이 페이지는 아직 선택하신 언어로 제공되지 않습니다. 검수를 거친 번역본을 준비하는 동안 영어 버전을 표시하고 있습니다.
개요
Most organizations do not lack AI ideas; they lack a disciplined way to decide which ones deserve investment. We work with executive and technical leaders to map candidate use cases against business value, data availability, integration effort and risk, so that the first systems you build are the ones most likely to reach production.
The output is not a deck of possibilities. It is an engineering-grade roadmap: prioritized initiatives, a reference architecture, governance expectations and a realistic account of what each use case depends on.
비즈니스 활용 분야
- 01
Use case portfolio prioritization
Score candidate initiatives on value, feasibility, data readiness and regulatory exposure, then sequence them so that early projects build the shared platform, data pipelines and evaluation practices that later initiatives can reuse.
- 02
Data and platform readiness
Review source systems, data quality, access controls and cloud foundations to establish what an AI workload actually needs before a model is chosen or a vendor contract is signed.
- 03
Build, buy or partner decisions
Compare commercial AI products, foundation model APIs and custom development for each use case, weighing total cost of ownership, data residency, vendor lock-in and the internal skills required to operate the result.
- 04
AI operating model design
Define ownership, review boards, intake processes and the roles needed to run AI responsibly at scale, aligned with frameworks such as the NIST AI Risk Management Framework and ISO/IEC 42001.
엔지니어링 접근 방식
- 01
Frame the business objectives
Interview leadership and process owners to anchor the work in operational goals, existing constraints and the specific decisions AI would inform, rather than in a list of technologies.
- 02
Inventory data and systems
Map the systems of record, data flows, integration points and access policies each candidate use case depends on, noting the gaps that would block a production deployment.
- 03
Assess value, feasibility and risk
Evaluate each opportunity against a consistent rubric covering business impact, technical complexity, data sensitivity, regulatory exposure and the level of human oversight the decision requires.
- 04
Validate with targeted prototypes
Where uncertainty is highest, build small prototypes against real data to test assumptions about retrieval quality, model behavior and user workflow before committing a larger budget.
- 05
Deliver the roadmap and architecture
Hand over a sequenced roadmap, target reference architecture, governance recommendations and delivery plan, with entry criteria for each initiative and the measures that will judge its success.
기술 및 플랫폼
Frameworks and standards
- NIST AI Risk Management Framework
- ISO/IEC 42001
- EU AI Act risk categories
- OECD AI Principles
Model ecosystems evaluated
- OpenAI
- Anthropic Claude
- Google Gemini
- Meta Llama
- Mistral
Cloud AI platforms
- Amazon Bedrock
- Amazon SageMaker
- Azure OpenAI
- Azure Machine Learning
- Google Vertex AI
- Databricks
Data foundations
- Snowflake
- BigQuery
- dbt
- Apache Kafka
- PostgreSQL
기술 명칭은 각 소유자의 상표이며, 파트너십이나 보증을 의미하지 않습니다.
제공 결과물
- Prioritized AI use case portfolio with scoring rationale
- Data and platform readiness assessment
- Target reference architecture for AI workloads
- AI governance and risk management recommendations
- Sequenced implementation roadmap with entry criteria
- Prototype findings for the highest-uncertainty use cases
리더들이 자주 묻는 질문
Do you recommend specific AI vendors?
We are model- and vendor-agnostic. Recommendations follow the requirements of each use case, including data residency, latency, cost profile, integration effort and your existing cloud commitments. Where several options are viable, we document the trade-offs so your team can make the decision with full context.
How is this different from a typical AI assessment?
The work is led by engineers who build and operate AI systems. Feasibility judgments are grounded in how retrieval, evaluation, integration and monitoring behave in production, and the roadmap includes the architecture and data work needed to deliver each initiative, not only the business case for it.
Will the roadmap address regulation and risk?
Yes. Each initiative is assessed for data sensitivity, decision impact and regulatory exposure, with reference to frameworks such as the NIST AI RMF, ISO/IEC 42001 and the EU AI Act where they apply. This is an engineering risk view, not legal advice, and regulatory conclusions should be reviewed by your counsel.