Life sciences

Life sciences R&D is entering a new phase. Biotech, focused on creating products based on incremental trial and error by manipulating biological and chemical systems in wet labs, is becoming TechBio—an entirely different R&D organism. TechBio integrates high performance computational technologies (like AI and data science) to accelerate research, clinical discoveries, and product development while protecting profitability, improving patient care, and elevating brand experience.

As we move from “biology first” to “biology plus computing first” (the TechBio mindset), the lab footprint will shrink for first movers and fast followers digitizing discovery. These life sciences leaders benefit from unified data and human-AI collaboration that outpaces competitors tied to siloed or physical-first, purely empirical “try everything” R&D models. The ability to process large volumes of data (e.g., omics, structural biology, chemical libraries) and the ability to simulate many molecular interactions means exploring more options faster and at a lower cost than before. 

In 2026, AI will move from a buzzword to the balance sheet. Now, the priority isn’t experimentation, nor is it productivity gains. It’s all about impact, value creation, and network effects. Life sciences leaders that scale human-AI collaboration and AI automation have a mandate to embed AI into everyday decision-making. That mandate includes establishing sound governance and measuring results that matter so every colleague can contribute at the top of their license. Organizations that focus on productivity without balancing differentiation and disruption will be outpaced.

Still, the performance gap is real:

• 92% of life sciences leaders plan to increase AI investment, but only 33% can quantify its value. Most are still talking about productivity and querying the corporate brain.
• 62% still run more than half of their core applications on legacy platforms, limiting interoperability and the ability to scale.
• 56% cite workforce skill gaps as the top barrier to AI ROI. 

Step Zero is data modernization: AI maturity starts with a single source of truth, and it’s measured when you move AI from a cost center to a value center. In 2026, value breakthroughs will come with three key considerations: 

1. Governance must be intentional and applied rigorously. 

   Regulatory scrutiny of algorithmic decision-making is intensifying. Responsible-use frameworks must evolve from discussion to daily discipline: Ethical review boards, bias testing, and model-drift monitoring aren’t optional. When life sciences leaders create the systems and operations to govern AI, it becomes a resourced, ROI-conscious organizational strategy that builds trust.

    

2. AI adoption and skill gaps need to be addressed. 

   Workforce readiness factors into AI maturity, in part because models are changing faster than people can. Continuous retraining—quarterly refreshers focused on prompting, interpretation, and validation—is essential for AI fluency, talent engagement, and safeguarding AI ROI.

    

3. Shift from accepting productivity and efficiency gains to a bigger aim. 

   Organizations that focus only on productivity without balancing differentiation and disruption will be outpaced. Turn ambition into revolution where AI reshapes organizations entirely, delivering groundbreaking innovations and establishing distinctiveness.

Modernization is more than upgrading technology. It’s a business strategy with immediate and long-tail advantages. Start by creating a center of excellence (CoE) focused on human-AI collaboration. Use this foundation to raise workflow standards and apply domain-specific strategies to eliminate vampiric tasks (e.g., regulatory paperwork and clinical data cleanup) replacing them with AI-driven solutions. This isn’t a tech refresh for optimization. It’s reinvention that powers ROI through growth.

2026 is a year for pivoting from pilot to performance at scale. 

Slalom gives life sciences organizations the power and intelligence to scale AI initiatives that deliver measurable business and patient impact. With a focus on repeatable, insight-rich models, these efforts drive faster, smarter decision-making across the molecule-to-market journey, from R&D acceleration to go-to-market precision.

In 2026, adaptability becomes an operating advantage. Life sciences organizations that prioritize it (or build on it), are poised to pull ahead. 

In addition to the burdens of operating in siloed organizational charts, 62% of life sciences organizations are still anchored to legacy systems and outdated architecture. The remaining 38% are addressing outdated architecture, but most still haven't fully solved it. As the workforce changes, leaders are losing the institutional knowledge needed to maintain old investments, just as opportunity costs are adding up.  

From standardizing trial conduct with good clinical practice (GCP) compliance workflows to automating adverse event reporting with human supervision, modernization unlocks the agility to align tools, talent, and processes with evolving market demands. Then, organizations can make the jump from static strategies to adaptive models built for speed, insight, and resilience.

What mindsets make adaptability possible going forward? 

Get modernization done. Replacing legacy systems with unified, data-driven architectures allows for faster, data-informed decisions. Open, continuous feedback loops create a shared-service infrastructure, so leaders can bring regulatory, quality, marketing, and distribution functions back in-house. It’s the difference between reactive quality checks to predictive quality assurance or the delta between making bets versus informed decisions on which drug candidates to shelve or advance based on real-time trial performance and market analytics.

Shift from fixed to fluid planning. Five-year roadmaps are giving way to two- to three-year adaptive cycles, often with six-month re-evaluation cycles baked in. Organizational focus isn’t only driven by budgets. It’s also driven by strategy, and execution must adjust as quickly as market conditions and therapy economics evolve. With connected architectures, supply chain leaders can make informed decisions about where to build redundancy, where automation or digital-twin modeling drives resilience, and which manufacturing or distribution processes should be repatriated based on tariff and risk exposure.

Evolve headcount, don’t evaporate it. Automation and human-AI collaboration will shift talent, so teams spend less time moving data and more time using their expertise. For example, clinical data workflows can be redesigned, moving from vampiric manual data entry and cleaning to AI-driven data harmonization with training and tools that automate reconciliation across electronic data capture (EDC), laboratory information management systems (LIMS), and clinical trial management systems (CTMS).

Without the right approach, old habits can spill into new technology. Top performing leaders are shifting performance metrics to reward learning, iteration, and intelligent risk-taking with incentives. They’re recognizing experimentation, process improvements, and effective use of AI agents, not just output volume. This encourages life sciences teams to embrace change instead of calcifying old habits in new tools (or working around change).

The shift from rigid to resilient starts with retiring legacy systems and retooling for intelligent adaptability. 

Slalom helps life sciences leaders trade technical debt for strategic agility. The result? Faster pivots, smarter execution, and resilient growth that keeps pace with science, policy, and provider/patient needs.

In 2025, technology raced ahead of talent readiness. In 2026, life sciences leaders won’t win by buying more bots. The wins come from teaching people to manage the bots. That experience and judgement, together with observability and transparency, define fluency.

Inconsistent adoption, limited ROI, and AI fatigue are curtailing fluency, though. This year, we’ll see a correction as human-AI collaboration takes on new urgency. Expect life sciences executives and front-line leaders to model curiosity, coach for effective use, and build both trust and engagement with AI. Open the door to reskilling late-career roles, upskilling mid-career roles, and developing new career paths for early-career roles, so innovative and efficiency advancements are coupled with accountability.

Expect responsible technology use, including AI use, to move from policy binders to active practice: 

1. Clinical trial design and operations: Clinicians and operations managers can use AI-generated insights to select better trial sites, recruit patients more effectively, and adjust protocols in real time. 
2. Sourcing and supply chain management: Analysts and logistics planners can use digital twins to simulate manufacturing plants, shipping routes, and sourcing networks to test “what if” scenarios, then make proactive decisions to manage disruptions.
3. Regulatory and compliance: Specialists can guide and review AI-generated filings, saving time while ensuring accuracy and compliance. 
4. Marketing and patient engagement: Strategists and marketers can design campaigns that personalize outreach to patients and providers, using AI to deliver the right message at the right time for more sales and deeper trust.
5. Talent development: HR and learning and development leaders can use AI to identify general workforce and team–specific skill gaps, then design, launch, and track success in adaptive training programs. 

Use this year to move from AI adoption to competency, confidence, and fluency. 

At Slalom, we know that the most adaptive organizations operate where people and technology bring out the best in each other. As automation and AI change the nature of roles, not their necessity, growth will only happen when organizations pair technology maturity with human capability and judgement.

After years of selling through intermediaries, pharmaceutical and biotech companies are moving closer to the patient. In 2026, the direct-to-consumer (DTC) model will expand and accelerate. This shift will be driven by pricing pressures, new digital channels, and the need for data transparency. It’s also driven by consumer expectations and generational change.

With pricing compression spurred by the Inflation Reduction Act (IRA) and the unfolding impact of the MFN executive order in the US, manufacturers are finding that going direct to patients (or caregivers) accomplishes three objectives: It preserves margin, it deepens market insights, and it creates opportunities to develop lasting trust. 

With direct access to patient and provider data, there are new privacy and compliance risks to manage. For modernized organizations with adaptable, automated, and well-governed technology, control over data protection, privacy, consent, compliance, and continuity can reshape risks as potential rewards. For instance, Pfizer’s Pfizer4All™, Lilly’s LilyDirect®, and Novo Nordisk’s NovoCare® are testing DTC channels that let consumers bypass insurance and buy brand medications at discounted (cash-pay) prices.

Consumers are responding well to this approach. Just as people engage with personalized recommendations in their Netflix queue or Starbucks app, patients are beginning to expect wellness and healthcare to offer the same level of personalization. For patients and their healthcare advocates, experience is everything. For life sciences brands, that experience is also equity.

DTC is a growth engine, giving life sciences brands the power to deepen patient relationships and capture higher margins.

At Slalom, we help life sciences organizations modernize commercial models, unify data, and build compliant, patient-first platforms that drive profitability without sacrificing patient trust. Our solutions deepen that trust and help organizations deliver personalized experiences at scale.

In 2026, tariff volatility, inflation, and logistical bottlenecks aren't going away. They’ll just keep evolving. That's why predictive modeling and precision at scale are taking on heightened focus and why digital twins are redefining control.

Companies can now simulate disruptions, from tariff hikes to typhoons, before they hit. These tools offer foresight and full-chain agility across organizations. “Lights-out” manufacturing and smart-factory operations, where AI orchestrates production with minimal human intervention, are growing their footprints. In clinical settings, digital twins now model biological responses to testing protocols, drugs, and other interventions. The same technology capably validates Software-as-a-Medical-Device (SaMD), accelerating testing and approvals. Digital twins are also being used to model scenarios that test how to source ingredients, how medicine gets made, and how meds are delivered (or recalled) to patients.

Meanwhile, personalization is rewriting the logistics playbook. It’s shifting the model from bulk buys to small-batch, just-in-time manufacturing and cold chain precision for cell and gene therapy (CGT) and other individualized treatments, especially in oncology, cardiology, and psychiatry. But leaders strapped to legacy systems can’t stretch their organization into these new competitive edges without breaking. 

Supply chain strategy is more than an advantage: It’s now a vital growth lever. 

Slalom helps the life sciences industry turn supply chains into living ecosystems, where cost and control stay balanced. With modern, intelligent, connected, and patient-centric supply chains, firms can sense and simulate disruption upstream and down. Then, machine learning and self-correction can happen in real time. Leaders can operate smarter, faster, and more resiliently in the face of emerging risks and market opportunities.