# Forward-deployed engineering, management consultancy or staff augmentation?
Choose management consultancy for strategy and business cases, staff augmentation when you already own the architecture and roadmap, and forward-deployed engineering when an ambiguous operational problem must become a working production system.
## Match the model to the work in front of you
For a CTO, COO or operating partner commissioning AI or workflow software, the delivery model determines what you are left with at the end. Management consultancies, staff augmentation agencies and forward-deployed engineering teams solve different problems and carry different risks. Problems arise when an organisation buys one model while expecting the output of another.
## When traditional management consultancy fits
Management consultancy is designed for board-level alignment, market analysis, operating-model design and investment cases. If the primary question is which markets to enter or how to structure a multi-year transformation budget, an advisory team is built for that work. Where advisory-led engagements struggle is the handover from recommendation to production software: a slide deck or high-level target architecture does not resolve dirty source data, undocumented operational exceptions or user resistance.
## When staff augmentation or an outsourced build team fits
Staff augmentation adds contractors into an existing engineering organisation where product management, technical architecture and evaluation standards are already established in house. Outsourced development teams work well when requirements are stable and can be specified in advance. Both models struggle when applied to new AI or workflow systems where the requirements only become clear once real users test the software on real cases.
## When forward-deployed engineering earns its keep
Forward-deployed engineering places senior software, data and AI engineers directly alongside the domain team that owns the problem. Instead of waiting for a finished specification, they sit with operators in the first weeks, connect to live systems, build a working vertical slice and iterate with users until the workflow changes. The team takes accountability for a production outcome rather than filling seats or delivering a report.
## Questions to ask before you sign
Ask whether the senior engineers you meet during scoping are the people who will write the code and sit with your operators. Ask what working software will exist in your environment after four weeks, how the system will be evaluated against real edge cases and how ownership transfers to your internal team at the end. If the deliverable is a document rather than a governed system in production, price and scope it as advisory work.
## Related reading
- [How to put document AI into an operational workflow](/solutions/guides/implementing-document-ai)
- [Document AI vs OCR: what does your workflow need?](/solutions/guides/document-ai-vs-ocr)
- [How to choose an AI engineering partner](/solutions/guides/choosing-an-ai-engineering-partner)
- [AI consultancy or an in-house AI team?](/solutions/guides/ai-consultancy-or-in-house-team)
- [Document processing for lenders](/solutions/lending-document-processing)
- [Legal case intelligence](/solutions/legal-case-intelligence)
- [AI knowledge search for professional services](/solutions/professional-services-knowledge-search)
- [AI-assisted case triage and review](/solutions/ai-case-triage)
- [AI document review for advisory firms](/solutions/ai-document-review)
- [AI readiness assessment](/solutions/ai-readiness-assessment)
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