Accelerating Speed to Value through Agentic Systems and Intelligent Automation in Life Sciences - with Robert Wenier of AstraZeneca

Wenier traces the architectural evolution from service-oriented architectures (SOA) through microservices to emerging agentic architectures. Unlike previous paradigms focused on stepwise technical execution, agentic systems aim to deliver end-to-end business outcomes directly, fundamentally changing how infrastructure supports business goals.

• •Service-oriented architectures performed logical steps requiring multiple systems to reach final answers
• •Microservices and serverless architectures improved speed and efficiency through event-driven, fragmented processes
• •Agentic architectures focus on achieving strategic end states directly rather than step-by-step technical execution
• •Infrastructure design is shifting from technical outcomes to direct business outcome delivery

The fundamental shift from technology as a business enabler to a business discriminator represents a major paradigm change. Previously, technology enabled stepwise processes; now it directly drives competitive advantage by delivering business outcomes without intermediate technical steps.

• •Traditional paradigm: business requests outcomes, technologists build platforms, users perform multi-step processes
• •New paradigm: technology delivers business outcomes directly, bypassing stepwise execution
• •Speed of achieving business outcomes becomes the competitive differentiator, not technical sophistication
• •Business leaders now expect direct value from technology rather than technical capabilities that enable value

AI is enabling value creation to move from corporate and business unit levels down to teams and individuals. The shift transforms workers from 'cogs in a machine' to 'the machine itself,' with each unit expected to deliver direct business value rather than technical outputs.

• •Value statements moving from corporate/business unit level to team and individual level
• •Individuals and teams now expected to deliver business outcomes, not just technical deliverables
• •Access to AI tools and understanding how to use them effectively are critical enablers
• •Data quality and cleansing at scale required to energize decades of company data for AI use
• •Co-pilots and general knowledge models reduce dependency on proprietary data infrastructure

AI agents can eliminate the manual logistics of moving data between systems, allowing professionals to make strategic decisions directly at the data source. This removes entire categories of work focused on data formatting, transfer, and preparation.

• •Traditional workflows involve moving data from source to Excel to other systems - pure logistics work
• •AI agents enable direct analysis and decision-making at the data source without manual transfers
• •Workers can be upskilled from tactical data handlers to strategic decision-makers
• •Example: analyzing bank account data for revenue composition without Excel intermediation
• •Focus shifts from making tactical decisions to making strategic margin and business decisions

Profitability has shifted from efficiency-based models to speed-to-market and speed-to-value models. Technology's primary purpose is now accelerating business outcomes, with companies needing to outpace competitors in decision-making and value realization rather than just reducing costs.

• •Historical profitability based on efficiency; modern profitability based on speed to market/value
• •Technology exists primarily to increase speed, not just enable manual processes to work
• •Getting tasks done to value quickly is the driving competitive force
• •Relegating logistics to AI agents frees humans for faster value-creating decisions
• •Speed advantage allows companies to outrun competitors in delivering outcomes

In pharma, high selling, general, and administrative (SG&A) expenses reduce research and development investment. Streamlining processes through AI to reduce SG&A burden enables faster speed to value and reinvestment in R&D, creating a competitive cycle of acceleration.

• •SG&A burden in pharma reduces available R&D investment, creating strategic impediment
• •Need to streamline laborious processes to achieve speed to value faster
• •Reduced SG&A enables reinvestment in additional streamlined opportunities
• •Competitive pressures intensifying as industrialization of AI capabilities approaches
• •Rapid evolution (9-month cycles) creating 'whiplash' in technology landscape

Many large SaaS providers view AI as a tool to add to existing platforms rather than recognizing it as a fundamental paradigm shift. This misreading threatens their business models as agentic architectures may eliminate the need for traditional large-scale SaaS platforms.

• •SaaS providers treating AI as a tool rather than recognizing paradigm shift
• •Traditional approach: add AI features to existing platforms to maintain profitability
• •Agentic architecture makes the tool less important than the outcome achieved
• •Agents can interpret data, answer questions, and make modifications without large SaaS platforms
• •Traditional HR, payment, and data management systems may become obsolete with intelligent agents

True industrialization means using AI to achieve complete business objectives from conception through commercialization, not just technical ends. This requires abstracting away traditional infrastructure concerns and focusing on business decision-making enabled by agents.

• •Industrialization means achieving end-to-end business objectives, not just technical tasks
• •Example: delivering entire product from conception, design, manufacturing to commercialization
• •Application servers, databases, storage volumes abstracted away and managed by agents
• •Users don't need to know underlying database technology or chipset specifications
• •Focus on whether agents deliver needed information for decision-making, not technical details

Winners in the AI age will be companies that don't feel constrained by traditional vendor relationships and licensing models. The shift requires adaptability to use whatever tools achieve business outcomes fastest, moving beyond strategic partnerships and license-based IT management.

• •Companies held down by traditional vendors will lose competitive advantage
• •Business expects outcomes achieved quickly regardless of vendor relationships
• •The 'nobody ever got fired for buying IBM' mentality is obsolete
• •Orchestrator agents should interface with whatever systems needed without license agreements
• •Adaptability and speed to outcome matter more than strategic vendor partnerships
• •IT management through licenses and partnerships is being replaced by outcome-driven tool selection