Digital Transformation: Democratizing Innovation

Novartis's Digital Transformation Journey

Initial Investments and Challenges

• Novartis embarked on a significant digital transformation journey over the last decade, marked by substantial investments aimed at modernizing its infrastructure and capabilities:

  • Migration to the Cloud: The company transitioned its technology infrastructure to cloud-based solutions to enhance scalability, flexibility, and efficiency.

  • Data Platform Investments: Novartis invested in advanced data platforms and integration tools to consolidate and harmonize data from various sources.

  • AI Talent Acquisition: The firm recruited AI specialists and data scientists to spearhead the development and implementation of artificial intelligence solutions.

• Despite these strategic investments, the adoption and integration of new data insights faced resistance from business managers across critical functions:

  • Lack of Data Embrace: Business managers in sales, supply chain, HR, finance, and marketing were slow to embrace the new information and analytics tools.

  • Limited Data Application: Many managers did not fully consider how data-driven insights could optimize their daily work processes and decision-making.

• The disconnect between data science initiatives and business operations posed significant challenges:

  • Visibility Gaps: Data scientists lacked sufficient visibility into the specific needs and operational contexts of various business units.

  • Integration Hurdles: The absence of clear alignment hindered the effective integration of data science outputs into daily business operations.

• The initial phase of digital transformation yielded mixed results:

  • Occasional Successes: Some areas, such as R&D, experienced successes with the implementation of digital solutions.

  • Pilot Project Failures: Many pilot projects failed to deliver tangible business value or achieve sustainable adoption.

Shift Towards Success

• A turning point in Novartis's digital transformation came with successful pilot projects focused on R&D and marketing personalization:

  • Demonstrated Business Value: These projects showcased the potential of AI and data analytics to drive meaningful improvements in specific areas.

  • Executive Attention: The demonstrated success garnered the attention and support of creative business executives.

• With executive sponsorship, Novartis accelerated the deployment of AI solutions across the enterprise:

  • AI Champions: Executives actively championed the adoption of AI to improve efficiency and performance.

  • Cross-functional Collaboration: Novartis paired data scientists with business employees to identify opportunities for process optimization and performance enhancement.

  • Training Initiatives: The company invested in training frontline business employees to leverage data for innovation and informed decision-making.

  • Agile Methodologies: Teams adopted agile methods to enhance flexibility, collaboration, and responsiveness in project execution.

• The transformation gained momentum, leading to significant innovation initiatives across various domains:

  • Digitally Enabled Sales: Implementation of digital tools and analytics to enhance sales processes and forecasting accuracy.

  • Order and Replenishment Systems: Redesign of order and replenishment systems to improve efficiency and responsiveness for healthcare customers.

  • Prescription Fulfillment: Revamping of prescription-fulfillment systems and processes to optimize customer experience and operational performance.

Impact of the Pandemic

• The COVID-19 pandemic underscored the critical importance of digital transformation and AI capabilities:

  • Supply Chain Management: Business teams collaborated with data scientists to effectively manage supply-chain disruptions.

  • Shortage Prediction: Advanced analytics were deployed to predict shortages of critical supplies and ensure timely availability.

  • Product Mix Optimization: The company enabled rapid changes to product mix and pricing policies based on real-time market dynamics.

  • At-risk Patient Identification: Analytics were developed to identify at-risk patients who were avoiding doctor visits, facilitating targeted interventions.

• The value of AI became evident to managers across the organization, driving further adoption and integration of digital solutions.

Democratizing Access to Data and Technology

• Prior to the widespread adoption of AI, technology investments were primarily centered around packaged enterprise applications managed by the IT department and external consultants.

• Under the leadership of Bertrand Bodson, Novartis embraced a strategy of democratizing access to data and technology:

  • Data Science Capabilities: Development of new data science capabilities to empower employees across the organization.

  • Democratized Access: Broadening access to data and technology beyond traditional technology silos to foster innovation at all levels.

  • Employee Training: Comprehensive training programs to enable employees to identify and capitalize on opportunities for data and technology to improve their work.

• The Novartis yearly AI summit in 2021, attended by thousands of employees, highlighted the company's commitment to fostering a data-driven culture.

The Need for Widespread Digital Solutions

• According to IDC’s Worldwide IT Industry 2020 Predictions report, enterprises would need to create approximately 500 million new digital solutions by 2023 to remain competitive.

• Achieving this scale of digital transformation requires broad participation and collaboration:

  • Beyond Technologists: The creation of digital solutions cannot be limited to small groups of technologists and data scientists.

  • Diverse Employee Groups: Larger, diverse groups of employees are needed to rethink business operations and drive digital innovation.

Elements of Tech Intensity

• Successful digital transformation hinges on the synergy of three key elements:

  • Capabilities: Developing digital and data skills in employees outside traditional technology functions; investing in process agility and a culture that encourages experimentation.

  • Technology: Investing in the right technologies, especially in the elements of an AI stack (data platform technology, data engineering, machine-learning algorithms, and algorithm-deployment technology), ensuring that technology deployed is easy to use and accessible to nontechnical employees.

  • Architecture: Investing in organizational and technical architecture that supports the sharing, integration, and normalization of data across isolated silos.

The Importance of Employee-Driven Transformation

• Companies often underestimate the critical role of employees in driving digital transformation within their respective functions.

• Frontline users must play a central role in the transformation process:

  • Agile Teams: Joining agile teams that dynamically coalesce and dissolve based on evolving business needs.

Building Tech Intensity

• Tech intensity measures the extent to which employees leverage technology to drive digital innovation and achieve tangible business outcomes.

• Companies that invest in technology and provide accessible tools empower their employees to achieve higher tech intensity and superior performance.

Digital Transformation Pays Off

• Leaders in tech intensity demonstrate significantly higher revenue growth and compound annual growth rate (CAGR) compared to laggards:

  • Leaders' Revenue Growth (2016–2019): 14.9\%.

  • Laggards' Revenue Growth (2016–2019): 7.1\%.

  • Leaders' CAGR (2016–2019): 9.1\%.

  • Laggards' CAGR (2016–2019): 4.4\%.

• Correlations between technology, capability, and architecture indices and measures of performance validate the impact of digital transformation.

• Greater intensity, particularly in investments in technical and organizational architecture, drives higher revenue growth.

Staging the Transformation

• Investing solely in technology does not guarantee improved performance. The architectural, managerial, and organizational approaches to transformation explain the differences among firms.

Traditional Model

• In the traditional model, digital and technology investments are primarily the responsibility of the IT department, resulting in:

  • Scattered and Inconsistent Impact: Limited and uneven impact across the organization.

  • Siloed Projects: Projects are customized to individual silos, leading to a lack of consistency and connectivity.

  • Limited Scalability: Sharing, scaling, or distributing innovation efforts across the organization becomes virtually impossible.

  • Perception Gaps: Discrepancies in perceptions of impact between technology and business employees.

Bridge Model

• Companies adopting the bridge model launch pilots that connect separate groups and develop shareable data and technology assets.

• Executives, managers, and frontline workers collaborate with IT and data scientists to drive innovation.

Hubs

• Organizations form data and capability hubs to link and engage additional functions and business units, shifting the bottleneck in innovation from technology investments to workforce capabilities.

• Companies invest in coaching and training a larger community of employees to enhance their digital skills and capabilities.

Platform Model

• Data hubs merge into a comprehensive software foundation that enables the rapid deployment of AI-based applications.

• Firms focus on building data-engineering capabilities and encouraging the reuse and integration of machine-learning models.

• Analytics-based prediction models are applied across the business, with increasing automation of basic operational tasks.

• Organizations function more like software companies, leveraging data and AI to drive efficiency and innovation.

Native Model

• Companies deploy an operating architecture centered on integrated data assets and software libraries, designed to deploy AI at scale.

• Hallmarks of the native model include a core of experts, broadly accessible tools, and investment in training and capability-building among businesspeople.

• These companies approach the capacity of digital natives like Airbnb and Uber, demonstrating advanced digital maturity.

The Imperative for Leaders

• Leaders must embrace transformation and work to sustain it by:

  • Articulating a clear strategy and communicating it relentlessly.

  • Establishing an organizational architecture to evolve into.

  • Deploying a real governance process to track, coordinate, and integrate technology projects.

  • Championing agility in all business initiatives.

  • Breaking free of tradition.

  • Training and coaching employees to understand the potential of technology and data, and release the innovators within their workforce.

  • Technology providers need to create tools and technology that make driving transformation intuitive for frontline workers while keeping data secure.