Ever wondered how successful companies innovate and grow? What sets apart Aetna’s Laurie Brubaker, and IBM's Blue Gene project? How are Dedicated Teams and Performance Engines key to successful innovation? Dive in to discover the secrets of their success, the role of leadership, and the critical balance between ongoing operations and innovation initiatives.

The Other Side of Innovation: Solving the Execution Challenge

In 2004, IBM unveiled Blue Gene, the world's fastest computer, powered by a vast network of standard chips rather than the most powerful processors. This groundbreaking design was achieved by a Dedicated Team. In contrast, Deere & Company, renowned for its 8030 tractor, relied solely on its existing product development team, the Performance Engine, for innovation. 

Performance Engines excel in efficiency and specialization but struggle with innovation tasks outside their expertise. Companies often overestimate these engines' capabilities, leading to disruptions in ongoing operations. For IBM's Blue Gene, a Dedicated Team was essential, whereas Deere managed without one for the 8030 tractor.

Understanding the limitations of Performance Engines is crucial. They face constraints in skills and work relationships, defined by depth, power balance, and operating rhythm. For instance, BMW created Dedicated Teams to develop regenerative brakes for hybrid vehicles, Electrolux formed new teams to shift focus to premium products, and Timberland established an Invention Factory to innovate trail running shoes.

Analog Devices (ADI) learned the hard way about the importance of organizational design for innovation. Despite being skilled in analog signal processors, ADI's Performance Engine was ill-equipped to commercialize a new digital signal processor (DSP), leading to inefficiencies and missed opportunities.

Successful innovation requires a partnership between a Dedicated Team and the Shared Staff. The Shared Staff handles routine tasks aligned with the Performance Engine's strengths, while the Dedicated Team tackles nonroutine tasks. This collaboration ensures the entire initiative is managed effectively, embracing uncertainty and leveraging specialized skills.

In short, balancing the capabilities of Performance Engines with Dedicated Teams is essential for innovation. Companies must assess work relationships and organizational dynamics to ensure both ongoing operations and innovation tasks are managed seamlessly.

Can Dedicated Teams Like IBM's Blue Gene Drive Innovation Better Than Performance Engines?

In 1995, the New York Times took a bold step into the digital world by launching New York Times Digital (NYTD). They created a Dedicated Team to manage everything from site construction to ad sales. However, by 1998, despite beating traditional rivals, they were missing out on new media opportunities like Monster.com. They realized their team was acting too much like a traditional newspaper, and only after a major overhaul did they find success.

Organizations often struggle with "organizational memory," a tendency to stick to old habits even when trying to innovate. To overcome this, companies need to carefully assemble Dedicated Teams by identifying needed skills, hiring the best people (from inside and outside), and designing a custom organizational model. This involves creating new roles and responsibilities, distinct cultures, and separate physical spaces to ensure the team can operate independently from the "Performance Engine" of the main business.

For example, Harley-Davidson successfully expanded its customer base by hiring people with deep services experience, breaking free from their manufacturing-centric roots. Hulu thrived by hiring outsiders with internet expertise, proving critics wrong by outperforming YouTube. 

Companies must avoid common traps like relying too much on insiders, adopting existing roles and metrics, and reinforcing old power dynamics. Instead, they should embrace new job titles, redefine work processes, and create a distinct culture for the Dedicated Team. This approach may be uncomfortable and challenging, but it’s essential for successful innovation.

Take the example of Dow Jones, which reinvented its subscription marketing process by integrating print and online teams. They hired outsiders, reshaped roles, and created new metrics, leading to a significant increase in subscriptions and efficiency.

Thomson expanded into business information services for law firms by assembling a Dedicated Team with outside hires and custom roles, successfully launching Peer Monitor. Lucent launched a network transformation services group by hiring experienced outsiders and creating new processes, leading to substantial revenue growth.

Ultimately, assembling a Dedicated Team requires defining necessary skills, hiring the best people, and creating a custom organizational model. This approach helps overcome organizational memory and drives successful innovation.

How NYT, Harley-Davidson, and Hulu Mastered Innovation with Dedicated Teams

Laurie Brubaker, an Aetna leader, saw an untapped opportunity in individual health insurance policies in 2005. With 45 million uninsured Americans, Brubaker believed Aetna could make a significant impact. CEO Ron Williams supported her vision, and soon Brubaker's initiative expanded to thirty states, serving 250,000 members and becoming Aetna’s second-fastest-growing business. 

Brubaker faced challenges in breaking away from Aetna’s established procedures, requiring new skills such as medical evaluations and Internet marketing. She leveraged Aetna’s network of health providers, balancing innovation with existing assets. Her diplomatic approach, recognizing and valuing her internal partners, was crucial in maintaining a positive relationship with Aetna’s Performance Engine.

The essence of successful innovation leadership, as demonstrated by Brubaker, lies in collaboration, not confrontation. Innovation leaders must create Dedicated Teams distinct from the Performance Engine while treating the existing operations as strategic partners. Conflicts are inevitable, as innovation demands short-term sacrifices for long-term gains, competing for resources and attention with ongoing operations. 

Positive and persuasive leadership is vital, but often insufficient. Senior executives must actively support innovation leaders, advocating for long-term priorities over immediate profits. Anticipating conflicts and managing them proactively is essential. Formal resource allocations should be streamlined, innovation initiatives should pay fully for Shared Staff contributions, and contingency plans should be discussed in advance.

Three companies illustrate successful partnerships between Dedicated Teams and Shared Staff: 

1. Dow Jones Integrated Solutions (DJIS): Facing an evolving advertising landscape, DJIS needed cross-media collaboration and longer sales cycles. CEO Rich Zannino and Matthew Goldberg built a Dedicated Team, respecting the Shared Staff's time and minimizing fears of competition. Zannino’s advocacy and strategic incentives ensured DJIS’s success, achieving $25 million in incremental revenues.

2. WD-40 Company: Seeking new growth, CEO Garry Ridge formed Team Tomorrow to develop breakthrough products like the WD-40 No-Mess Pen. Despite initial challenges, strategic collaboration, clear role division, and steady CEO support led to successful new product launches, enhancing the company’s market reach.

3. Infosys Consulting: Expanding from software development to IT-related consulting, Infosys built a Dedicated Team under Steve Pratt. Despite initial resistance, Pratt’s persuasive leadership and senior management’s active support fostered successful client engagements, contributing to Infosys’s continued growth, reaching $4 billion in revenues.

Effective innovation management requires a disciplined approach, treating initiatives as experiments. Learning from experiments involves formalizing the process, breaking down hypotheses, and seeking truth analytically. Success hinges on fast learning and adapting to insights, ensuring long-term growth and innovation.

How Did Laurie Brubaker Propel Aetna to New Heights in Individual Health Insurance?

In 2004, Sony Corporation’s electronics business in the U.S. suffered due to poor customer service. Within two years, they turned it around, thanks to disciplined experimentation. Phil Petescia, a senior executive at Sony, hypothesized that customer service agents would be more effective if they specialized in certain products or issues. He anticipated that this specialization would lead to deeper knowledge, more problem-solving, and greater job satisfaction among agents.

The biggest challenge was connecting customers to the right specialist on the first try. Sony employed an automated system to ask diagnostic questions—cautiously, to avoid customer frustration. Petescia ran experiments, varying the number and type of questions asked, discovering the “point of pain” where customers became agitated. This formal approach, tracking every change meticulously, led to dramatic improvement in customer service.

Learning from experiments is crucial, but it's not easy. Executives often prioritize results over learning. However, focusing on learning—turning speculative predictions into reliable ones—actually leads to better results. Consider two companies with identical innovation initiatives: one focuses on results, the other on learning. The latter adjusts its predictions based on new data and revises its plans, leading to better outcomes, even if it fails early and cheaply.

Learning cannot be left to intuition alone. Cooking and video gaming, with their quick and clear results, are ideal environments for experimentation. Business experiments, however, often yield partial results over time. A rigorous learning process is essential to convert these partial answers into a clear picture.

The scientific method is indispensable for innovation. Before launching an experiment, write down your plan, expectations, and reasons. Analyze disparities between predictions and outcomes to draw lessons and revise the plan. This process helps validate or invalidate assumptions and improve predictions.

Sony’s success illustrates the importance of disciplined experimentation. Here are ten principles to guide formal experimentation:

1. Invest heavily in planning.
2. Create the plan and scorecard from scratch.
3. Discuss data and assumptions.
4. Document a clear hypothesis of record.
5. Find ways to spend a little, learn a lot.
6. Create a separate forum for discussing results.
7. Frequently reassess the plan.
8. Analyze trends.
9. Allow formal revisions to predictions.
10. Evaluate innovation leaders subjectively.

Thomson Corporation and Infosys provide inspiring examples of companies that have successfully implemented these principles. Thomson developed a formal process for large product development projects, scrutinizing plans across multiple functions, offering data sets early, and holding frequent reviews. Infosys, aiming to grow into a full-service consulting firm, created separate systems for new service launches, focused on assumptions, and monitored trends in performance.

To achieve quick learning and better results, innovation leaders must adopt a rigorous process based on the scientific method. Each initiative requires a dedicated plan, frequent reviews, and subjective evaluation of leaders. Following these principles will minimize the cost of failure and maximize the probability of success.

How Sony’s Disciplined Experimentation Transformed Customer Service

Let's explore the importance of breaking down hypotheses into smaller, testable assumptions for innovation initiatives. Take the case of auto insurance, where most people believe they are better-than-average drivers and find the system unfair. Roger Parker at Allstate redefined the innovation process to prioritize consumer feedback, leading to the creation of Your Choice Auto (YCA) with features like accident forgiveness and rewards for safe driving.

When deciding how to market YCA, Parker's team tested five different sales approaches. Surprisingly, the least favored approach, starting with the highest-priced package, proved most successful. This disciplined experiment-and-learn approach helped Allstate launch YCA successfully, addressing various unknowns such as customer appeal and profitability.

Analog Devices, Inc. (ADI), a semiconductor company, faced similar challenges in the cellular phone market. Initially focusing on sound quality, ADI shifted its strategy to prioritize functionality and customer support. Despite fluctuations, ADI continued to adapt, eventually realizing that cost was a critical factor. Unable to compete on price, ADI exited the market.

Cause-and-effect diagrams are powerful tools for documenting assumptions and guiding conversations about an innovation initiative's progress. They help maintain focus on long-term strategic concerns, make competing theories explicit, and avoid applying inappropriate metrics from the existing business.

IBM's approach to building the world's fastest computer, Blue Gene, illustrates the importance of resolving critical unknowns early and inexpensively. By systematically testing prototypes with increasing numbers of chips, IBM validated the potential of massively parallel computing.

In short, quick learning is facilitated by a clear, shared hypothesis of record. Simple sketches of cause and effect are superior to spreadsheets for communicating assumptions. Mental simulation is adequate for early predictions, while quantitative analysis becomes valuable for refining estimates once cause-and-effect relationships are established. Identifying and resolving the most critical unknowns early is crucial for innovation success.

Can Roger Parker's Innovative Approach at Allstate Transform the Auto Insurance Industry?

Imagine the bustling world of the toy industry, where creativity meets pressure, especially during the holiday season. Fisher-Price, a subsidiary of Mattel, relies heavily on innovation, producing hundreds of new products each year. 

Tina Zinter-Chahin, the head of research and development, likens her industry to fashion but with "hard tooling." The company’s success hinges on both creativity and rigorous observation of children playing with toys. However, innovation demands risk-taking, which requires a sense of personal safety.

Zinter-Chahin fosters a culture of psychological safety and lifelong learning, ensuring her team feels secure in taking risks. Celebrations follow successful launches, while failed products become learning opportunities. She acknowledges that conversations about failure are challenging but crucial for future success. Such discussions help turn speculative predictions into reliable ones.

Overconfidence in predictions is a dangerous bias in innovation, often leading to the presumption that initial plans are correct. This bias can hinder learning and reduce the odds of success. Unlike the Performance Engine of a company, which thrives on predictability and accountability to plan, innovation requires flexibility and the presumption that initial predictions are likely wrong.

Executives like Zinter-Chahin understand the need for a distinct approach to innovation. At Infosys, humility and lifelong learning are celebrated, aiding innovation. Thomson Corporation employs strategies like conservative initial plans and upward revisions. Innovation leaders like Brian Hall evaluate performance based on both execution and learning, creating an environment conducive to truth-seeking.

Accountability in innovation is multifaceted—leaders should be responsible for results, actions, and learning. Holding someone accountable for actions involves close scrutiny, while accountability for learning demands a rigorous process of hypothesis testing and revision. Successful innovation leaders must embrace a learning mindset, face facts, and continuously improve predictions.

Incentives play a crucial role in attracting top talent to innovation initiatives. Companies must offer rewards even when initiatives fail despite good leadership, ensuring innovation leaders are motivated and supported. Creating the right incentives helps foster a culture where innovation thrives.

In short, leading an innovation initiative requires addressing fundamental incompatibilities with ongoing operations. Success demands a custom organizational model and a rigorous learning process. If done right, leaders can move on to greater roles, shaping a more innovative company from the top. As supervising executives, they must ensure initiatives get off to a good start, monitor interactions with the Performance Engine, stay engaged in the learning process, and shape the initiative’s endgame.

Ultimately, innovation leaders must navigate biases, foster psychological safety, and embrace accountability for learning. They need to craft clear innovation agendas aligned with strategic objectives, ensuring resources are focused and initiatives are executed effectively. With the right approach, innovation can become a powerful engine driving long-term success.

Tina Zinter-Chahin's Innovation Magic at Fisher-Price: Balancing Creativity, Risk, and Learning

In 2004, IBM introduced Blue Gene, the world's fastest computer, thanks to a Dedicated Team that utilized a network of standard chips rather than relying on the mightiest processors. Meanwhile, Deere & Company innovated with its 8030 tractor using its existing team, the Performance Engine. This highlights a key difference: Performance Engines are great at efficiency but often falter at groundbreaking innovation, which sometimes requires a Dedicated Team.

Companies like BMW, Electrolux, and Timberland recognized this and formed Dedicated Teams for their innovative projects, while Analog Devices (ADI) discovered the hard way that their Performance Engine couldn't commercialize a new digital signal processor effectively, missing out on opportunities.

Successful innovation necessitates collaboration between Dedicated Teams and Shared Staff. The Shared Staff manages routine tasks seamlessly, while the Dedicated Team tackles nonroutine challenges, ensuring effective management of the entire initiative.

In 1995, the New York Times launched NYTD to venture into the digital world with a Dedicated Team. Despite initial success, they missed new media opportunities, learning that innovation requires adapting beyond traditional methods.

Overcoming organizational memory—sticking to old habits—is crucial for innovation. Companies need to assemble Dedicated Teams by identifying necessary skills, hiring the best people, and designing a custom organizational model. Harley-Davidson expanded its customer base by hiring services experts, and Hulu thrived by bringing in internet-savvy outsiders.

Avoiding common traps like relying too much on insiders and reinforcing old power dynamics is essential. Companies should embrace new job titles, redefine work processes, and foster a distinct culture for Dedicated Teams.

Dow Jones successfully reinvented its subscription marketing by integrating print and online teams, reshaping roles, and creating new metrics, leading to significant growth. Similarly, Thomson and Lucent expanded into new markets with Dedicated Teams, achieving substantial success.

Aetna's Laurie Brubaker exemplified successful innovation leadership by expanding individual health insurance policies, balancing innovation with existing assets, and maintaining positive relationships with Aetna's Performance Engine.

Innovation leaders must treat existing operations as strategic partners, anticipating conflicts and managing them proactively. Senior executives' support is vital for long-term priorities over immediate profits. Companies like Dow Jones, WD-40, and Infosys demonstrate successful partnerships between Dedicated Teams and Shared Staff, achieving significant growth through strategic collaboration.

Learning from experiments is crucial for innovation. IBM's Blue Gene project illustrates the importance of resolving critical unknowns early. Companies must invest in planning, customize metrics, document hypotheses, and frequently revise plans to ensure successful innovation.

In the toy industry, Fisher-Price fosters a culture of psychological safety and lifelong learning, turning failed products into learning opportunities. Overconfidence in predictions can hinder innovation, requiring flexibility and the presumption that initial plans might be wrong.

Accountability in innovation involves responsibility for results, actions, and learning. Innovation leaders must embrace a learning mindset, face facts, and continuously improve predictions. Incentives play a crucial role in attracting top talent and fostering a culture where innovation thrives.

Leading an innovation initiative requires addressing incompatibilities with ongoing operations, crafting a custom organizational model, and a rigorous learning process. Successful leaders navigate biases, foster psychological safety, and embrace accountability for learning, driving long-term success through effective innovation initiatives.

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The Other Side of Innovation: Solving the Execution Challenge

Vijay Govindarajan

7 Key ideas

21 MINS

4.5 (68)

Ever wondered how successful companies innovate and grow?

What sets apart Aetna’s Laurie Brubaker, and IBM's Blue Gene project?

How are Dedicated Teams and Performance Engines key to successful innovation?

Dive in to discover the secrets of their success, the role of leadership, and the critical balance between ongoing operations and innovation initiatives.