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Strategic monitoring - Industrial technologies / automation

Summary excerpt presented by SRPInnov

Sector: Industrial technologies / automation
 Period covered: January–March 2025

1. Executive Summary

During this quarter, several major developments were observed in the field of industrial automation, AI applied to production processes and connected systems.

Three critical trends identified:

  1. Acceleration of the adoption of industrial AI : +38% of investments announced by major European players.
  2. Emergence of new industrial connectivity standards (IIoT 2.0) , driven by regulators and several international consortia.
  3. Strengthening of cyber constraints on industrial infrastructures , with two new directives in preparation.

Early warning signs to watch for:

  • Arrival of low-cost modular micro-robots developed by an Asian startup.
  • First European factories without operators present in continuous production.
  • New economic models based on the rental of industrial AI.

Impact on a large company:

  • Increased pressure on competitiveness (automation = massive cost differential).
  • The need to anticipate regulatory changes to avoid compliance risks.
  • Opportunity for strategic repositioning in emerging industrial markets.

2. Methodology (summary)

The monitoring is based on a corpus of more than 230 sources:

  • Scientific publications and sector reports
  • Patent filings (Espacenet, USPTO)
  • Corporate announcements, fundraising, acquisitions
  • European regulatory frameworks
  • Technical communities (IEEE, automation forums)
  • International trade press

The information was analyzed and consolidated via:

  • Impact matrices / probability
  • Competitive comparative analysis
  • Actor mapping
  • Targeted PESTEL analysis

3. Major Trends

🔹 Trend 1: Explosion of AI in production chains

Description :
 AI solutions dedicated to industrial processes (predictive, anomaly detection, energy optimization) are becoming widespread rapidly.

Key data:

  • +45% adoption projected in Europe by 2026
  • 62% of factories with more than 500 employees are testing an autonomous AI module
  • Deployment costs have decreased by 27% in two years.

Impact :

  • Strong pressure on non-automated businesses
  • Productivity differential of between 15 and 28% observed depending on the sector

Opportunities :

  • Reduction of industrial downtime
  • Optimizing energy costs

Risk :
 Ninth European company lagging behind → loss of competitiveness in 2026.

🔹 Trend 2: Next-generation IIoT (IIoT 2.0)

Description :
 New protocols that are more secure, faster, and interoperable.

Data :

  • 3 industrial consortia launched in Germany and Korea
  • New standards are expected by the end of 2025.
  • Regulatory pressure on data collection and security

Impacts:

  • Upgrades to existing machines will be necessary.
  • Opportunity to transform current investments into competitive advantages

🔹 Trend 3: Increased cybersecurity regulations in industry

Description :
 Europe is preparing two directives targeting critical industrial networks.

Key points:

  • Obligation to integrate secure protocols on machines
  • Mandatory cybersecurity audits by 2027
  • Increased fines for non-compliance

Impacts:

  • Need for cybersecurity investments → medium term
  • Risk of line shutdowns if non-compliance

4. Actor mapping (summary)

Major established players:

  • Siemens
  • Rockwell Automation
  • Schneider Electric
  • ABB Robotics

Recent movements:

  • Siemens acquires a German AI startup (energy optimization)
  • ABB invests €42 million in a micro-robotics R&D center

Emerging players / startups:

  • Hoshin Robotics (Asia): Modular micro-robots — weak signal
  • NeuraLoop (Finland): Autonomous AI for ultra-fast lines
  • DeepPlant (US): Full-stack industrial digital twins

Disruptive potential:

  • Asian low-cost startups
  • Cloud providers bypassing traditional integrators
  • New entrants purely AI without hardware

5. Weak signals & potential disruptions

🔻 Weak signal 1: Low-cost modular micro-robots

  • Origin: Asian startup → prototypes at < €300
  • Potential impact: ultra-accessible automation for SMEs → market disruption
  • Probability: average
  • Recommendation: monitor the first deployments in Europe

🔻 Weak signal 2: 100% autonomous factories (Northern Europe)

  • Several pilot sites without physically present operators
  • Impact: disruption of the social model + 3x productivity
  • Probability: high
  • Recommendation: follow the associated economic models

🔻 Weak signal 3: Industrial AI rental (AI-as-a-Service)

  • Already adopted in the United States
  • Impact: Shift in investment models
  • Probability: high
  • Recommendation: anticipate CAPEX/OPEX impacts

6. Strategic Recommendations

Immediate action (0–3 months)

  • Prioritize an IIoT diagnostic to measure the impact of new standards
  • Mapping current cybersecurity vulnerabilities
  • Identify rapid AI projects with ROI < 12 months

Short term (3–12 months)

  • Deploy an AI pilot on a priority route
  • Strengthening partnerships with innovative suppliers
  • Prepare a cybersecurity compliance roadmap for 2026

Medium term (12–36 months)

  • Integrate AI as a standard in all CAPEX projects
  • Explore the opportunity of advanced automation or modular robotics
  • Set up an internal industrial transformation monitoring unit

7. Conclusion

Three critical issues emerge for any large industrial company:

  1. Automate more to maintain competitiveness.
  2. Securing infrastructure to avoid future sanctions.
  3. Anticipating technological disruptions (micro-robots, autonomous AI).

These elements should guide strategic decisions for 2025–2026.
Strategic monitoring - urban mobility sector

Summary excerpt presented by SRPInnov

1. Context & Objectives

The urban mobility sector is undergoing rapid transformation due to:

  • the electrification of fleets,
  • the rise of shared mobility services,
  • the arrival of technology players,
  • environmental regulatory pressure,
  • new consumer habits.

Objective of strategic intelligence:
 Providing a large company operating in urban transport with a clear analysis of the developments that will impact its activities between 2025 and 2030.

2. Executive Summary

Our strategic monitoring identifies 18 major trends , including 6 critical trends and 4 weak signals to monitor as a priority .
The ecosystem is evolving towards:

  • an over-technologization of services ,
  • a strengthening of environmental regulations ,
  • an increase in partnerships between manufacturers and tech players ,
  • an acceleration of intelligent multimodality .

Established players risk a loss of competitiveness if they do not adapt quickly to new innovation dynamics.

3. Major trends identified (excerpt)

Trend 1 — Total electrification of fleets (impact: very strong / time horizon: 2025–2028)

European regulations are accelerating the transition. Established manufacturers are investing heavily in low-cost or modular solutions.

Trend 2 — Mobility as a Service (MaaS)

Users prefer integrated services (bus + bike + car sharing + scooters).
Municipalities support this model through public platforms.

Trend 3 — Partial Automation

Level 3/4 autonomous vehicles are appearing in pilot city centers.
High potential impact but gradual adoption.

Trend 4 — Increasing environmental pressure

Increased number of low emission zones (LEZs) and stricter CO₂ standards.

Trend 5 — Hybridization of transport and data

Operators are also becoming collectors of massive amounts of data, creating a new source of value.

4. Weak signals detected (excerpt)

Here are the 4 weak signals identified as priorities:

Weak signal 1 — Arrival of private autonomous micro-shuttles

Startups are testing 100% autonomous micro-shuttles for inter-neighborhood zones.
Currently marginal, but could transform local transport services by 2030.

Low signal 2 — Interchangeable modular batteries for fleets

Innovation driven by Asian startups.
Significantly reduces operating costs: could revolutionize the business model of electric fleets.

Weak signal 3 — AI-driven dynamic insurance

Insurers are testing contracts that are adjusted in real time according to usage.
Potential impact on fleet operating costs.

Weak Signal 4 — Application of Generative AI to Network Optimization

Automated optimization of planning, schedules and routes.
Still in the laboratory phase, but with strong potential for effectiveness.

5. Mapping of stakeholders (excerpt)

The actors were classified into 4 categories:

1. Historical Actors

  • Car manufacturers
  • Public and private transport operators
    → Threatened if they do not quickly digitize their services.

2. New technological entrants

  • AI startups mobility
  • Suppliers of innovative batteries
  • MaaS Platforms
    → Strong disruptive capacity.

3. Potential Partners

  • Infrastructure managers
  • Cloud developers
  • Data analytics companies
    → Opportunities for synergies.

4. Aggressive foreign actors

  • Asian companies specializing in low-cost electric fleets
    → Risk of pressure on prices.

6. Risks & Opportunities for the Client Company

Risks:

  • loss of market share to independent private solutions
  • dependence on battery suppliers
  • increasing regulatory pressure
  • decrease in margin on traditional services

Opportunities :

  • development of proprietary MaaS solutions
  • creation of a premium data service
  • strategic partnerships with AI startups
  • cost optimization through intelligent automation

7. Recommendations (excerpt)

Short term (0–12 months)

  • actively monitor the interchangeable battery ecosystem
  • launch an AI optimization pilot for network planning
  • strengthen partnerships with local mobility startups

Medium term (1–3 years)

  • develop an integrated MaaS platform
  • investing in a partially autonomous fleet
  • create an internal “monitoring + innovation” unit

Long term (3–5 years)

  • reposition the company as a “transport + data” operator
  • integrate autonomous micro-shuttles into future planning
  • modernize the central digital infrastructure
Technology Watch - Generative AI

Summary excerpt presented by SRPInnov

1. Context & Objectives

The client, a major player in the digital services sector, wants to anticipate:

  • the rapid evolution of generative AI,
  • its impact on its business,
  • priority technologies to integrate,
  • the risks related to competition and regulation.

Objective of technology monitoring:
 Identify, classify and evaluate AI technologies with a potential impact on the client's business by 2025–2030 .

2. Executive Summary

Our analysis identifies:

  • 32 emerging AI technologies ,
  • 9 highly mature technologies ,
  • 6 potential disruptive innovations ,
  • 3 critical regulatory risks ,
  • and 8 major strategic opportunities .

The ecosystem is evolving very rapidly under pressure:

  • large language models (LLMs),
  • advanced automation,
  • hardware innovations (specialized AI chips),
  • and vertical sector-specific applications.

Competition is intensifying: traditional players are now being challenged by highly specialized startups.

3. Key technologies identified (excerpt)

Technology 1 — Advanced Language Models (Next-Generation LLM)

Impact: Very high | Maturity: High | Horizon: Immediate (2024–2026)
 Applications: support automation, code generation, structured content creation, document analysis.

Technology 2 — Multimodal AI

Text + image + video + audio combinations.
Impact: High | Maturity: Medium | Horizon: 2025–2028
 Potential for training, simulation, and recording analysis.

Technology 3 — Autonomous AI Agents

AI capable of performing complex tasks without human supervision.
Impact: very strong | Potential disruption
 Applications: workflow management, business automation, process optimization.

Technology 4 — Edge AI

AI integrated into local devices (sensors, terminals).
Impact: Medium | Maturity: High
 Interest in industry, logistics, security.

Technology 5 — Automated Code Generation

Tools capable of producing, testing, and debugging code.
Impact: very high | Timeframe: 2025–2027
 Reduces time-to-market and increases productivity.

Technology 6 — Advanced Audio-Video AI

Transcription, emotional understanding, video generation.
Impact: High | Associated regulatory risks
 Potential in training, communication, HR.

4. Potential disruptive innovations

Disruption 1 — Next-generation specialized AI computers (ASICs)

Enables 10x more powerful models at a reduced cost.
Impact: profound transformation of the market.

Disruption 2 — AI agents capable of multi-system interactions

Example: an agent who can manage CRM + ERP + customer support.
Impact: very strong — service revolution.

Disruption 3 — Generative AI dedicated to highly specialized sectors

Healthcare, legal, finance: each industry will have its own proprietary models.
Impact: strong differentiation for early adopter companies.

5. Identified risks (excerpt)

Risk 1 — Intensification of competition

Vertically focused AI startups are gaining ground and threatening established players.

Risk 2 — Dependence on cloud/AI providers

Risk of technological lock-in and increased costs.

Risk 3 — Rapid regulatory evolution (Europe & USA)

New constraints surrounding data, ethical AI and security.

6. Major strategic opportunities

✔ Automated optimization of internal processes (30 to 60% potential gains)
✔ Creation of premium AI services for end customers
✔ Integration of AI agents into value chains
✔ Leveraging multimodal AI to automate complex operations
✔ Development of proprietary AI platforms
✔ Opportunities for strategic partnerships with high-growth startups
✔ Cost reduction through embedded AI
✔ Leveraging untapped internal data

7. Recommendations (excerpt)

Short term (0–12 months)

  • Deploy an internal LLM pilot to automate document tasks
  • Evaluate emerging agent-based solutions
  • Mapping the internal data available for AI

Medium term (1–3 years)

  • Create a hybrid AI architecture (cloud + edge)
  • Develop an integrated proprietary AI platform
  • Establish a strategic partnership with a specialized AI startup

Long term (3–5 years)

  • Establish an internal “AI & Innovation” division
  • Developing AI agents connected to critical systems
  • Prepare for compliance with future European standards
Competitive intelligence - analysis of a changing market

Summary excerpt presented by SRPInnov

1. Context & Objectives

The client, a major player in the B2B software solutions sector, is facing:

  • an intensification of competition,
  • the arrival of innovative new entrants,
  • pressure on prices,
  • a rapid evolution of customer expectations (AI, automation, security),
  • aggressive acquisitions by foreign companies.

Objective of competitive intelligence:
 Map industry specialists, analyze the strengths and weaknesses of key competitors, identify disruptive new entrants, and assess risks/opportunities for the coming years.

2. Executive Summary

The monitoring team identifies:

  • 12 direct competitors
  • 7 indirect competitors
  • 5 new entrants with strong disruptive potential
  • 3 priority threats
  • 4 key strategic opportunities

The ecosystem is dominated by:

  • the massive integration of AI into software solutions,
  • the rise of low-cost SaaS models,
  • foreign actors with significant financial power
  • Sectoral specialization as a new differentiating factor.

The customer can differentiate themselves through vertical innovation and added value, rather than through price.

3. Competitive Mapping (excerpt)

The mapping classifies the competitors into 4 groups:

Group A — Traditional leaders (major historical players)

Positioning: robust, wide range, strong reputation.
Weaknesses: slow innovation, high costs.
Examples: major international publishers.

Group B — Technology Innovators

Positioning: integrated AI solutions, automation, rapid adoption.
Weaknesses: lack of maturity, few advanced integrations.
Examples: AI SaaS startups.

Group C — Aggressive new entrants (foreigners)

Positioning: very low price, modernized interface, massive R&D teams.
Weaknesses: poor customer support, security still unclear.
Impact: very high threat.

Group D — Specialized (vertical) solutions

Positioning: targeted by profession or sector (health, finance, energy).
Weaknesses: poor ability to expand horizontally.
Opportunity: collaboration or integration.

4. SWOT Analysis (excerpt)

Data focused on the 4 main competitors

Competitors analyzed:

  • Actor A: historical leader
  • Actor B: AI startup
  • Actor C: low-cost foreign actor
  • Actor D: verticalized solution

Competitors — SWOT Analysis

Actor A — Historical Leader

Strengths : market confidence, stability, extensive network
Weaknesses : slow innovation, high prices
Opportunities : AI, process automation
Threats : Innovative new entrants

Actor B — AI Startup

Strengths : rapid innovation, proprietary AI
Weaknesses : lack of advanced features
Opportunities : B2B partnerships
Threats : rapid acquisition/disappearance

Actor C — Low-cost foreign publisher

Strengths : unbeatable prices, strong growth
Weaknesses : questionable security, weak support
Opportunities : SME market
Threats : European regulations

Actor D — Specialized Solution

Strengths : Unique industry expertise
Weaknesses : limited range
Opportunities : co-development
Threats : lack of funding

5. Competitive Trends to Monitor (excerpt)

Trend 1 — Automation and Advanced AI

Competitors are integrating AI into all layers of the product.
Impact: high.

Trend 2 — Aggressive freemium models

Some new entrants adopt strategies aimed at quickly gaining market share.
Impact: very high.

Trend 3 — Market consolidation (acquisitions)

Small, specialized startups are being bought out by large corporations.
Impact: medium but increasing.

Trend 4 — Sectoral Specialization

Customers are looking for solutions tailored to their business.
Impact: strong.

6. Risks & Opportunities for the Client

Risks

  • loss of market share to native AI solutions
  • price war with low-cost players
  • progressive obsolescence of systems
  • customers attracted by vertical solutions

Opportunities

  • to position itself as a premium augmented AI solution
  • develop a high value vertical offering
  • forging alliances with AI startups
  • strengthening security and compliance (major differentiation)

7. Recommendations (excerpt)

Short term (0–12 months)

  • Overhauling the value strategy: deprioritizing the price war
  • Quickly integrate AI modules into 2 key functionalities
  • Identify 2 AI startups for potential partnerships or acquisition

Medium term (1–3 years)

  • Develop a range of vertically integrated solutions (3 priority sectors)
  • Enhance product performance through automation
  • Improving security and compliance (major USP)

Long term (3–5 years)

  • Create a complete ecosystem around the solution (APIs, partners)
  • Expanding the market internationally through a premium positioning
  • Prepare targeted strategic acquisitions
Regulatory monitoring - Proactive analysis of legal developments impacting a European technology player

Summary excerpt presented by SRPInnov

1. Context & Objectives

The client is a European technology company offering SaaS solutions for SMEs and large accounts.
It operates in an increasingly strict regulatory environment, particularly in the following areas:

  • Data protection (GDPR, DSA)
  • Artificial intelligence
  • Cybersecurity (NIS2)
  • Financial compliance (if banking clients)
  • Data hosting and sovereignty

The objective is to anticipate obligations, avoid legal risks, reduce compliance costs and guide product strategy.

2. Executive Summary

Regulatory monitoring identifies:

  • 3 priority European regulations with a direct impact
  • 2 major risks of non-compliance
  • 4 strategic opportunities enabling the client to improve its positioning
  • One essential product adaptation over the next 18 months

The regulatory landscape is becoming a competitive advantage for companies that are able to anticipate rather than react.

3. Priority Regulations to Monitor

1. AI Act (European Law on Artificial Intelligence)

Status: gradual entry between 2025–2026.
Impact: high for all software incorporating AI.

Key points:

  • AI classification by risk levels
  • Mandatory transparency regarding usage and data
  • Obligation to assess risks and documentation
  • Very high fines for non-compliance

Impact on the customer:
 ➡ Need to add an internal AI compliance module for future developments.

2. NIS2 (Cybersecurity)

Status: in application from 2024–2025 in most European countries.
Impact: significant for technology companies providing critical services.

Obligations:

  • Strengthening cybersecurity procedures
  • Regular audits
  • Obligation to notify incidents
  • Stricter responsibilities for leaders

Impact on the customer:
 ➡ Obligation to review internal data protection processes and invest in strengthened cyber protocols.

3. DSA & DMA (Digital Services Act / Digital Markets Act)

Status: applied progressively according to the company's status.
Impact: moderate but growing.

Key points:

  • Transparency in data management
  • Restrictions on certain advertising practices
  • Enhanced moderation requirements depending on the service

Impact on the customer:
 ➡ Adaptation of user data processing and clarification in the Terms and Conditions.

4. Identified Risks

❗ Risk 1 — Non-compliance with the AI ​​Act

Significant financial penalties and risk of loss of customer confidence.

❗ Risk 2 — Lack of NIS2 preparation

In the event of a cyber incident, there is high legal liability, reputational and financial impact.

❗ Risk 3 — Stricter GDPR controls

The authorities are intensifying their checks.

❗ Risk 4 — Hindrance to B2B sales

Large companies require full compliance before signing.

5. Strategic Opportunities

1. Positioning “Software Compliant Ready”

Becoming a publisher that anticipates all standards: a huge competitive advantage.

2. Development of AI Act-compliant functionalities

Creating audit or traceability tools can become a premium selling point.

3. Enhanced security = increased credibility

NIS2 compliance reassures the market.

4. Easier access to major accounts

Public companies and small regulated businesses prefer suppliers who are compliant from the outset.

6. Strategic Action Plan

Short term (0–12 months)

  • GDPR documentation update
  • Rapid cybersecurity audit (NIS2)
  • Creation of a registry for all features using AI
  • Internal training on the new obligations

Medium term (1–3 years)

  • Implementation of an automated compliance dashboard
  • Strengthening cyber processes (firewall, monitoring, incident response)
  • Setting up a compliance/data governance team

Long term (3–5 years)

  • Development of a “compliance by design” roadmap
  • International standardization for non-EU markets
  • Integrating compliance as a differentiating marketing element

Examples of deliverables provided
Mapping emerging players in a technology market

Please contact us to receive the maps.*
Summary of key trends transforming an industry

Economic sectors are evolving under the combined influence of technological disruptions, regulatory constraints, and changing customer expectations. Organizations must now navigate an environment where innovation is continuous, cycles are becoming shorter, and competition is more aggressive.
Here are the 6 key structural trends observed in the majority of industries.

1. Artificial Intelligence & Advanced Automation

AI is becoming the central driver of transformation in almost every sector.
Its major impacts:

  • Business process automation (RPA, AI agents)
  • Real-time value chain optimization
  • Customizing services at scale
  • Reduction of operating costs
  • Assisted and predictive decision-making

As a result, companies that are able to integrate AI quickly gain a considerable competitive advantage.

2. Data Explosion & Data-Driven Decisions

Companies are moving from a descriptive logic to a predictive and prescriptive logic.

  • Exponential growth in data volumes
  • Advanced analytics tools (Big Data, Data Lake, BI)
  • Data governance and quality have become critical
  • Business models based on the value of information

As a result, data is no longer a support tool, but a strategic asset in its own right.

3. Regulatory Pressure & Compliance as a Competitive Advantage

Standards are multiplying: AI Act, GDPR, NIS2, environmental taxonomies, sector-specific standards…

  • Increase in corporate bonds
  • Strengthening cybersecurity and transparency
  • Direct impact on products, services and processes
  • Increased demand from large accounts for compliant solutions

As a result, proactive organizations are transforming compliance into a premium differentiator.

4. Transformation of Business Models (SaaS, Platforms, Subscriptions)

Most markets are evolving towards the following models:

  • subscription-based
  • platform-oriented
  • incorporating marketplaces
  • offering continuous value rather than a one-time sale

As a result, companies must rethink their revenue chain and develop high value-added services.

5. New Customer Behaviors & Hyper-Demanding

Customers (both B2B and B2C) expect:

  • speed, simplicity, automation
  • near-instant customization
  • security and transparency
  • seamless experience across all channels
  • very short response times thanks to AI

As a result, customer experience becomes a critical element of competitive positioning.

6. Acceleration of Technological Innovations

All industries are impacted by:

  • Generative AI
  • Edge computing
  • Advanced Cybersecurity
  • Next-generation robotics
  • Augmented and assisted reality
  • Smart materials
  • Green energy & sustainable technologies

As a result, innovation cycles are accelerating, requiring a continuous capacity for adaptation.

Conclusion — A Structural and Sustainable Transformation

The companies that will succeed in this new landscape will be those that:

  • anticipate disruptions before their competitors
  • invest in AI and data
  • adopt a proactive stance towards regulations
  • modernize their business model
  • place the customer experience at the heart
  • maintain constant vigilance to capture weak signals

Strategic intelligence thus becomes an essential lever for transforming these trends into a sustainable competitive advantage.
Analysis of weak signals regarding the evolution of a business model

A company wishes to anticipate possible transformations of its business model in the face of technological, competitive, societal or regulatory developments.
The goal of SRPInnov is to detect weak signals that allow for the identification of:

  • emerging threats,
  • strategic opportunities,
  • changes in customer behavior,
  • innovations that could disrupt the value chain.

1. Transformation of the value chain through “invisible” digitalization

New players are introducing automated or AI-based microservices, but gradually, often in niche markets.
Low but growing risk of disintermediation
 Potential for a freemium model + premium services

2. Emergence of “low-touch” and “self-service” solutions

Customers are adopting simple, fast, often free or very inexpensive tools, allowing them to obtain a service that was previously outsourced themselves.
Indication of a shift in value towards assistance, expertise and personalisation .
Possibility of a hybrid automated + human model .

3. Rise of alternative players from neighboring markets

Companies that were not historically competitors are starting to occupy the space.
Risk of increasing indirect competition
 Opportunity for new partnerships or diversification.

4. Transformation of customer expectations

Customers show a preference for:

  • solutions accessible 24/7
  • more visual deliverables
  • more transparency
  • less long-term commitment
    Indicator of a transition towards a modular model .

5. Regulations in preparation that may change costs

Draft laws, directives, voluntary standards…
This can create:

  • new operating costs,
  • or, conversely, a regulated captive market .

6. Weak signals related to AI

Specialized AIs are beginning to automate fragments of the business model.
Future cost reduction
But there is a risk of standardization → value shifts towards strategy , insight, advanced analysis, and personalization.

Potential consequences on the economic model

1. Towards a “Premium + Automated” model

Human value is shifting towards:

  • the analysis
  • the strategy
  • personalization
    The operational aspects can be partially automated.

2. Towards greater income variability

Customers are looking for:

  • flexible subscriptions
  • microservices
  • modular services

3. Need to introduce new tools / platforms

To remain competitive, a business model can evolve towards:

  • interactive dashboards
  • dynamic deliverables
  • from the previous day continuously
  • services that are more “consulting” than “execution”

Recommended strategic directions

1. Strengthen differentiation through added value

Insight, analysis, expertise → things that AI cannot replace.

2. Develop hybrid offerings

Low-cost entry-level solution + personalized premium service.

3. Explore technological partnerships

Co-development, API integration, automation.

4. Create a portfolio of modular offerings

Adapted to emerging segments and new customer behaviors.

Conclusion

Analysis of weak signals shows that business models generally evolve before the industry is aware of it .
The identified signals allow us to anticipate:

  • the risks of disruption,
  • emerging opportunities,
  • changes in value within the string,
  • future customer expectations.

SRPInnov supports companies in the early detection of weak signals, scenario planning and the transformation of business models.
Sectoral regulatory monitoring report

REPORT — Regulatory Monitoring of Cosmetics (France)

Reference period: updated November 2025
Level: Operational / Strategic
Author: SRPInnov

1) Executive Summary

  • The main framework applicable to cosmetics in France is Regulation (EC) No. 1223/2009 (Cosmetics Regulation): product safety obligations, Product Information File (PIF), and notification via the CPNP for placing on the market in the EU.
  • Since January 1, 2024 , ANSES has been responsible in France for monitoring and expert assessments related to cosmetic products (transfer from ANSM to ANSES). This changes the local contacts and alert procedures.
  • Recent high-impact changes: extension of labelling obligations for fragrance allergens (expanded list with compliance deadlines for manufacturers) and restrictions on microplastics → formulation & labelling impacts.
  • Priority recommendation: full compliance audit (PIF, labelling, substances), adjustment of product sheets & packaging, and implementation of a regulatory compliance dashboard (KPI + alerting).

2) Essential regulatory framework (key sources)

  • EU Cosmetics Regulation (EC) No 1223/2009 — general product safety requirements, PIF, substance prohibitions/restrictions.
  • Cosmetic Products Notification Portal (CPNP) — obligation to notify any cosmetic product placed on the EU market; no additional national notification required if product notified to the CPNP.
  • ANSES — vigilance & expertise (France) : since 2024 ANSES manages vigilance and expertise in cosmetics; French contact points and procedures to be updated.
  • Restricted lists / prohibited substances / REACH & ECHA : substances prohibited or restricted via annexes and by REACH/ECHA texts — to be cross-referenced with your formulation. (ECHA — lists & restrictions).
  • Recent developments/to follow: extension of labelling of fragrance allergens (official changes published / compliance schedules); microplastic restrictions (impact on formulas containing glitter, microbeads).

3) Priority texts/files to follow (FR/EU)

(Ranked in order of importance for a manufacturer/distributor in France)

  1. Regulation (EC) No 1223/2009 (Cosmetics Regulation) — general requirements, PIF, responsibility of the "responsible person".
  2. CPNP (Cosmetic Products Notification Portal) — mandatory prior notification for placing on the EU market.
  3. ANSES - new national missions & procedures (cosmetic vigilance) — follow guides and contacts.
  4. Update on fragrance allergens / Annexes (EU updates Oct 2023) — expansion of allergen list and labelling obligations / timetables (check deadlines for compliance).
  5. Microplastics restrictions/initiatives on micropollutants — labeling requirements and/or restrictions on the use of intentionally produced plastics. Impact formulations & claims.
  6. REACH / ECHA restrictions and lists — some cosmetic substances are affected by REACH chemical restrictions or authorizations (to be monitored).
  7. Green claims / anti-greenwashing rules (EN/EU) — framework for environmental claims; caution on claims “organic, natural, recyclable”.

4) Impact analysis (operational / product / commercial)

A. Product & Formulation

  • Allergen labeling: review INCI lists, update labeling/FTS, plan reformulations if critical allergens.
  • Restricted substances / REACH: withdrawal or substitution of restricted molecules → impact on formulations and sourcing costs.
  • Microplastics / plastic elements: removal or replacement of glitter/microbeads → redesign of packaging and formulations.

B. Process & Compliance

  • PIF & documentation: complete and centralize Product Information File for each reference (safety, toxicology data, evidence of claims).
  • CPNP notification: process all references via CPNP before placing them on the EU market; automate status tracking.

C. Commercial / go-to-market

  • Claims & marketing: reframing environmental and health claims (risk of greenwashing); preparing evidence & certifications.
  • Access to major accounts: preparing compliance files (PIF, tests, attestations) for tenders / B2B markets.

5) Operational checklist (to be executed immediately)

Initial audit (Day 0–Day 30)

  • [ ] List all product references & PIF statuses.
  • [ ] Verify that each product is notified on the CPNP .
  • [ ] Update INCI sheets and labeling (new allergens).
  • [ ] Launch audit substances vs REACH/ECHA lists (prohibited / restricted).
  • [ ] Check ANSES contact person & vigilance reporting procedures.

Actions (Days 30–90)

  • [ ] Reform formulas if necessary (substitution of prohibited ingredients)
  • [ ] Set up compliance & responsible register (Data/Legal/Product)
  • [ ] Update Terms of Use / Commercial Conditions if necessary (compliance clauses)
  • [ ] Prepare files for major accounts (test reports, SDS, certificates)

Continuous monitoring (monthly/quarterly)

  • [ ] Regulatory bulletin (alert) — incidents, consultations, EU votes
  • [ ] CPNP Updates & PIF Status
  • [ ] REACH / ECHA / ANSES / DGCCRF / European Commission monitoring

6) Recommended roadmap (12–36 months)

Short term (0–6 months)

  • Full audit of PIF + CPNP + INCI
  • Update packaging & allergen labeling
  • Compliance & alerting dashboard

Medium term (6–18 months)

  • REACH/microplastics risk ingredient substitution program
  • Automation of notifications and documentation (CPNP sync)
  • HR / Quality / R&D Training

Long term (18–36 months)

  • Credible certification & labeling (e.g., ISO/sector-specific)
  • Integrating compliance as a selling point ("compliant by design")
  • Active monitoring via ECHA/ANSES database subscription alerts

7) SRPInnov KPIs & Deliverables (proposal)

  • % of complete PIF references
  • % of products notified by CPNP
  • Number of critical regulatory alerts per quarter
  • Average adaptation time (days) to a regulatory obligation
  • Estimated compliance cost (CAPEX/OPEX) per major change

Recommended deliverables: monthly bulletin, flash sheet (alert), quarterly impact report, compliance checklist, standardized PIF sheet template.

8) Message templates & alert processes (templates)

  • Flash alert (ex.) — subject: Regulatory alert — [text] — High impact (include summary, impacts, recommendation, responsible party, deadline) — send to DG, Legal, R&D.
  • Internal PIF update note — checklist of missing parts + responsible party.

9) Sources & references (selection — to be consulted regularly)

  • Regulation (EC) No 1223/2009 (Cosmetic Products Regulation).
  • Cosmetic Products Notification Portal (CPNP) — European Commission.
  • ANSES — new cosmetic vigilance missions (since January 1, 2024).
  • Extension of the list of fragrance allergens — update & compliance schedules.
  • Microplastics restriction / EU initiatives (impacts on formulation & labelling).
  • ECHA / REACH — lists of prohibited substances / restrictions.
  • DGCCRF — practical guides and obligations France (market controls).
In-depth study on an emerging technology and its implications

Study — AI for skincare personalization

(Application: photo/biometric diagnostics, product prescription, formulation optimization, on-site device)

1) Executive Summary

AI applied to skin diagnostics and cosmetic personalization is transforming the brand-customer relationship: it enables automated recommendations, customized formulations, and more efficient e-commerce journeys. The personalized cosmetics market is already significant (approximately USD 25 billion in 2024, with strong growth expected), and major players (L'Oréal) and startups (Haut.AI, Skin Analytics, Atolla/Function of Beauty) are making the technology accessible. Regulatory frameworks (AI Act, product safety, GDPR) require a combination of technical rigor, clinical evidence, and data governance to avoid legal and reputational risks.

2) What exactly is it?

Definition: systems combining computer vision (image/selfie analysis), machine learning (deep learning), user data (questionnaires, product history, sensors), and sometimes biological measurements (home tests, DNA data/biomarkers) for:

  • detect skin parameters (complexion, texture, wrinkles, hydration, spots, porosity),
  • segment skin profiles,
  • recommend routines, ingredients, or personalized formulations.

Typical components: image acquisition pipeline + preprocessing, dermatological assessment model, recommendation module (business rules + ML), A/B testing engine to optimize recommendations.

3) State of the art & representative actors

  • Large groups & R&D: L'Oréal (ModiFace, SkinConsult, Beauty Genius, Unveil Perso) invests in AI diagnostics and on-site devices.
  • B2B Startups/Technology: Haut.AI offers clinical skin analysis solutions for brands & R&D; Skin Analytics develops AI for clinical triage (e.g., skin/melanoma), demonstrating the level of medical maturity possible.
  • DTC / personalisation: Atolla (acquired by Function of Beauty) and others offer home testing + personalized formulas.
  • Marketplace / retail features: retail players like Nykaa are integrating AI “skin scan” into their apps to recommend products.

4) Technological maturity (where are we at?)

  • Computer vision for visible parameters (wrinkles, spots, redness): high maturity — commercial products deployed.
  • Deeper measurements (actual hydration, collagen level, dermal structure): medium/emerging maturity — requires sensors or devices (e.g., Unveil Perso). citeturn0search15
  • Medical diagnosis (pathology, cancer): possible but regulated (AI as Medical Device) — maturity varies (e.g., Skin Analytics shows a certified medical level).

5) Concrete use cases for a cosmetics brand (prioritized)

A. Acquisition & e-commerce conversion

  • Selfie → instant diagnosis → personalized product recommendation → increased conversion rate and reduced churn. (Immediate use case, short-term ROI).

B. Formulation & R&D

  • Aggregation of customer profiles → identification of formulation insights → creation of micro-batches or personalized formulas (in-store device / home kit). (Medium-term use case, CAPEX/OPEX).

C. Premium service (subscription / refill)

  • Subscription offers based on regular diagnostics and formulation readjustment → increased ARPU. (Use case scale).

D. In-store diagnostics & device dispensing

  • Dispensing on-site customized formulations (e.g., Unveil Perso) for a premium experience; high entry cost but differentiating.

E. R&D/Clinical validation

  • Development of clinically validated algorithms for product claims, useful for B2B/key accounts.

6) Business & commercial impacts (for a cosmetics company in France)

Opportunities

  • Improved e-commerce conversion, reduced returns, increased average order value, and customer loyalty through subscriptions.
  • New value streams (diagnostics as a service) and differentiation in a saturated market.
  • Proprietary consumer data → R&D guided by real evidence.

Threats / risks

  • Regulatory: AI providing diagnostic capabilities similar to healthcare functions may trigger stricter obligations (AI Act, qualification as a medical device). The AI/health framework is evolving rapidly.
  • Data & privacy: collection of images and biometric data → GDPR issues, explicit consent, data minimization.
  • Quality & responsibility: diagnostic errors → reputational risks, complaints.
  • Supplier dependency (cloud/ML): lock-in, variable costs.

7) Regulations & Compliance — Key Points (France / EU)

  • AI Act (EU) : Certain AI applications considered "high-risk" (healthcare/diagnostics) may be subject to strict requirements. The timeline has recently changed; verify operational implications before large-scale commercial deployment.
  • Cosmetics Regulation & claims : any recommendation involving action on the product must remain compatible with authorized claims (no unpermitted curative promises).
  • GDPR : facial images = potential biometric data → explicit consent and legal basis required, impact assessments (DPIA) frequently necessary.
  • Medical Device : if the tool claims to diagnose pathologies (e.g., suspicious lesions), it can be considered a medical device (MDR/CE marking) — do not cross this threshold without medical validation.

8) Ethical & reputational risks

  • Algorithmic bias (performance varies according to phototypes, age, ethnicities) → discrimination risk.
  • Excessive over-personalization → intrusive perception.
  • Unproven claims → risk of greenwashing / fraudulent marketing.

9) Strategic recommendations (operational roadmap)

Phase 0 — Preparation (0–1 month)

  • Regulatory gap analysis : AI Act, GDPR, Cosmetics Regulation, device rules.
  • DPIA for all image/biometric processing.
  • Data policy & consent (clear opt-in, retention period).

Phase 1 — Proof of Concept (3–4 months)

  • Objective: to validate data collection, model, UX and conversion KPIs.
  • Scope: a feature (e.g. selfie analysis for 3 parameters: texture, wrinkles, spots).
  • Data: Diverse dataset (phototype, age, lighting), B2B partnership (High AI or ModiFace integration). citeturn0search14turn0search2
  • Deliverables: web/mobile prototype, performance report (accuracy per segment), bias mitigation plan.

Phase 2 — Pilot (6–9 months)

  • Deployment in 1 market (France) or on 2 channels (website + app); A/B test recommendations vs standard catalogue.
  • Limited clinical measurements (if health claims): validation vs. dermatologist on a sample.
  • Metrics: conversion, average order value, NPS, product returns, subscription rate.

Phase 3 — Industrialization & Scale (12–24 months)

  • API & R&D integration (data → insights formulation).
  • Devices / in-store if positive ROI.
  • Governance & compliance : external audits, certifications (if necessary), automation of consents.

10) Recommended KPIs

  • Diagnostic accuracy by segment (phototype, age)
  • Post-diagnosis conversion rate (%)
  • Average basket size adjusted (%)
  • Subscription/retention rate at 90 days
  • % of cases requiring human review
  • Number of compliance incidents per quarter

11) Detailed pilot plan (operational template)

Duration: 3–6 months
Indicative budget: depends on integration (€50–150k for POC + SaaS integration + data labeling)
Steps:

  1. Constitution dataset (2–4k diverse images, consents)
  2. Selection of technology partner (Haut.AI, ModiFace) or internal development
  3. UX development (photo capture guide, consent modal)
  4. Training & validation (train/val/test)
  5. Limited deployment & metrics collection
  6. Analysis of results & next steps (scale / pause / R&D)

POC deliverables: functional demo, metrics report, DPIA, bias mitigation plan, business proposal.

12) Quick Operational Checklist

  • [ ] Perform DPIA (images / biometrics).
  • [ ] Check regulatory qualification (medical device?)
  • [ ] Obtain explicit consent & log consent.
  • [ ] Assemble diverse dataset (phototypes, sexes, ages, lighting).
  • [ ] Implement a human review (escalation) process.
  • [ ] Document the origin of training data (licenses).
  • [ ] Prepare evidence for marketing claims (clinical tests if needed).
  • [ ] Cloud contract & data localization (sovereignty?)

13) Business & Positioning Recommendations

  • Premium positioning: offering diagnosis + "personalized follow-up" subscription: justifies price and loyalty.
  • B2B offer: white-label package for retailers and pharmacies (API integration).
  • R&D data play: monetizing aggregated non-identifying insights for formulation & innovation.

14) Key sources & recommended readings

(the 5 most promising sources cited in the text)

  • Customization market size / report 2025.
  • L'Oréal — SkinConsult / ModiFace / Beauty Genius (use cases & industrialization).
  • Haut.AI — AI solution for brands and R&D (B2B).
  • Skin Analytics — examples of AI classified as a medical device (dermatological triage).
  • Regulatory news AI Act / timeline & tensions (Reuters — deadline 2027).

15) Conclusion (excerpt)

AI applied to personalized skincare is ready for commercial adoption in visual diagnostics and product recommendation, offering rapid marketing ROI and customer loyalty. However, success requires data governance , targeted clinical validation of claims, and strict compliance with the European regulatory framework—a strategic opportunity that must be built with rigor.