Choosing an **AI solution** in 2026 is no longer just about "picking the best model". The real question is almost always: **what product format and level of integration** to achieve a measurable ROI, without blowing up run costs, complexity, or risks (GDPR, AI Act, security)...
Choosing an AI solution in 2026 is no longer just about "picking the best model". The real question is almost always: what product format and level of integration to achieve a measurable ROI, without blowing up run costs, complexity, or risks (GDPR, AI Act, security)?
Between a ready-to-use SaaS, an assembly of building blocks, and a custom build, all three options can be "good". They are just not good for the same constraints.
A SaaS (Software as a Service) is a packaged cloud tool sold by subscription. You buy a capability (e.g., writing assistance, support agent, document extraction) with an already designed interface and workflows.
Main advantage: fast time-to-value.
Main risk: you adapt your process to the tool, and not the other way around.
If you need a refresher, Impulse Lab has a clear definition of the SaaS model.
"Assembling" means building your solution from existing components: AI APIs (LLM, vision, audio), orchestration, RAG, automation, connectors, knowledge base, security layer, plus a minimal interface.
It is not "magic" no-code. It is often a lightweight and integrated product, whose value comes from the complete chain (data → decision → action), not from a screen.
Some common building blocks (depending on the use case):
API / RAG / agents pattern (useful for moving from demo to production): see AI integration in business: API, RAG, and agents patterns
Standard connections (CRM, helpdesk, ERP, Google Workspace)
A context integration protocol (e.g., MCP)
Custom-built consists of developing a specific solution, designed for your users, your constraints (data, security, processes), your UX, and your differentiation.
Main advantage: perfect control and alignment with the business.
Main risk: cost and time, but above all the obligation to operate (tests, observability, runbook, iterations).
Most "bad choices" come from a bias: comparing features instead of comparing constraints.
Here is a simple (and generally sufficient) grid to decide.
Criterion that really matters | SaaS | Assemble | Custom |
|---|---|---|---|
Timeframe for a useful V1 | Very fast | Fast to medium | Medium to long |
IT system integration (CRM, ERP, helpdesk, SSO) | Variable, often limited | Strong (targeted) | Very strong |
Business customization (rules, exceptions) | Limited | Good | Excellent |
Control (security, logs, degraded modes, SLA) | Depends on the provider | Good if well designed | Very good (if industrialized) |
Reversibility (changing blocks, avoiding lock-in) | Often low to medium | Good | Good (but you handle the run) |
Total Cost of Ownership (TCO) at 12-24 months | Low at first, can rise | Often optimizable | Variable, depends on the run |
Product/process differentiation | Low | Medium to strong | Strong |
Quick read:
SaaS is a good choice if your need is standard and your goal is to execute fast.
Assemble is often the best ratio when the value comes from integration and workflow.
Custom makes sense when you are looking for differentiation, control, or a non-standard business UX.
If your need looks like that of 1,000 other companies (e.g., transcription, summaries, generic content generation), a SaaS is often sufficient.
If your use case has specific rules, exceptions, or its own repository (catalog, contracts, internal procedures), you quickly shift towards assemble (RAG + integrations) or custom.
If the AI must act (create a ticket, enrich a CRM, generate a quote, trigger a follow-up), integration becomes central.
In this case, isolated "chat" solutions tend to fail: they answer, but they do not close the loop.
Two simple questions:
What does an AI error cost (time, money, reputation, compliance)?
Do you need traceability (sources, logs, human validation)?
The higher the risk, the more you need architecture, guardrails, and validation. This pushes towards assemble or custom.
If you process sensitive data (customer, financial, HR, health data, trade secrets), you must clarify:
where the data goes,
who has access to it,
how long it is kept,
how you audit its usage.
In France, trade-offs are often made with DPO and security teams according to GDPR principles. A useful resource from the regulator: CNIL, AI and data protection.
As soon as the AI needs to answer based on your documents (procedures, T&Cs, offers, knowledge base, specs), a RAG becomes the most frequent pattern.
To understand the principle without falling into complexity, the RAG (Retrieval-Augmented Generation) definition is a good starting point.
If your teams have to use AI all day (support, ops, sales, finance), UX is an ROI factor. A SaaS might be enough if the interface fits their daily routine. Otherwise: assemble with a targeted interface, or custom.
The classic trap: choosing based on monthly cost (SaaS) without counting:
the time lost due to lack of integration,
workaround costs,
knowledge debt (undocumented prompting, unspoken rules),
the drift of variable costs (API usage, volume).
Conversely, the custom trap: funding a V1 without funding its operation (tests, monitoring, iterations, ownership).
A useful AI solution almost always has:
a baseline (before),
1 North Star KPI,
2 to 4 steering metrics,
1 to 2 guardrails (quality, risk, cost).
If you cannot instrument this, the problem is rarely "the model". It is the scoping.
Use case | What determines the choice | Frequent choice |
|---|---|---|
Writing copilot (emails, posts, generic summaries) | Standard need, low integration | SaaS |
Meeting summaries + tasks in your CRM | Action in your tools + traceability | Assemble |
Support assistant that cites the internal base + suggests an answer | Source of truth + quality control | Assemble (RAG) |
Agent that creates/edits tickets, triggers workflows, checks rules | Multi-tool actions + guardrails | Advanced assemble or custom |
AI tool at the core of your product offering (differentiation) | UX, roadmap, control, reliability | Custom |
Document processing (invoices, contracts) with specific rules | Business exceptions + audit + compliance | Assemble or custom |
In many SMEs and scale-ups, assembly wins because it allows you to:
deliver fast,
integrate into real workflows,
maintain reversibility (change models, change building blocks),
avoid "rebuilding" what the market already does well.
The tipping point towards custom generally happens when:
UX is strategic,
compliance requires fine-grained control,
differentiation is central,
integrations and business rules become too specific.
The TCO of an AI solution does not only come from the sticker price. The areas that often cause overruns:
Integration: SSO, roles, permissions, synchronization, webhooks.
Knowledge quality: updating sources, ownership, versioning.
Evaluation: recurring test suites, "golden" cases, regression tracking.
Observability: logs, correlation, costs, latency, error rates.
Run: support, incidents, degraded modes, vendor management.
Adoption: targeted training, usage rules, internal champions.
The right choice is not the one that minimizes a single area, it is the one that minimizes lost value (time, errors, opportunities) for an acceptable level of risk.
In 2026, compliance is no longer an afterthought. It dictates the ability to deploy.
Three pragmatic principles:
A simple classification (green, orange, red) is often enough to decide what can go into a SaaS, and what must be controlled via a gateway, a controlled assembly, or a custom build.
Whether it is a SaaS or a custom project, ask for verifiable elements:
retention policies,
audit logs,
access control,
flow documentation,
evaluation protocol.
An AI assistant in production needs an owner, iterations, and a usage framework. Otherwise, you get a POC that works on Tuesday and fails on Thursday.
For the European framework, you can consult the European Commission's reference page on the EU AI Act.
If you are hesitating, here is a sequence that reduces risk without slowing down:
Define: user, job-to-be-done, baseline, KPI, data, constraints, integrations.
Often: 1 SaaS and 1 assemble, or 2 SaaS with integration requirements.
Decide with a scorecard: impact, quality, risks, integration, run, costs.
This is exactly the goal of an opportunity audit or an instrumented pilot.
Impulse Lab steps in when you want a useful, integrated, and manageable AI solution, not a demo.
Depending on your context, the team can:
conduct an AI opportunity audit (use cases, ROI, risks, prioritization),
build a V1 in short cycles (with weekly deliveries),
set up integration with your existing tools (API, RAG, agents, automation),
train your teams to accelerate adoption.
If you want an overview of the support options (audit, training, custom), this guide is a good complement: AI services: audit, training, or custom, what to choose?
And if you suspect that custom development is necessary, you can delve deeper into the selection criteria: Custom AI development: criteria for a rational choice
In practice, the right starting point is often a short scoping session: one hour to clarify constraints and KPIs, then a realistic test plan. You can discuss this directly via Impulse Lab.
Our team of experts will respond promptly to understand your needs and recommend the best solution.
Got questions? We've got answers.
Leonard
Co-founder
