Deciding with your data: what agentic AI really changes

This article sets out an augmented decision method in which AI prepares analyses and scenarios while the company retains control of its data, checks and final judgement.

• Audience: SME and mid-cap leaders, data managers, and finance or operations teams.
• Scope: decisions that can be quantified from internal data, illustrated through pricing, margin and volume questions.
• Thesis: compute has become much cheaper, while data preparation and verification remain substantial investments.

By Paul-Antoine TUAL, AI Transformation Leader, Croissance et Transitions. August 2026, revised September 2026.

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1. A better-informed leadership decision

A 4% price rise, the closure of a product line or the recruitment of a salesperson can now be examined faster when the required data is accessible and its definitions are stable.

• Hypothesis: state the decision, horizon and evidence that would make you abandon it.
• Data: assemble volumes, margins, discounts, logistics costs, attrition and operational capacity.
• Output: compare several scenarios instead of requesting one recommendation.

A properly connected agent can prepare the dossier in minutes or hours, although speed says nothing by itself about the quality of the data, allocation rules or conclusions.

• It retrieves three years of sales from the ERP and CRM under least-privilege access.
• It recalculates margin after a documented allocation of logistics costs.
• It exposes seasonality, assumptions and sensitivities for every scenario.

IBM’s global 2026 survey shows that leaders are open to AI-generated input, while measuring a stated attitude rather than decision quality or the maturity of French SMEs [9].

• Sample: 2,000 CEOs and equivalent senior leaders across 33 geographies and 21 sectors.
• Finding: 64% say they are comfortable making a major strategic decision from AI-generated input.
• Cautious reading: analysis complements experience; it transfers neither judgement nor accountability to the model.

2. Separate compute, access and production

Comparing an agent task with a complete data project creates a false equivalence because the two purchases cover different assets, risks and time horizons.

• Data project: pipelines, reference data, data model, security and recurring operations [1].
• One-off analysis: collection, business framing, interpretation and presentation [3].
• Agent task: computation and synthesis inside an environment that has already been prepared.

Official pages checked on 6 September 2026 confirm low access and execution costs, while making clear that prices vary with licences, region, volume and tools invoked [5][6][7].

• Microsoft lists Microsoft 365 Copilot at €28.10 excluding VAT per user per month on annual billing, alongside a qualifying licence [5].
• Anthropic lists Claude Pro at $20 per month, or $17 per month on annual billing, subject to usage limits [6].
• Google estimates a standard Deep Research task at $1–$3 under preview pricing and says cost depends on research depth [7].

The agentic advantage lies in a controlled sequence of operations, while MCP provides an open protocol for exposing tools and context sources to AI applications [6].

• An agent can read a file, run a query, apply a business rule and produce a traceable dossier.
• The connector should restrict authorised resources, operations and identities.
• Token budgets, tool calls and error recovery remain measurable costs.
• Marginal cost falls most when the company has already funded its data, access and controls.

3. Turn intuition into testable assumptions

A useful decision dossier makes assumptions contestable and links every scenario to observable data without presenting the model’s projection as a certain forecast.

• Base: a central scenario calculated from validated data and a relevant period.
• Variants: high and low assumptions for variables that genuinely drive the result.
• Refutation: thresholds that invalidate the preferred scenario and a premortem of failure causes [27].
• Decision: reversal cost, signals to monitor and a review date.

Field experience remains valuable for asking the right question, and an explicit procedure can then reduce anchoring, confirmation, status quo and overconfidence biases documented by decision psychology [26].

• Write down the intuition before seeing results to avoid rewriting the hypothesis afterwards.
• Ask for evidence against the preferred scenario to counter confirmation bias.
• Vary visible assumptions to expose the influence of the first number proposed.
• Record the choice and actual outcome to calibrate intuition over time.

Bpifrance Le Lab’s 2026 barometer illustrates the gap between adoption and reported gains among French mid-caps, without allowing that gap to be attributed to any particular method [10].

• 77% of respondents report professional generative AI use by themselves or their staff.
• Among AI-using mid-caps, 17% report time savings, equivalent to 13% of the full sample.
• The result supports measuring each use case against a defined indicator rather than inferring broad productivity from adoption.

4. Verify in proportion to the stakes

BIRD and Spider 2.0 show that performance varies sharply with the task and protocol, which supports local testing without turning a benchmark score into a business error probability [11][12].

• BIRD publishes a 92.96% human reference and an 81.95% historical best on its execution evaluation [11].
• Spider 2.0 covers enterprise tasks of different complexity and warns that scores may move as evaluation issues are corrected [12].
• Tracks, data and settings differ; Figure 2 shows dated laboratory results rather than a universal ranking.
• A 2026 study also identifies annotation errors in text-to-SQL reference sets [13].

Long-running research evaluations likewise support criterion-by-criterion review because a fluent answer can omit requirements or propagate an error between stages [14][15].

• Check coverage, accuracy, freshness and source diversity separately.
• Bind decisive calculations to a system of record rather than a secondary citation.
• Ask the accountable business owner to review assumptions and exceptions.

A robust control combines technical traceability, arithmetic verification and business reconciliation, with effort scaled to the potential cost of an error.

• Retain the query, data version, filters and transformations.
• Recalculate a representative sample and every sensitive total by hand.
• Reconcile revenue, margin and receivables with the relevant accounting systems.
• Review unexplained discrepancies before presenting the dossier to the executive committee.

5. Prepare governed, usable data

Vendor-sponsored surveys published in 2026 frequently flag silos and data quality, although their large-organisation samples do not automatically describe a French SME [16][17].

• Cloudera/HBR Analytic Services reports 7% of respondents calling their data “completely ready” and 56% citing silos [16].
• Nasuni/Sapio reports that 46% saw their initiatives reveal quality or governance problems [17].
• These findings are risk signals; a local assessment should measure the company’s actual data, permissions and uses.

A reusable agent-ready foundation combines quality, access, security and history so that later analyses can reuse governed assets.

• Reference data: one stable identifier for each customer, product, supplier and legal entity.
• Access: monitored APIs or connectors, least-privilege scopes and separate service identities.
• Confidentiality: classification of margins, salaries and cash data before any external processing.
• Memory: retained assumptions, decisions, outcomes and corrections for the next cycle.
• Control: apply the agentic-system rules and sovereignty framework to the actual use case.

6. Avoid generic recommendations

A 2026 exploratory study of seven models and seven strategic tensions finds frequent convergence towards socially valued options, a useful design signal that remains insufficient to predict every recommendation produced in business [19].

• The protocol covers 15,000 simulations and reports convergence on six of seven tensions.
• The effect depends on the models, dilemmas and simulated contexts studied.
• A generic recommendation should therefore be tested against the company’s own constraints and data.

Private data can make analysis more specific when it is relevant, clean and correctly interpreted, without becoming an automatic defence against copying or error.

• Ten years of customer history may reveal cohorts and cycles absent from public data.
• Actual margins and segment conversion rates provide company-specific constraints.
• Metric definitions, controls and feedback speed complete that asset, as discussed in the end of the model race.

For buyers, the practical consequence is to purchase a better-informed decision rather than a volume of pages, requiring traceability, explanation and the ability to challenge the first answer.

• The deliverable should connect every decisive claim to verifiable data or a source.
• The supplier should state assumptions, limits and human controls.
• Value should be judged on decision quality and observed outcomes, rather than analysis volume.

7. A five-step leadership protocol

The protocol consists of five successive steps that produce a comparable, auditable and reversible dossier before the final decision.

• Write the decision and its turning-back criterion. One sentence: “Should we do X before Y, given Z”. Then the reversibility criterion: what would make us say no, and how much turning back costs. Your intuition sets the hypothesis; that is its place, and decision-gated development makes it the first guardrail.
• Connect the agent to the systems. A connector with minimal access rights to the ERP, the CRM or the warehouse, rather than three-week-old manual exports that freeze figures already wrong.
• Require scenarios, their assumptions and their refutation. Three quantified trajectories, written assumptions, sensitivity analysis, and the premortem of the preferred scenario: what kills it, with what probability, detectable through which signal.
• Control before believing. The rules of section 4: queries and sources required, a second independent engine, a hand-recounted sample, accounting reconciliation. Any unexplained gap is an alert, not a detail.
• Decide in session, with a cool head. Group the trade-offs into a dedicated decision session, as we detail in the personal effectiveness method, and keep the human signature on everything that commits the company.

IBM’s survey describes an expectation of greater automation for codifiable operational decisions, reinforcing the need to define eligible decisions, exceptions and accountable owners in advance [9].

• Respondents expect 48% of operational decisions where consistency and guardrails can be codified to be made without human intervention in 2030, up from 25% at the time of the survey.
• This is a respondent forecast; it guarantees neither adoption speed nor decision quality.
• Figure 3 therefore places analysis in a loop where humans frame, verify, decide and measure.

The full cost has shifted

AI reduces the cost of generating an analysis, while decision value still depends on data quality, calculation checks and accountable judgement.

• Cheaper: variants, exploratory queries, synthesis and presentation.
• Still costly: integration, security, reference data, business validation and supervision.
• Still human: defining the stakes, accepting risk and making decisions that commit the company.

A first experiment can use one real, reversible decision and compare it with the usual process against criteria defined before the test.

• Choose a dossier for the next executive meeting and freeze the starting data.
• Compare elapsed time, full cost, calculation discrepancies, assumption quality and usefulness.
• Decide whether to industrialise, repair the data foundation or stop the use case.

The Junyr AI maturity audit is a free, no-commitment 30-minute video call that positions the company on the scale, identifies its main blocker and first sensible project, and provides a one-page follow-up.

• Bring: a recent decision, the available data and its validation path.
• Receive: a position, a priority blocker and a proportionate first step.

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References and reading limits

[1] Fenxi, Combien coûte un projet data en 2026 : prix et délais (fenxi.fr, published 30 March 2026, updated 10 August 2026): foundational SME project €10,000 to €30,000, advanced project with AI €30,000 to €60,000 and above; client examples (€18,000 and 6 weeks for 40 employees, €12,000 for 80 sites); recurring data stack from €300 to €1,000/month (small structure) to €1,500 to €5,000/month (structured SME).

[2] Data analyst salary surveys, France 2026, convergent secondary sources (Glassdoor and specialist schools, consulted 18 August 2026): national median around €45,000 gross per year, €35,000 to €42,000 at junior level, more in Paris. Orders of magnitude not verified against a single primary source, cited as reference points.

[3] IntoTheMinds, Combien coûte une étude de marché ? (published 26 February 2025, updated 12 April 2026): most projects between €8,000 and €12,000; competitive analysis €5,000 to €10,000.

[4] Vendr, Gartner and Forrester pricing pages (updates dated February 2026, SaaS purchasing platform, intermediary reading): Gartner subscription $25,000 to $60,000 per year for targeted access, $100,000 to more than $500,000 for extended access, advisory hours $3,000 to $7,000; Forrester, observed median $65,755 per year.

[5] Microsoft, French Microsoft 365 Copilot pricing page (checked 6 September 2026): €28.10 excluding VAT per user per month on annual billing, alongside a qualifying Microsoft 365 licence.

[6] Anthropic, Claude pricing and MCP documentation (checked 6 September 2026): Claude Pro costs $20 monthly or $17 per month on annual billing; MCP is described as an open protocol standardising access from AI applications to context sources and tools.

[7] Google, official Gemini Deep Research agent documentation (checked 6 September 2026): estimated cost of $1–$3 for a standard task and $3–$7 for Max, depending on preview pricing and research depth.

[8] TechCrunch and MacRumors (9 April 2026): launch of a $100-per-month ChatGPT tier, slotted between the $20 and $200 plans.

[9] IBM Institute for Business Value, global leadership study (4 May 2026; 2,000 CEOs and equivalent senior leaders, 33 geographies, 21 sectors, fieldwork February to April 2026): 64% report comfort making major strategic decisions from AI-generated input; respondents project 48% for operational decisions whose guardrails can be codified in 2030, up from 25% at the time of the survey.

[10] Bpifrance Le Lab, 16th French mid-cap barometer (23 June 2026; 534 responses, fieldwork 11 March to 18 May 2026): 77% report professional generative AI use; 17% of AI-using mid-caps report time savings, equivalent to 13% of the full sample.

[11] BIRD, official leaderboard (checked 6 September 2026): published human reference of 92.96% and displayed historical best of 81.95% on execution evaluation; the project documents cleaning work on the dataset.

[12] Spider 2.0, official leaderboard (checked 6 September 2026): the site describes 632 problems derived from enterprise use cases and notes that scores may change when evaluation issues are corrected.

[13] Jin, Choi, Zhu and Kang (University of Illinois Urbana-Champaign), arXiv:2601.08778, v3 of 19 January 2026: systematic annotation errors identified in the reference answers of text-to-SQL benchmarks, including BIRD and Spider 2.0.

[14] Li, Du, Xu, Zhu, Wang and Mao, DeepResearch Bench II, arXiv:2601.08536 (January 2026): 132 research tasks, 9,430 criteria drawn from more than 400 hours of expertise; the strongest models satisfy fewer than 50% of the criteria.

[15] Zhan, Fan, Huang, Guo and Huang (Zhejiang University, University of Hong Kong), arXiv:2601.22984 (30 January 2026): hallucination propagation along the trajectory of deep research agents; no agent robust end to end; temporal anchoring and source homogeneity biases.

[16] Cloudera and Harvard Business Review Analytic Services, release of 5 March 2026 (October 2025 survey, more than 230 data and AI decision-makers): 7% of data judged “completely ready” for AI; 56% cite data silos, 44% the lack of a data strategy, 41% quality or bias.

[17] Nasuni, Sapio Research survey, release of 18 May 2026 (1,000 decision-makers, companies of more than 1,000 employees, United States, United Kingdom, France, Germany-Austria-Switzerland, fieldwork March 2026): 97% deploying or piloting AI agents; 57% of AI projects fail to meet their objectives; 18% at scale; 46% saw their AI initiatives reveal data quality and governance problems.

[18] Efalia, Baromètre DSI Data et IA 2026 (efalia.com, 11 August 2026; methodology not detailed on the page consulted): 96% of organisations judged not ready for AI; 85% of CIOs state they do not control the quality of their document estate; 62% devote less than 1% of the IT budget to it.

[19] Romasanta (Esade), Thomas (Sydney and Imperial) and Levina (NYU Stern), Researchers Asked LLMs for Strategic Advice. They Got Trendslop in Return, Harvard Business Review (16 March 2026); methodology and quotations relayed by Fortune (10 April 2026): 7 models, 15,000 simulations, 7 strategic tensions, convergence on 6 tensions out of 7 towards the socially valued option; closing quotation on leadership cited verbatim.

[20] Ramírez, Lang, Köhler and Mennell, A Faster Way to Build Future Scenarios, MIT Sloan Management Review (9 December 2025, Winter 2026 issue): Fazer and Unum cases; the classic approach is described as “waiting six months or more for useful insights”; no quantified gain claimed.

[21] Syntec Conseil, 30th strategy and management consulting barometer, via La Lettre du Conseil (11 June 2026; 140 firms surveyed from February to June 2026): 1.5% decline in 2025, sixth-worst year since 2002; Consultor, 2025 review and 2026 outlook for strategy consulting (17 June 2026): +2% anticipated in 2026 by the same study, consultancies’ AI usage now a client negotiation argument.

[22] BCG, 2025 results release (23 April 2026): $14.4 billion in revenue (+7%, 22nd consecutive year of growth); AI- and technology-centred services account for more than 40% of total revenue, driven by 25% year-on-year growth in AI services; 33,500 employees.

[23] Consultor, Chez McKinsey, l’IA s’invite aux entretiens de recrutement (27 January 2026): case study worked with the internal assistant Lilli; assessment of prompt quality, critical thinking in the face of outputs, and the ability to contextualise them.

[24] Bernard Marr, How Klarna’s AI Agent Strategy Backfired But Became a Useful Lesson, Forbes (16 July 2026): 2.3 million conversations in the first month, resolution time from 11 to 2 minutes, support staff from 5,000 to 3,500 people, then rehiring of around a hundred specialists; CEO Sebastian Siemiatkowski’s admission as reported by Forbes (“cut too far, too quickly”, loss of valuable human expertise).

[25] Journal du Net (7 July 2026), analysis of the April 2026 French inter-inspectorate report (IGF, IGAS, IGA) on AI in the public sector: up to 40% unregulated AI use among local government staff; conversational assistants rarely SecNumCloud-certified; companies’ strategic data addressed only marginally.

[26] Daniel Kahneman, Thinking, Fast and Slow (2011): confirmation, anchoring, overconfidence and status quo biases in intuitive decision-making.

[27] Gary Klein, Performing a Project Premortem, Harvard Business Review (September 2007): the premortem technique, projecting oneself into the project’s failure to list its causes before deciding.