PLAYBOOK ATLASPlaybook Atlas

Technology

AI in technology: explore 85 applications.

Make engineering work easier to reproduce, inspect and improve.

Explore code review, testing, debugging, documentation and security-analysis workflows for software and platform teams.

Plan your first AI move See the 85 applications

Where the 85 applications sit

Explore the domains
  1. 37Software Development
  2. 20Security and Compliance
  3. 18Data Management
  4. 15Process Automation

01Why AI now

Why technology is moving on AI, in three figures.

Each figure links to its source.

  1. Tech firms: nearly 1 in 3 adopt GenAI rapidly

    Nearly one-third of surveyed technology leaders reported rapid GenAI adoption at their companies, versus 11% of leaders in other industries.

    Source: Deloitte Insights

  2. AI/ML talent gap: 68% of organizations

    A global survey of more than 500 hiring and training leaders found that 68% of organizations lacked enough employees skilled in AI and machine learning.

    Source: Linux Foundation Research

  3. Big Tech capex topped $400B in 2025

    Capital expenditure by five large technology companies exceeded $400 billion in 2025 and was projected to rise another 75% in 2026, driven by data-center investment.

    Source: International Energy Agency

How to evaluate a first project

Start with a reproducible engineering task and an explicit acceptance condition. Compare proposed changes with the current workflow using real failures, useful tests and review findings. Verify behavior, security and operational effects before release; generated code or a passing isolated test is only part of that evidence.

02Capability map

Where technology puts AI to work, domain by domain.

Pick a domain to see its applications. Each one opens a full report.

Inside Software Development

Applications in Software Development

What the use cases ask AI to do

Share of the technology use cases, by the kind of work the AI does.

  1. Generation

    Creates: text, images, code and other content.

    44%

  2. Reasoning

    Analyzes, predicts and decides: scoring, forecasting, classifying.

    43%

  3. Agentic

    Acts: plans, uses tools and completes multi-step tasks.

    13%

Under 1% each: perception and optimization.

03Workflow ideas

Ways AI could change the work, in technology.

Atlas groups these applications by the changes they propose to a workflow. These preliminary classifications help you explore ideas; each application still needs a review of its sources and operating requirements.

Workflow categories in this catalog

Share of the 37 applications assigned a category.

How to evaluate an application

  1. Start with a bounded task

    Identify the inputs, the person responsible, and the decision or output the workflow must produce.

  2. Check the support for each method

    Look for source-backed details about the specific implementation, including its limits and human review.

  3. Measure the pilot

    Record the current baseline, agree on acceptance criteria, and test on representative cases before expanding.

Explore applications

04What gets built

The approaches technology uses most, and when to skip them.

Knowing when not to use one matters as much as knowing when to.

  1. 01 · 16 applications

    RAG-Standard

    LLM + retrieval layer that injects relevant documents into the prompt at query time.

    Use it when

    • You need an LLM to answer questions based on proprietary or private documents that are not part of its pretraining data.
    • Your knowledge changes frequently (e.g., policies, product docs, pricing) and you want updates to be reflected without re-training the model.
    • You have a large corpus of unstructured or semi-structured text (wikis, PDFs, tickets, emails, manuals) that users struggle to search effectively.

    Skip it when

    • Your task is primarily structured data querying or analytics (e.g., reporting over a relational database) where SQL or BI tools are more accurate and efficient.
    • You have very small, static knowledge (e.g., a short FAQ) that can fit directly into a prompt or be encoded as rules without a retrieval layer.
    • You require strict, deterministic behavior with no tolerance for probabilistic errors (e.g., certain financial calculations, safety-critical controls).
    RAG-Standard, in depth

  2. 02 · 10 applications

    AI Evaluation Diagnostics

    AI evaluation, failure analysis, and model diagnostics workbenches.

    AI Evaluation Diagnostics, in depth

  3. 03 · 9 applications

    Workflow Automation

    Use AI inside orchestrated workflows to automate multi-step business processes.

    Use it when

    • When you have repetitive, rule-heavy workflows that still require human judgment or document understanding.
    • When the process spans multiple systems (ERP, CRM, ticketing, email) and manual coordination is slow or error-prone.
    • When large volumes of semi-structured or unstructured data (emails, PDFs, forms) must be processed consistently.

    Skip it when

    • When the process is highly creative, exploratory, or non-repeatable, with no stable sequence of steps.
    • When decisions are extremely high-stakes (e.g., life-critical medical decisions, major legal judgments) and regulations require full human control.
    • When you lack reliable access to the necessary systems or data sources to complete the workflow end-to-end.
    Workflow Automation, in depth

05The applications

85 technology applications, each with a full report.

The widest first, by the number of use cases each covers. Every report shows what changed, how it works, who has done it and what can go wrong.

  1. Code Quality Review AssistantAI 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.Human Creative → Augmented19 use cases
  2. Cyber Threat Intelligence OperationsThis 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.Expert → AI13 use cases
  3. Coding Assistance and ReviewThis 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.Human Creative → Augmented12 use cases
  4. AI-Assisted Product and Developer Collaboration WorkflowsAI workflows that accelerate collaborative technology work across experimentation, content organization, customer chat support, software development, LLM trace debugging, design creation, and template discoverability through targeted recommendations, copilots, observability, and risk-managed enterprise rollout.Recommendation Systems7 use cases
  5. Code Generation AssistantThis 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.Human Creative → Augmented5 use cases
  6. Software Test GenerationAutomated 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.Human Creative → Augmented4 use cases
All 85 applications, A to Z

Work with us

The catalog shows what is possible. A sprint decides what you should fund.

Reading every report still leaves the hardest question open: which move is worth funding first, here, given your constraints. That is the sprint: two weeks, fixed scope, and a candid answer either way.

Book a 30-minute callHow the sprint works

Two weeks Fixed scope A candid fit-or-no-fit answer

  1. 01

    Three ranked opportunities

    A decision-ready shortlist scored against value, feasibility, evidence, risk, and time to proof.

  2. 02

    One pilot-ready workflow

    A bounded operating design with actors, handoffs, data needs, controls, and success measures.

  3. 03

    A defensible 90-day plan

    Build-versus-buy guidance, ROI assumptions, risk treatment, sequencing, and executive decisions.

06Rules & standards

Rules and standards to check, with their scope and sources.

Selected US and EU references for these workflows. Applicability depends on jurisdiction, organization, data and intended use. The planning notes are Atlas editorial guidance; they are not official implementation-time estimates or an exhaustive list of obligations.

  1. EU AI Act · role and intended-use classification

    Law or regulation

    European Union · activities within AI Act scope

    Obligations depend on the system, intended use and provider or deployer role. Transparency, prohibited practices, general-purpose models and high-risk systems have distinct requirements and application dates.

    European Commission: AI Act framework and implementation dates

    Source checked

    Project planning note

    Classify the company’s role, system purpose and applicable dates before assigning release requirements.

  2. NIST Secure Software Development Framework

    Voluntary framework

    United States framework · usable internationally

    The SSDF recommends secure development, software protection and vulnerability-response practices. It is guidance rather than a universal law; procurement or contract terms may incorporate specific requirements.

    NIST: Secure Software Development Framework

    Source checked

    Project planning note

    Map the product’s development and update process to secure practices and any specific customer or procurement obligations.

Your next step

Find your first move in technology.

Ask the workspace which move fits your company. Add your size, systems and constraints, and it works from these 85 applications to a plan your team can act on.

Plan your first AI move

Rather talk it through? Book a 30-minute call