Custom AI consulting to transform business processes

Updated March 2026 - A firm offering custom AI consulting does not simply install a generic tool. It starts from your real processes, identifies operational friction and bottlenecks, prioritises the use cases that deliver measurable returns on investment and builds an implementation that is compatible with your data environment, your governance requirements and your actual organisational objectives.

In 2026, AI adoption by micro-businesses and SMEs is reaching an inflection point. According to the 2025 France Num barometer, which surveyed over 11,000 companies, 26% of SMEs now use artificial intelligence solutions — double the rate of just one year earlier. Generative AI dominates usage at 22% adoption (+12 points), followed by chatbots and virtual assistants (14%, +9 points). Behind these figures lies an operational reality: companies that have integrated AI are seeing concrete gains in productivity, service quality and reduction of repetitive workloads.

Two companies in the same sector never have exactly the same processes, information systems, regulatory constraints or working habits. An off-the-shelf AI solution — configured identically for every organisation — rarely delivers sustainable results.

This is precisely where a custom AI consulting firm adds value: it does not start from technology, but from your operational reality. It maps your workflows, identifies friction points, evaluates the quality and availability of your data, then designs a response tailored to your context. This approach is all the more relevant given that 37% of SMEs report difficulty finding a suitable digital provider (France Num 2025 barometer, +15 points in one year). The demand for personalised support is clear and growing.

Certain business processes are particularly well-suited to AI integration. Field experience and official data converge on four families of use cases:

• document processing and qualification: automatic reading, categorisation and extraction of key data from invoices, contracts, compliance documents and correspondence. Document analysis adoption is at 6% (+3 points), but the real potential is far higher for companies with significant document volumes;
• customer support and assisted response: generating first-draft responses, classifying support tickets by priority, routing contacts to the right team member.

Chatbots and virtual assistants already represent 14% of AI usage in SMEs;

• content and analysis production: structured drafting of reports, summaries, financial analyses and communications from raw data inputs. Generative AI, at 22% adoption, is the primary driver of this transformation;
• information search and extraction: querying large document repositories to quickly find contractual clauses, regulatory références or précédents — dramatically reducing research time for knowledge-intensive workflows.

Beyond these four areas, task automation (5%, +2 points) and data analysis (5%, +2 points) are two fast-growing segments that directly impact business steering. The France Num barometer also notes that 75% of SMEs already exploit their data to manage their operations, with accounting and financial data leading the way (64%).

A custom AI project is not improvised. We recommend a structured four-phase approach:

1. Mapping existing workflows — Document the target process in its entirety: input data types, volumes, current processing steps, error rates, recurring bottlenecks and stakeholders involved. This phase is essential: you cannot improve what you do not understand.

2. Prioritisation by value and feasibility — Not every process deserves an AI intervention. The prioritisation matrix cross-références potential operational gain against data availability, regulatory constraints (GDPR, trade secrets) and integration complexity with your existing tools.

3. Data and compliance scoping — What data will the AI system process? Where is it hosted? Which GDPR obligations apply? Bpifrance's "Osez l'IA" programme, which has €25 million allocated to support SMEs and mid-market companies, includes a Data AI diagnostic (3 to 10 days, 80% funded) that covers precisely these aspects.

4. A limited but measurable pilot — Before a full rollout, an operational pilot on a real process with defined success indicators. Not a technical demonstration, but a real-world test whose impact can be measured: time saved, error rate reduced, processing cost decreased.

Hayot Expertise advice: the most valuable AI project is not the one that impresses in a demo. It is the one that removes a recurring bottleneck — reliably and repeatedly — without weakening quality or internal control. The companies getting the best results treat AI as a précision tool, not a universal solution.

Integrating artificial intelligence into your processes can be partially funded. Bpifrance's "Osez l'IA" (Dare AI) programme offers multi-phase support:

• Phase 1 (free): awareness-raising and online AI self-assessment to evaluate your company's maturity;
• Phase 2: personalised Data AI diagnostic conducted by an expert (cost between €3,900 and €13,000 excl. tax, 80% covered by Bpifrance);
• Phase 3: feasibility study and AI solution selection (in-house development vs. market solution);
• Phase 4: experimentation of the solution on a defined scope, with a support ceiling of €60,000 excl. tax.

This programme, part of the France 2030 strategy, is available to SMEs and mid-market companies with 10 to 2,000 employees and revenue above €250,000 excl. tax. Micro-businesses can benefit from the awareness phase, which is open to all companies.

See also how AI can accelerate business growth in 2026, digital transformation consulting and accounting digitalisation.

Failed AI projects generally share the same root causes:

• starting with the tool rather than the process: buying a licence before identifying the problem to solve almost always leads to underutilisation;
• neglecting data quality: an AI model is never better than the data that feeds it. Incomplete, inconsistent or poorly structured data will produce unreliable results;
• ignoring change management: even the best technical solution will fail if the teams who must use it are not involved, trained and supported;
• aiming for perfection from day one: an imperfect but operational pilot is worth far more than a perfect project that never ships.

We can help you identify concrete use cases, evaluate their potential impact and structure a measurable pilot before any broader rollout. Our approach combines a financial, process and organisational lens to ensure that every AI project creates real, measurable value.

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