In 2026, the question isn't "should we do AI?" but "**how to make AI a measurable business lever**." Many companies invest in POCs and test assistants, only to face the same reality: gains are diffuse and hard to attri...
In 2026, the question is no longer "should we do AI?", but "how to make AI a measurable business lever". Many companies invest in POCs, test assistants, automate a few tasks... then hit the same reality: the gain is diffuse, difficult to attribute, and the data isn't up to par.
The Business & AI topic thus becomes a matter of alignment. Alignment between strategy (where the company wants to create value), data (what makes AI truly actionable), and ROI (how to prove, steer, and arbitrate).
When an AI initiative disappoints, it is generally not because "the model is bad". The most frequent causes are structural.
1) The strategy is implicit. We launch AI because "everyone is doing it", or because a tool is impressive. Result: we optimize a task that doesn't really weigh on the P&L, or we automate a process that is already fragile.
2) Data is a bottleneck. AI depends on data that is accessible, clean, up-to-date, and linked to reality (clients, orders, tickets, operations). However, many organizations still have dispersed data, different definitions across teams, or tools that don't talk to each other.
3) ROI isn't "designed" from the start. Without a baseline, without a test protocol, without steering indicators, it becomes impossible to distinguish a real impact from a placebo effect. You can have decent adoption and zero ROI, or the reverse.
The goal is therefore not to "do an AI project", but to put AI at the service of a business trajectory, with data discipline and a measurement mechanism.
To align strategy and AI, you must translate the strategy into concrete value levers. In most SMEs, scale-ups, and structuring companies, these levers fall into 5 families:
Revenue: acquisition, conversion, upsell, churn reduction, NRR improvement.
Margin: operational productivity, support cost reduction, lowering the cost of non-quality.
Cash: shortening cycles (invoicing, collection, closing), improving forecasting.
Risk: compliance, security, fraud, human error.
Experience: perceived quality, delays, service consistency.
Next, identify the processes where AI can act with a simple mechanism: decide better (classification, scoring, prediction) or act faster (automation, generation, assistance).
A good test: if you cannot link a use case to a business indicator already being tracked (or that you are ready to track), the use case is probably too much of a "gadget" at this stage.
Without falling into an infinite list, here are frequent examples where alignment is often clear:
Customer Support: improving resolution rates, reducing handling time, absorbing growth without multiplying hires.
Sales ops / RevOps: enrichment and qualification, lead prioritization, reducing administrative work in the CRM (to be linked to your sales organization, see the RevOps glossary).
Finance: reconciliations, anomaly detection, reporting generation, accelerating the closing process.
Operations: planning, quality control, root cause analysis from incidents.
The key point: the business sponsor must be explicit (who owns the result), and the gain must be formulated as a testable hypothesis.
AI highlights a sometimes uncomfortable truth: data is not an asset until it is usable.
For a company in a structuring phase, the challenge is not to build a "perfect data platform", but to secure the minimum viable data for your priorities.
Availability: does the data exist (or can we capture it)?
Quality: is it consistent, complete, and sufficiently up-to-date?
Accessibility: can it be accessed easily, without manual extraction, and with clear rights?
Legality and risk: GDPR, trade secrets, sensitive client data, traceability.
Here is a simple grid to decide if a use case is ready, or if you need to invest in data first.
Data signal | Team symptom | Risk on ROI | Priority action |
|---|---|---|---|
Data distributed across 5 unconnected tools | Reporting done "by hand" in spreadsheets | Delays, hidden costs, unusable models | Integrations and automations (light ETL) |
Different definitions of the same KPI | Recurring debates in committees | ROI impossible to attribute | Governance (definitions, source of truth) |
Incomplete or noisy history | The model "stalls" in production | Loss of confidence, abandonment | Targeted cleaning, quality rules |
Sensitive data without a framework | Legal and security brakes | Project stop or high risk | Access policy, anonymization, contractualization |
This approach avoids a classic trap: launching a "priority" AI use case without a reliable data pipeline, then concluding too quickly that "AI doesn't work".
For integration topics (API, permissions, data exposure), an architecture framework is often decisive. Impulse Lab has dedicated content on clean and secure integration models, useful if you are connecting AI bricks to your IS: AI API: Clean and Secure Integration Models.
ROI is not a slide at the end of a project. It is a design constraint.
ROI = (measured incremental gain) / (total cost of ownership)
Where it gets complicated is in the "measured" and in the "total cost". A serious AI initiative must include:
A baseline (before)
An output metric (what the system produces)
An outcome metric (what changes in the process)
An impact metric (what changes in the business)
Guardrails (quality, risks, satisfaction)
Here is a measurement chain model that works well for teams wanting to prove impact without getting locked into an overly complex system.
Level | Example (customer support) | Example (sales) | Why it's useful |
|---|---|---|---|
Output | % of responses proposed by the assistant | % of emails/notes generated | Measures "raw" usage |
Outcome | Average Handling Time (AHT) | Admin time per opportunity | Measures operational effect |
Impact | Cost per ticket, CSAT | Conversion rate, pipeline velocity | Measures business value |
Guardrails | Escalation rate, errors | Opt-out rate, compliance | Avoids "toxic" ROI |
If you want to go further on measurement, you can cross-reference this article with dedicated content: AI KPIs: Measuring the Impact on Your Business.
The shortest path to real ROI rarely goes through a "big project" from the start. It goes through a test protocol that forces clarity.
A robust approach consists of formalizing: hypothesis, test population, duration, metrics, scaling criteria, and stop conditions. On this subject, you can rely on: Enterprise AI Test: Simple Protocol to Validate Your Ideas.
When AI becomes strategic, you don't have "a project", you have a pipeline of initiatives. And therefore an arbitration problem.
A good practice is to steer a portfolio with a common grid, accepting that not all use cases have the same horizon.
Quick wins (2 to 6 weeks): targeted automations, internal assistants, extraction and synthesis, simple classification.
Core process (6 to 16 weeks): IS integration, data quality, process change, adoption.
Differentiation (3 to 9 months): custom platforms, product advantage, data moats.
Criterion | Question | Score (1-5) |
|---|---|---|
Business Value | What impact on revenue, margin, risk, or experience? | |
Frequency | How many times per week/month does the case occur? | |
Data Readiness | Does the data exist and is it exploitable? | |
Feasibility | Integration complexity, dependencies, delays | |
Adoption | Will teams use it without friction? | |
Risk | GDPR, errors, reputation, compliance |
You can then prioritize by (Value + Frequency + Adoption) versus (Data readiness + Feasibility + Risk), to obtain a realistic roadmap.
AI increases speed, but also the surface of risk: sensitive data, automated decisions, hallucinations, bias, vendor dependence.
Two useful benchmarks in 2026:
The European regulatory framework, with the AI Act (European Commission), which structures obligations according to risk levels.
A risk management framework applicable in business, such as the NIST AI Risk Management Framework (useful for structuring policies, controls, and responsibilities).
Without going into a full audit, a pragmatic foundation includes:
Data rules: what can be sent to an external model, what must remain internal, and how to trace it.
Human controls: where the human validates, where AI assists, where AI executes.
Observability: logs, quality sampling, drift metrics, incidents.
Responsibilities: a business owner, a technical owner, and a governance lead.
If you need to frame this quickly, a good entry point is a structured diagnosis. Impulse Lab details a risk and opportunity mapping approach here: Strategic AI Audit: Mapping Risks and Opportunities.
For an SME or a scale-up, the goal of the first month is not to "deploy AI everywhere". It is to make the decision rational and obtain a first measurable impact.
Choose 1 to 2 levers (margin, revenue, cash, risk, experience) that are truly priority.
List 10 recurring operational pain points (lost time, errors, bottlenecks).
Formulate 3 result-oriented AI hypotheses (with a business owner).
Map sources (CRM, support, billing, product, operations).
Identify "sources of truth" and KPI definitions.
Decide what is missing and what can be fixed quickly.
Baseline, metrics, guardrails.
Test population, duration, success criteria.
Total cost estimation (tools, integrations, team time, run).
Build a usable flow (even if simple), integrated into daily life.
Train the teams concerned (usage, limits, responsibilities).
Measure, iterate, decide (scale, stop, or invest in data).
This plan may seem "non-technical", but it is exactly what conditions the transition from POC to ROI.
When you have a validated use case, the question becomes: how to make it reliable, maintainable, and aligned with your IS? This is often where the gap widens between a company that "uses AI tools" and a company that creates an advantage.
Industrialization generally implies: integrations, automation, governance, security, UX, change management, and a delivery cadence that allows for fast learning.
Impulse Lab supports these trajectories with AI opportunity audits, adoption training, and the development of custom web and AI solutions, with a product-oriented and production-focused approach. If you want to frame a roadmap or challenge your priorities, you can discover the agency at Impulse Lab.
To deepen the ROI angle in a complementary way (methods, trade-offs, classic errors), this content can also help you: Transforming AI into ROI: Proven Methods.
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