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business person places AI puzzle piece to represent the concept of AI in New Product Development

ISBM Report of Member Firms’ Use of AI in New Product Development

January 25, 2024

In the dynamic world of business, staying ahead of the curve is paramount. One of the most pivotal innovations in recent years is Artificial Intelligence (AI). Its potential to reshape industries and redefine operational efficiencies is undeniable. 

New Product Development (NPD) stands at the forefront of this transformation, with AI promising to revolutionize the way products are conceived, developed, and brought to market. 

But how far has this promise been realized? Drawing from a comprehensive study by ISBM Distinguished Research Fellow Dr. Robert G. Cooper of member firms, this article explores the current state of AI in NPD, shedding light on the achievements of early adopters and the broader landscape of typical firms.

Quick Takeaways

  • AI promises to transform New Product Development (NPD), but its broad adoption is still growing, highlighting a gap between early adopters and the mainstream.
  • Companies like GE and Unilever have showcased significant AI-driven improvements in their NPD processes.
  • Businesses face hurdles like limited AI awareness, resource constraints, organizational resistance, and the need for a clear AI strategy.
  • Successful AI integration in NPD requires assessing organizational mindset, infrastructure, executive support, and strategic vision.
  • Leaders must actively guide their organizations towards AI, emphasizing timely adoption, education, strategic planning, expert collaboration, and risk mitigation.

The Current Landscape of AI in New Product Development

In recent years, the buzz around AI and its potential to revolutionize various sectors has been prominent. One area that has garnered significant attention is New Product Development. 

With the promise of remarkable benefits such as drastically reduced development times and an accelerated pace of innovation, AI’s integration into NPD seemed almost inevitable. However, a recent study by Dr. Robert G. Cooper of ISBM member firms paints a different picture.

The study set out with a primary objective: To gauge the current status of AI adoption in NPD among typical firms. While pioneering companies have already showcased the transformative benefits of AI in NPD, the broader landscape is less promising. 

The majority of surveyed businesses have not yet integrated AI into their NPD processes, especially across the 13 identified crucial areas. Moreover, the overall “intention to adopt” AI remains tepid, with a slight inclination towards Natural Language Processing (NLP).

The potential of AI in NPD is evident, but its widespread adoption among typical firms is still in its infancy, indicating a disparity between the early adopters and the mainstream.

Case Studies: Early Adopters of AI in NPD

Several pioneering companies have already harnessed the transformative potential of AI in NPD to achieve remarkable results. These early adopters serve as beacons, illuminating the path for others to follow. 

Below, we explore some of these groundbreaking case studies:

GE's Enhanced Design Speed

image shows General Electric logo
  • Challenge: Traditional design and testing of turbine blades was time-consuming, often taking engineers two days to analyze the fluid dynamics of a single blade design.
  • AI Solution: By leveraging AI, GE trained a surrogate model that drastically reduced this time.
  • Outcome: The company can now evaluate a staggering one million blade design variations within just 15 minutes, effectively halving their overall design times.

Unilever's Robotic Laboratory

image shows Unilever’s logo
  • Challenge: Ensuring consistency and efficiency in the creation and testing of new products.
  • AI Solution: Unilever invested in a $120-million laboratory, the “Material Innovation Factory” (MIF), which is staffed entirely by robots specializing in material chemistry.
  • Outcome: This state-of-the-art facility has set a new benchmark for the chemical, materials, and pharmaceutical sectors, ensuring meticulous data processing and unparalleled consistency across tests.

Nestle's Innovative Concept Generator

  • Challenge: Gleaning insights from diverse data sources and identifying market gaps and opportunities.
  • AI Solution: Nestle introduced an AI-powered “concept generator” that analyzes data sources, identifies market gaps, and generates new product concepts based on the insights.
  • Outcome: This method, applicable to both B2C and B2B sectors, has streamlined their product ideation process, making it more data-driven and efficient.

General Motors' AI-Driven Car Design

image shows General Motors logo
  • Challenge: Traditional customer concept-testing for car designs was both expensive and time-consuming.
  • AI Solution: GM employed a generative AI model that creates fresh car designs based on designer prompts. Additionally, an AI predictive model was used to forecast consumer preferences.
  • Outcome: The company effectively eliminated the initial customer concept-testing phase, saving time and resources.

Digital Twins in NP

image shows Siemens and General Electric logos
  • Challenge: Monitoring product performance post-launch and during prototype testing.
  • AI Solution: Companies like GE and Siemens have adopted digital twins, digital models that mimic products or components. These models can be used during development for prototype testing and post-launch for monitoring product performance.
  • Outcome: GE successfully implemented digital twins for its GE90 engines on the Boeing 777, enhancing monitoring and performance. Siemens, a pioneer in this domain, introduced ATOM, a virtual model for its turbines and compressors, and also offers similar software to customers for their NPD.

While industry giants like GM, GE, Siemens, Unilever, and Nestle have taken the lead, the question remains: How can more typical firms harness the power of AI in their NPD processes? 

The Gap in AI Adoption

While the benefits of AI in New Product Development are evident, a significant gap exists between the early adopters and the mainstream. Bridging this gap requires not just technological solutions but also a shift in:

  • Mindset
  • Strategy
  • Organizational culture 

As the case studies of pioneering companies demonstrate, those willing to navigate these challenges stand to gain a competitive edge in the market. This section explores the reasons behind this disparity and the challenges faced by typical firms.

Lack of Awareness and Understanding

Many businesses remain unaware of the full spectrum of AI’s capabilities in NPD. Without a clear understanding of the many potential benefits and new applications, companies may be hesitant to invest resources in AI-driven initiatives.

Resource Constraints

Implementing AI solutions often requires significant investment, both in terms of finances and human resources. Smaller firms, in particular, may find it challenging to allocate the necessary funds and expertise to AI projects.

Organizational Resistance

Change, especially one as transformative as AI, can be met with resistance within an organization. Employees may fear job displacement, while management might be wary of the uncertainties associated with new technologies.

Lack of Clear Strategy

Without a well-defined strategy for AI adoption, businesses can find themselves overwhelmed by the plethora of AI tools and applications available. This can lead to piecemeal adoption, which often fails to deliver the desired results.

graph shows the many AI application options available to businesses, leaving them with extensive options

Data Challenges

AI thrives on data. However, many companies struggle with data collection, management, and analysis. Inadequate or poor-quality data can hinder the effectiveness of AI applications.

Concerns Over Dependence

Some businesses express concerns about becoming overly reliant on AI, fearing potential vulnerabilities, especially in the face of evolving cybersecurity threats.

Slow Adoption Rate

The study by Dr. Cooper highlights a tepid “intention to adopt” AI among surveyed businesses. This indicates a broader trend of hesitation or lack of urgency in integrating AI into NPD processes.

Performance Results of AI in NPD

While the early adopters have showcased impressive outcomes, what does the broader data indicate about the results of AI in NPD?

The performance results of AI in NPD present a mixed picture. While the technology holds immense promise, its true potential is realized only when businesses commit to a holistic AI strategy, backed by quality data and organizational buy-in.

Limited Adoption, Limited Results

The study revealed that many businesses have not yet fully integrated AI into their NPD processes. Consequently, the performance improvements reported by these firms are modest at best. On average, there’s only a 20% improvement across key metrics like reduced time-to-market and better decision-making.

graphic shows that there's only a 20% improvement across key metrics like reduced time-to-market and better decision-making

Success Stories of Heavy Adopters

In contrast, companies that have heavily invested in AI for NPD report significant gains. For instance, GE’s AI-driven design optimization led to a 50% reduction in development time for turbine designs. Similarly, Nestle, by leveraging AI, accelerated its product development pace by 60% over six years.

The Potential vs. Reality Disparity

While the potential benefits of AI in NPD are vast, the actual results achieved by the majority of firms in the study were underwhelming. This underscores the gap between what’s possible with AI and what’s currently being realized by most businesses.

The Importance of Data Quality

AI’s effectiveness is intrinsically linked to the quality of data it’s fed. Firms that reported better performance results often had robust data management practices in place, ensuring that their AI tools had accurate and comprehensive data to work with.

Beyond Quantitative Metrics

While metrics like time-to-market and cost savings are crucial, the qualitative benefits of AI in NPD shouldn’t be overlooked. Enhanced decision-making, improved product quality, and the ability to innovate more effectively are some of the less tangible, yet equally significant, outcomes of AI adoption.

A Word of Caution

It’s essential to approach the reported performance results with a discerning eye. The modest improvements reported by many firms in the study do not necessarily reflect the limitations of AI. Instead, they highlight the challenges of limited adoption and the need for a more comprehensive AI strategy.

Readiness to Adopt AI in NPD

The journey to integrating AI in New Product Development is complex, and a company’s preparedness is crucial for success. Here is a snapshot of the current state of businesses’ readiness to embrace AI:

A Word of Caution

It’s essential to approach the reported performance results with a discerning eye. The modest improvements reported by many firms in the study do not necessarily reflect the limitations of AI. Instead, they highlight the challenges of limited adoption and the need for a more comprehensive AI strategy.

graph explores businesses’ readiness to adopt AI in New Product Development
  • Adoption Levels. Many firms are still at the starting line, with limited AI integration in their NPD processes.
  • Organizational Mindset. While some leaders show patience for AI benefits, there’s a general reluctance to give AI decision-making autonomy.
  • Infrastructure and Skills. Beyond software, businesses often lack the technical expertise and infrastructure to support AI-driven NPD.
  • Executive Support. The absence of a strong executive champion can hinder AI adoption and its potential impact.
  • Strategic Vision. A clear, top-down strategy for AI is essential, rather than a fragmented approach.
  • Cultural Readiness. A culture of innovation and adaptability can significantly smoothen the AI integration process.
  • Training. Investing in team training ensures everyone understands and can leverage AI’s potential.
  • Risk Management. With AI comes risks, from data security to algorithm biases, necessitating robust risk strategies.

Businesses must critically assess their readiness across various facets. Those proactive in addressing these challenges will be better poised to tap into AI’s transformative power.

Messages for Management: Embracing the AI Future

As the landscape of New Product Development undergoes a seismic shift with the advent of AI, it’s imperative for management to not only take note, but to actively steer their organizations towards this promising horizon. 

Here are some key takeaways for leaders looking to harness the power of AI in NPD:

The Time is Now

With early adopters reaping significant benefits, sitting on the sidelines is no longer an option. As highlighted by a Forbes article, the consequences of inaction can be detrimental in this fast-evolving landscape.

Educate and Inform

Ignorance can be a significant barrier. Invest in workshops, seminars, and training sessions to ensure that the entire team, from top executives to ground-level employees, understands the potential of AI in NPD.

Strategic Vision is Key

AI adoption should not be a fragmented effort. Develop a holistic, enterprise-wide strategy, ensuring that AI initiatives align with broader business goals.

Collaborate with Experts

Engaging with AI consultants or specialists can provide valuable insights, helping tailor AI strategies to specific business needs and ensuring effective implementation.

Cultural Shift

Beyond technology, embracing AI requires a cultural transformation. Foster a culture that values innovation, continuous learning, and is open to change.

Risk Mitigation

While AI offers numerous benefits, it’s essential to be aware of potential pitfalls, from data security concerns to ethical considerations. Implement robust risk management strategies to navigate these challenges.

Stay Updated

The world of AI is rapidly evolving. Regularly review and update AI strategies to ensure they remain relevant and effective.

Embrace the AI Era Today with ISBM

The potential of AI in New Product Development is evident, with early adopters reaping significant benefits. Yet, many businesses remain hesitant, with limited AI adoption. 

As ISBM Research Fellow, Dr. Cooper’s study highlights, the AI future is already here. Businesses face a choice: Embrace this change and harness AI’s vast potential or risk being left behind in the competitive landscape. The call to action is clear: It’s time to adapt, innovate, and lead in the AI-driven era of NPD.

Ready to adapt, evolve, and strive for excellence? ISBM is a nonprofit, global network of business researchers and practitioners. Ask about how an ISBM Membership can help you now or check out our courses in NPD, taught by Dr. Cooper or visit ISBM today to learn more!

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