Article
Published
1 June 2026
The AI landscape: traditional, generative, and agentic AI
Traditional artificial intelligence (AI) refers to machine learning and statistical models that operate on structured data to automate, predict, and optimise decision-making processes. These systems are designed to identify patterns and improve outcomes in areas such as forecasting, risk management, and operational efficiency.
Generative AI (GenAI) extends these capabilities by using large language models (LLMs) and other generative architectures that are built to handle general inputs and outputs. GenAI can analyse and create content from unstructured data such as text, images, or audio, enabling more interactive and context-aware use of information. An assistant (often used interchangeably with agent) is an LLM configured to perform a specific task through prompt engineering and data access.
Agentic AI systems equip LLMs with a level of autonomy and access to a predefined toolbox allowing models to perform complex tasks such as writing code, drafting slides, querying databases, and accessing systems.
It’s one thing having a good model, but it’s another thing getting it to work in a real-life drug discovery environment and operate at a super-fast timescale. Like weaving your way through the woods on a bicycle, you need to be agile and quite fast at making decisions.
Mishal Patel, Senior Vice President of AI & Digital Innovation, Novo Nordisk
The state of AI in life sciences
Life sciences encompass the full arc from R&D-intensive drug discovery through commercial launch, manufacturing, and post-market operations – each stage carrying distinct regulatory nuances, data profiles, and AI maturity levels. The industry serves a global patient population through a value chain spanning research, quality assurance, tech transfer, manufacturing, supply chain, and batch disposition with commercial functions driving market access and revenue.
AI is already mission-critical in parts of the life sciences value chain. In R&D, investments, such as those made in Isomorphic Labs and Anthropic, signal that AI-driven drug discovery is moving from experimental to foundational. In operations, AI remains predominantly efficiency-focused, being used to optimise yield, predict maintenance needs, and streamline quality processes. On the commercial side, use cases such as competitor intelligence, content generation, and field-force coaching are gaining traction, though adoption varies dramatically between front-runners and laggards.
The industry’s AI strategy is predominantly top-down and leadership-driven, and is notably ‘builder-happy’, investing heavily in custom in-house AI applications alongside vendor partnerships rather than relying on off-the-shelf solutions. However, a large maturity gap persists: while frontrunners have established dedicated AI departments led by Chief AI Officers and multi-year roadmaps backed by extensive investment, many organisations remain in exploratory or pre-adoption phases. The near future will be defined by the tension between AI's transformative potential and the industry's characteristically low error tolerance and traditional predisposition towards human execution.
Life sciences are investing heavily in AI, but adoption remains polarised and the gap between frontrunners and laggards is widening
Current state of AI in the life sciences industry…
AI strategy across life sciences is predominantly top-down and leadership-driven, with organisations favouring structured planning over rapid experimentation. Frontrunner pharma companies have established dedicated AI organisations under Chief AI Officers and are investing heavily in custom in-house applications. Medical writing, specifically Draft 0 document generation, has emerged as one of the more advanced areas for generative AI, and use cases such as competitor intelligence, predictive maintenance, and content management are moving from pilot to production.
Yet overall maturity remains uneven. AI capabilities are mostly centralised in Centres of Excellence, and many smaller firms have not yet adopted even basic copilot tools. AI tends to live in isolated pockets rather than being embedded across end-to-end value chains, and adoption depends heavily on the digital maturity of the organisation.
… but what is hindering adoption?
The primary barriers to AI adoption in life sciences are structural and cultural, not technological. Ultra-low error tolerance creates an innovator's dilemma: AI may deliver a tenfold efficiency improvement, but at only 90% accuracy. Validation of AI solutions remains a black box, and GxP compliance requirements add cost and complexity to every deployment, while the EU AI Act introduces over 100 pages of new regulation that governance frameworks are racing to absorb.
But the regulatory burden is only part of the story. AI initiatives are often IT-driven without direct business involvement, and organisations consistently overestimate the technology while underestimating the behavioural changes needed.
Digital transformation
AI across the value chain of digital transformation
AI is accelerating how life sciences organisations manage regulatory, safety, and quality processes, creating efficiency gains for early adopters and laying the foundation for autonomous workflows in the medium term.
Highlighted AI use cases in digital transformation
Several advanced AI use cases in digital transformation are moving from pilot to production and can deliver significant returns on investment from early deployment.
Reference cases
Digital transformation: Implement reference cases
These cases illustrate how AI can accelerate compliance, reduce manual effort, and embed safe, scalable day-to-day adoption across regulated life sciences organisations.
Streamlining PQS and quality processes with AI for a leading pharmaceutical company
Quality management // Compliance GenAI
A client in a highly regulated industry struggled to keep pace with frequent regulatory updates across jurisdictions. Manual consolidation and assessment made compliance slow, error-prone, and difficult to scale. We designed an AI-driven compliance workflow that automatically screens regulatory updates, identifies relevant changes, and supports faster, more consistent regulatory adaptation with human oversight.
Impact
- 50% faster regulatory review processes
- 50%+ reduction in early compliance labour costs
- Improved accuracy and consistency, enabling a more proactive and risk-resilient compliance approach
Building everyday AI habits in a global life sciences company
Change management // Generative AI
A global life sciences company sought to accelerate responsible generative AI adoption across its organisation. Over six months, Implement designed and delivered a role-based learning programme spanning four modules across 50 instructor-led sessions, combined with microlearning, a refreshed AI learning hub, and structured feedback loops. The programme targeted practical use cases, governance, and output quality to build confidence and safe, routine AI usage in daily work.
Impact
- Trained more than 1,600 employees in six months, with classes nearly fully booked within a week of launch
- Daily unique users of the company GPT solution doubled from approximately 350 to 750, with two hours saved per week on average after training
- Established consistent, compliant usage patterns and positioned the company to continue scaling AI value
Operations
AI across the value chain of operations
AI is reshaping life sciences manufacturing and supply chain operations, with early adopters achieving measurable gains in yield, quality, and planning efficiency across the value chain.
Highlighted AI use cases in operations
Several advanced AI use cases in operations are moving from pilot to production and can deliver significant returns on investment from early deployment.
Reference cases
Operations: Implement reference cases
These cases illustrate how agentic AI can streamline complex pharma operations, improve compliance and consistency, and free up expert capacity through scalable, real-time support.
AI-assisted pharma deviation reporting: faster and more consistent
Agentic AI // Compliance GenAI
A client with complex regulatory demands sought to simplify and accelerate its deviations management process amid rapid growth. Implement supported the client by delivering a production-grade AI module where orchestrated AI agents generate section-by-section drafts grounded in historical deviations, approved templates, and global process descriptions.
Each draft is automatically validated against governing SOPs, site-specific rules, and data requirements, reducing rework and minimising iterations between authors, QA, and process owners.
Impact
- Reduced deviation handling time by 10–15%, equivalent to 400–900 working days per site per year
- Strengthened quality, consistency, and auditability through standardised, SOP-aligned content across sites
- Freed SME capacity for higher-value work without compromising compliance or traceability
State-of-the-art agentic AI solution with potential to simplify tech transfer
Product operations // Agentic AI
A solution paradigm intended for those seeking to move beyond static documents and sequential, interdependent processes. Projects deploy an autonomous, agentic AI solution. Implement supports clients by deploying specialised AI agents that collaborate directly to resolve complex process steps in real time, addressing central bottlenecks. The decentralised architecture enables low-latency, targeted responses to SMEs and establishes a scalable foundation for future add-ons.
Impact
- Low-latency resolution of complex process steps
- Delivers SME-relevant responses by leveraging core data
- Establishes a scalable foundation that is accessible to operations 24/7
Commercial
AI across the value chain of commercial
AI is already transforming life sciences commercial operations, from launch preparation through content management to field-force effectiveness, creating competitive advantages for early adopters.
Highlighted AI use cases in commercial
Several advanced AI use cases in commercial life sciences are moving from pilot to production and can deliver significant returns on investment from early deployment.
Reference cases
Commercial: Implement reference cases
These cases illustrate how GenAI can drive sustainable commercial growth, improve customer engagement, and strengthen pharmaceutical visibility and narratives across emerging AI channels.
Leveraging generative AI for sustainable growth in a global pharmaceutical company
Content & insight generation // Field force excellence
A global pharmaceutical company was facing a significant scale-up to grow their business. The company prepared to launch into new therapy areas, while also expanding the product portfolio within current therapy areas. This also entailed reaching new target groups, more HCPs, and more patients.
Accordingly, the company needed to ensure sustainable growth. GenAI was perceived as a key lever for achieving sustainable growth, but the client needed a clear direction and roadmap on where and how to leverage GenAI across the commercial organisation.
Impact
- 40% reduction in time spent on LMR processes
- 30% efficiency gains in market research and competitor intelligence
- 20% efficiency gains in field force HCP targeting and HCP engagement
Generative search engine optimisation to increase visibility across AI search engines
Digital marketing // AI strategy
A global pharmaceutical company identified that its products and narratives were not consistently represented in the authoritative sources that AI search engines rely on, resulting in lower visibility versus competitors. As patients and healthcare professionals increasingly use AI-powered search to explore treatment options, Implement supported the client in actively shaping and strengthening its narrative across these sources to ensure AI-generated responses reflect accurate, current, and strategically aligned key messages.
Impact
- Expanded to 11 tracks across functions, engaging selected media outlets and authority sources to disseminate aligned key messages
- Early tracking indicates a shift in AI search responses towards the company's desired narrative
- Ambition is to scale across markets, increasing visibility and favourability while ensuring scientifically accurate information
In our experience, life sciences must make explicit strategic choices to unlock the value of AI
There are no universally 'right' answers – but there is a cost to not making the choices explicit. Realising sustained AI impact is driven by a small number of deliberate strategic trade-offs.
Future outlook
The future is impossible to predict. But what is certain is that AI can no longer be treated as something to explore ‘when the time is right’– the time is now.
Across life sciences, organisations are moving in a common direction. Not because the destination is known, but because standing still is no longer an option. The early leaders are not those with perfect roadmaps, but those who take AI seriously, invest early, and remain curious and experimental.
In the near term, progress starts with pragmatic use cases such as copilot adoption, raw materials forecasting, and computer vision for quality control that serve as entry points to build governance, validation frameworks, and regulatory readiness. The harder work lies inside the organisation: understanding where AI can fundamentally reshape value creation across the value chain, and where experimentation, not optimisation, is required.
At Implement, we see early leaders investing not just in technology, but in AI factories, data foundations, validated environments, and most importantly, organisational learning. Lasting impact will depend on organisations' ability to bridge the widening gap between frontrunners and laggards, and to disrupt themselves from the inside out.
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