here both to generate artifacts (code, tests, summaries, reviews) and to evaluate them. Execution‑trace alignment, semantic triangulation, reasoning‑step analysis, and structured selection methods like ExPairT allow teams to automatically check, compare, and iteratively refine model outputs. Domain‑specific datasets and benchmarks (e.g., for Go unit tests or Python code review) make it possible to specialize and benchmark models for concrete quality tasks, creating a feedback loop that steadily improves automated code quality assurance capabilities.
Automated Software Test Generation focuses on using advanced models to design, generate, and maintain test assets—such as test cases, test data, and test scripts—directly from requirements, user stories, application code, and system changes. Instead of QA teams manually writing and updating large libraries of tests, the system continuously produces and refines them, often integrated into CI/CD pipelines and specialized environments like SAP and S/4HANA. This application area matters because modern software delivery has moved to rapid, continuous release cycles, while traditional testing remains slow, labor-intensive, and error-prone. By automating large parts of test authoring, impact analysis, and defect documentation, organizations can increase test coverage, accelerate release frequency, and reduce the risk of production failures—especially in complex enterprise landscapes—while lowering the overall cost and effort of quality assurance.
How technology is being transformed by AI
12 solutions analyzed for business model transformation patterns
Dominant Transformation Patterns
Transformation Stage Distribution
Avg Volume Automated
Avg Value Automated
Top Transforming Solutions