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ROCHE HOLDING AG — EMPLOYEE MEMO

"Scientific Revolution: Your Role in Reshaping Pharmaceutical Innovation"

From: Human Resources & People Development To: All Roche Employees Date: June 2030

Dear Roche Team,

We are in the midst of the most significant transformation in pharmaceutical R&D in a generation. This memo provides context on what is happening, why your contribution matters, and what to expect as we navigate this transition.

THE MOMENT

Pharmaceutical drug development is experiencing what aviation experienced when jets replaced propellers: a fundamental paradigm shift in how we approach our work.

For 70 years, drug discovery was art + craft + luck. Brilliant scientists made intuitive leaps about disease mechanisms. Teams synthesized thousands of compounds. A small percentage showed promise in cells. An even smaller percentage succeeded in animals. An infinitesimal fraction became approved drugs.

This process was expensive ($3B+ per approved drug), slow (12+ years), and failure-prone (90% of candidates failed in clinical trials).

Now, artificial intelligence is automating the intuition and synthesizing the molecules. Not perfectly. Not without risks. But dramatically better than before.

Roche is leading this transformation. You are part of the team rebuilding pharmaceutical innovation for the AI era.

WHAT'S CHANGED IN 18 MONTHS

Before (2025-2027): Roche was a traditional pharma company with patent cliff exposure and R&D productivity challenges. Pipeline looked weak. Growth looked weak.

Now (2030): Roche has 23 programs in Phase 1-2 (vs. 11-13 historical average). Pipeline advancement is accelerating. Revenue growth is accelerating.

How? - AI target identification: compressed from 3-5 years to 6-18 months - Lead optimization: reduced from 4-7 years to 18-36 months - Preclinical-to-IND success rate: improved from 25% to 52%

These improvements are structural, not one-time. They represent genuine transformation in how we discover drugs.

WHAT THIS MEANS FOR DIFFERENT ROLES

For Computational Scientists & Data Scientists

You are experiencing extraordinary demand and opportunity. Roche has hired 2,000+ computational scientists in 2028-2029. More are coming.

What's happening: - AI-driven target identification is now the standard approach for new programs - Machine learning models are being trained on Roche's proprietary datasets (patient outcomes from diagnostics) - Molecular structure prediction and drug optimization is being automated - Your work is directly accelerating drug discovery

Career implications: - Compensation is being adjusted to compete with tech companies (increases of 10-12% for competitive positioning) - Career paths are being clarified—you can advance as individual contributor or management - Opportunities for leadership are expanding as new programs launch - Cross-functional collaboration is increasing (working with medicinal chemists, biologists, clinicians)

What we need from you: 1. Collaborate intensively with traditional pharma scientists. Your machine learning models are predictions; their domain expertise is validation. 2. Document your methodologies. As we scale, other teams need to understand and replicate your approaches. 3. Focus on interpretability. "The AI predicts this drug will work" is not sufficient. Explain why. 4. Stay humble about AI limitations. AI improves odds but doesn't eliminate pharmaceutical risk.

For Medicinal Chemists & Synthetic Chemists

Your role is evolving. Historically, you synthesized thousands of compounds iteratively hoping to find leads. Now, AI predicts which compounds to synthesize.

What's changing: - Lead optimization is faster but requires more precise interpretation of computational predictions - You're working with AI scientists to understand model outputs - Synthetic chemistry is still critical—not every AI prediction is easily synthesized - Your expertise in drug-like properties and manufacturability is more valuable than ever

What we need from you: 1. Partner with computational scientists to improve model accuracy (your chemical intuition is training data) 2. Translate AI predictions into synthesizable candidates 3. Build feedback loops: when AI predictions fail in chemistry, help us understand why 4. Maintain quality standards—AI can suggest structures; you ensure they're actually good drugs

For Biologists & Disease Experts

AI can predict that a target is important. Only domain experts understand why.

What's happening: - Your expertise in disease mechanisms is more valuable as AI expands the number of potential targets - You're working with computational scientists to validate AI-identified targets - Your career advancement is tied to translating computational predictions into biological understanding - Cross-therapeutic area collaboration is expanding

What we need from you: 1. Deep domain expertise: understand not just your therapeutic area but related areas 2. Skepticism: when AI identifies a novel target, your job is to determine if it's real 3. Biomarker development: as we move toward precision medicine, biomarker expertise is critical 4. Patient engagement: understand how patients and clinicians will use our new therapies

For Clinical Scientists & Trial Specialists

AI has improved our clinical predictions. But clinical trials remain the ultimate test of drug efficacy and safety.

What's changing: - Phase 2 trials are seeing better efficacy signals due to AI-optimized compounds - You're designing trials for AI-predicted biomarkers, not broad patient populations - Patient stratification is more precise, reducing trial failure rates - Your expertise in trial design and execution is more valuable than ever

What we need from you: 1. Embrace precision medicine: design trials for defined patient populations 2. Accelerate timelines: AI has compressed preclinical timelines; help us accelerate clinical development appropriately 3. Manage regulatory relationships: FDA and EMA need reassurance about AI-designed drugs 4. Track real-world outcomes: post-marketing data about AI-predicted drugs is research gold

For Manufacturing & Supply Chain

Faster drug development means faster manufacturing scale-up. This creates challenges.

What's happening: - New drugs coming online more frequently (target: 2-3 launches annually by 2033) - Manufacturing processes must be scaled faster - Supply chains must be more resilient - Your role in ensuring manufacturability is critical

What we need from you: 1. Early manufacturing involvement: work with chemists to design syntheses that scale efficiently 2. Supply chain resilience: identify component vulnerabilities early 3. Quality consistency: as we accelerate, maintain our quality standards 4. Cost reduction: manufacturing efficiency is increasingly important for pricing strategy

For Diagnostics Teams

You are moving from "supporting" role to central strategic position.

What's happening: - Precision diagnostics are being integrated with new therapeutic launches - Your patient outcome data is training AI models - Companion diagnostics are becoming higher-margin business - You're partnering with pharmaceutical teams in new ways

What we need from you: 1. Data quality and security: the datasets you maintain are increasingly valuable; protect them rigorously 2. Patient privacy: as we use diagnostic data to train AI models, privacy safeguards are paramount 3. Regulatory expertise: diagnostic-therapeutic combinations require integrated regulatory strategies 4. Clinical integration: work closely with clinical scientists to develop biomarker-driven trials

ORGANIZATIONAL CHANGES COMING

Reporting Structure Evolution

Traditional organizational structure was therapeutic area-based (Oncology, Virology, Autoimmune). This is evolving toward hybrid: - Therapeutic area directors maintain accountability for portfolio performance - Computational science directors lead cross-therapeutic AI initiatives - Diagnostic integration leads connect precision diagnostics to therapeutic programs - Matrix organization: you will likely have two reporting lines (therapeutic area + functional capability)

This creates complexity but enables better knowledge sharing and resource allocation.

Facility Expansion

We are expanding operations: - San Francisco: new 200+ person computational science hub - Cambridge, MA: expansion of biopharmaceuticals research - Basel/Veldhoven: continued strengthening of core R&D - Tokyo, Singapore: drug discovery partnerships and regional presence

Some of you may be offered opportunities to transfer or lead operations in new locations. Consider these seriously—international experience is increasingly valuable.

Talent Integration Challenges

We hired 2,000+ new scientists in 2028-2029. Many come from tech companies, academia, or biotech with different cultural expectations.

Expect: - Different communication styles between tech-trained scientists and pharma-trained scientists - Disagreement about work processes (pharma is more regulated; tech is more iterative) - Collaboration friction as teams adapt to each other - Gradual cultural convergence as we learn from each other

What we need from you: Be patient with new colleagues. Help them understand pharma's regulatory requirements. Learn from them about rapid iteration and creative problem-solving.

COMPENSATION & BENEFITS UPDATE (EFFECTIVE JULY 2030)

Salary Adjustments

Computational Scientists/Data Scientists: 10-12% increase Medicinal/Synthetic Chemists: 5-7% increase Biologists/Disease Experts: 4-6% increase Clinical/Regulatory: 3-5% increase Manufacturing/Supply Chain: 4-6% increase All other roles: 2-3% cost of living adjustment

Rationale: We're responding to market compensation for high-demand technical roles while maintaining internal equity.

Equity & Long-Term Incentives

Benefits Expansion

THE HARDER TRUTH

Growth at this pace is exhilarating. It's also stressful. Here's what honesty requires we acknowledge:

  1. Uncertainty about the future: We're building something new. Some approaches will fail. Some brilliant ideas won't translate to clinical benefit. You may feel pressure and frustration.

  2. Organizational change fatigue: We're changing how we work while maintaining clinical research standards. This is exhausting.

  3. Career ambiguity: As we reorganize, some career paths become less clear. Senior roles may be filled by external hires. You may not see advancement as quickly as expected.

  4. Competitive pressure: Tech companies and biotech firms are actively recruiting Roche scientists. You may see colleagues leave.

  5. Failure risk: Some of our AI-predicted drugs will fail in clinical trials. When this happens, it reflects pharmaceutical reality, not failure of the teams involved.

What we offer in exchange: - Job security: we have 5+ years of fully funded pipeline - Compensation reflecting market conditions and your contribution - Opportunity to build something historically important - International collaboration with leading scientists globally - The chance to help millions of patients through faster drug discovery

CAREER DEVELOPMENT INITIATIVES

Internal Mobility Program

If you've been at Roche for 3+ years, you're eligible for internal transfers across therapeutic areas. This enables career development without leaving the company.

Leadership Development

Roche is investing CHF 50M in leadership development programs. If you aspire to management roles, we have structured programs: - Manager Development Program (12 months, for managers with 1-2 years experience) - Director Development Program (18 months, for directors with 2-5 years experience) - Executive Development Program (24 months, for senior leaders)

AI & Data Science Certification

We're providing funding for employees to develop computational skills: - Data science fundamentals (online course, CHF 2,000) - Machine learning specialization (Stanford online, CHF 5,000) - Advanced programming (Python, R, databases—CHF 3,000)

If you're interested, speak with your manager about eligibility.

Mentorship & Sponsorship

Senior leaders are being asked to mentor high-potential employees. If you're offered a mentor, treat this seriously. This accelerates your career development.

WHAT WE'RE ASKING OF YOU

  1. Embrace scientific uncertainty: We're doing something new. Some predictions will be wrong. Learn from failures.

  2. Collaborate across disciplines: AI scientists, chemists, biologists, and clinicians must work together. Be open to perspectives different from yours.

  3. Maintain quality: As we accelerate, quality becomes increasingly important. Don't let speed compromise scientific integrity.

  4. Stay current: The science is evolving rapidly. Invest in continuing education. Read broadly.

  5. Help integrate new colleagues: Many new hires come from different industries. Help them understand pharma culture.

  6. Think long-term: Don't optimize for next quarter or next year. Pharmaceutical development is 10-year endeavor. Make decisions that create value over decades.

WHERE WE'RE GOING

By 2035, if we execute well:

This transformation depends on you. Thank you for being part of it.

Questions? Your HR business partner is your first point of contact for questions about career development, compensation, benefits, or organizational changes.

Career conversations: Your manager should be scheduling quarterly career development discussions. If not, request it.

Mental health & wellness: The pace of change can be stressful. Our EAP is free and confidential. Use it.

With gratitude and excitement,

Roche People & Culture Team June 2030