The Adoption and Performance Impact of AI in New Product Development: A Management Report

Dr. Robert Cooper

kHub Post Date: September 6, 2024

Read Time: 10 Minutes

The AI Landscape for NPD

The landscape of new product development (NPD) is undergoing a seismic shift, driven by the  adoption of Artificial Intelligence (AI). Early adopters like Nestlé, GE, Siemens, and Pfizer are redefining how they conceive, develop, and launch new products, achieving dramatic improvements in time-to-market, accelerating innovation, and setting new benchmarks for product ingenuity.[1] However, these industry giants have the advantage of vast IT infrastructures, deep pockets, and well-oiled change management processes.

But what about the average firm? Can typical companies realistically harness AI in NPD, and if so, what tangible benefits can they expect? A recent McKinsey study revealed that by early 2023, only 13% of firms globally had adopted AI for NPD,[2] despite the fact that “improved innovation” is the top benefit realized by firms that deployed AI in a variety of areas. [3]

Figure 1. AI-NPD Positioning Map shows where AI is used in the NPD process & the role of AI as a facilitator or originator

The AI landscape for NPD is complex, with over 40 different applications ranging from idea generation to market analysis, product design, and even in-use product testing. We mapped this landscape to visualize the tools available and where they can be applied (see Figure 1).[4] No studies to date had explored the performance impacts or even the adoption of specific AI tools such as those in Figure 1 for NPD, however… until now.

Our recent research, conducted with business leaders from the IRI and ISBM in the USA and Quer-Kraft in Germany, offers eye-opening insights. [5] These findings, initially detailed in IRI’s Research-Technology Management journal, are now translated into practical guidance for managers in this report.

We focus on three critical questions: [6]

1. How widespread is AI adoption for NPD, and which applications are gaining traction?
2. What measurable impacts is AI having on NPD performance, and which applications deliver the highest returns?
3. What are the key drivers of AI adoption, and how prepared are firms to embrace this technology?

AI Adoption in NPD

The adoption of AI tools for NPD is sluggish. Figure 2 illustrates this, showing that the average AI usage rate across 13 logical applications—from idea generation to pre-launch product testing—is only 6%! All but two of these applications have adoption rates under 10%. For insights on these AI applications, how they work, and industry illustrations, see 1 and 4.

Figure 2. Current usage of AI by application in NPD

Overall, 77% of firms are not using any of these 13 AI applications, resulting in an average adoption rate of 23%, which, although higher than McKinsey’s 13% figure from a year earlier, still indicates a long road ahead. German businesses generally have a higher adoption rate than those in the US, with 31% of German firms using at least one AI application for NPD compared to 18% of US firms.

Two of the most popular applications are market analysis and competitive analysis, used by 16% and 9% of businesses, respectively. This is unsurprising, as many managers already use search engines for similar tasks, so adopting a more sophisticated AI version is a natural progression. Natural Language Processing (NLP), which translates unstructured text, such as customer feedback into meaningful insights, is also gaining traction.

As the NPD process progresses into development and testing, AI adoption rates decline even further (Figure 2). Among these applications, AI for product design—particularly advanced forms of computer-aided design (CAD)—is the most commonly utilized.

Strikingly, none of the companies in our study report using AI to make go/no-go investment decisions in NPD projects—a stark contrast to the financial sector: “A Hong Kong venture capitalist fund credits a single member of its management team with pulling it back from the brink of bankruptcy. But the executive is not a seasoned investment professional, nor even a human being. It is an algorithm known as Vital.”[7]

The study also explored three advanced AI applications:

• AI in Project Management: Automates project plan creation, monitors progress, and flags potential delays or issues.
• AI in Portfolio Management: Tracks project performance, identifies risks, prioritizes projects, and automates reporting.
• NLP: Extracts actionable insights from unstructured data, such as customer reviews and social media posts, drawing strategic conclusions, and even conceptualizing new product ideas.

Despite AI’s transformative potential in both project and portfolio management, none of the businesses in our study use these three AI applications (Figure 2). Furthermore, intent to adopt remains modest, around 20%. In contrast, NLP is gaining ground, with 11% of businesses already utilizing it, and it ranks high in future adoption intent. This suggests that while AI’s broader role in project and portfolio management is still emerging, NLP is rapidly proving its value, particularly in harnessing unstructured data to drive innovation.

This very limited adoption of AI in NPD is alarming, given all the hype in business media, positive news releases from early adopters, the abundance of AI tools, and the higher adoption rate in Asian countries. What’s more, AI tools for these applications are commercially available, requiring no in-house software development, minimal internal expertise, and pose relatively low risks for experimentation.

Many non-adopter businesses indicated an intention to adopt AI for these same tasks in the foreseeable future (Figure 3). While future adoption rates are higher than current usage, they still fall short of 50% for most applications, except market and competitive analyses and NLP. A strong commitment to adopt AI for NPD tasks is notably lacking.

There are no significant differences in the relative popularity of AI applications between US and German firms: What’s popular in Germany is also more popular in the US.

Figure 3. Intention to adopt AI for NPD

Figure 4. Improvements realized by using AI for NPD

Impact of AI on NPD Performance

Although only a minority of firms have adopted at least one AI application for NPD, the outcomes for those businesses are very positive. Figure 4 shows the performance improvements across five key metrics for AI-adopter businesses.

The improvements are fairly consistent across metrics—from reduced time-to-market to better decision-making—averaging a solid 35% improvement. Better decision-making and accelerated NPD stand out as the top benefits.

Which applications have the greatest impact on performance? With so few businesses having adopted AI for NPD, discerning the impacts of specific applications is challenging. However, very significant correlations are detected between a handful of AI applications and performance results, as shown in Table 1. These impacts are most pronounced for project acceleration and enhanced decision-making, with minimal effects on the other three performance indicators.

The high-impact AI applications are all in the development and testing stages (Table 1):

• Product Testing and Validation: AI for automated product testing, virtual market testing, and in-use testing shows strong positive effects on both acceleration and decision-making.
• Product Design: Leveraging AI alongside CAD to design and optimize products yields positive impacts across multiple metrics, notably acceleration and decision-making, as well as fewer errors and improved productivity.
• Prototyping: Rapid prototyping and automatic translation of drawings into prototypes significantly impact acceleration.
• Extensive AI usage in NPD overall: Extensive use of AI generally impacts all performance metrics except enhanced agility. High-AI-user businesses, in particular, achieve shorter times to market and improved decision-making.

Despite frequent mentions in the literature, the impacts of AI in the front-end of NPD on performance metrics are subdued. Only AI for idea generation and competitive analysis significantly affect both acceleration and better decision-making.

Although often touted, the positive effects of NLP and AI for simulation modeling (including digital twins) are limited to project acceleration (modest impacts). AI for both portfolio and project management show no positive effects on performance metrics, but then none of the businesses in the study are using these tools either.

A word of caution: Just because an AI tool “shows no impact” does not mean that the AI tool is ineffective; it might be that so few firms were using it that no impact could be detected. But when an impact is detected in Table 1, it means that the impact is quite strong.

Drivers of AI Adoption

Are businesses truly ready to adopt AI? We evaluated five key readiness metrics, identifying the most important based on the strength of their connection (their correlations) to extensive AI use for NPD. These impactful “drivers of AI adoption” are shown in Table 2; the top three are:[8] 

1. Demonstrated business value, 
2. Senior management commitment, and 
3. Trust. 

Some drivers, like demonstrated business value, have a powerful correlation with extensive AI adoption—this single factor accounts for about half of what propels AI use.[9] However, as Figure 5 shows, evidence of AI’s business value for NPD is notably lacking [10]—partly because most AI projects in business fail! [11]

Figure 5. The five drivers of AI adoption: their relative strengths and impacts on AI adoption

The results reveal a low readiness level for AI adoption in NPD. Two-thirds of businesses have yet to embrace AI for NPD, and where an executive sponsor exists, they often lack the capability and credibility to lead this transformation.

None of the businesses studied are willing to hand over decision-making to AI, a significant red flag that could hinder AI adoption in NPD. A key challenge in AI implementation is building trust, particularly among line managers and executives, in the superiority of data-driven decisions over intuition or traditional methods.[12]

What’s striking is how poorly businesses perform on all the key drivers of AI adoption. Two of the top three—demonstrated business value and trust—are areas where businesses are particularly weak (Figure 5).

Next Steps

First, get up to speed on AI and its applications for product development. Leading firms have demonstrated that AI for NPD yields results, yet many respondents in our study seemed unaware of the AI applications and their potential in NPD. A number of articles can help you quickly get up to speed. [13]

Second, this must be a senior management-led initiative. Senior management must become AI literate, at least aware of its possibilities, then create a vision, define goals, and assemble a cross-functional task force to undertake the AI initiative.

A common mistake is to adopt AI on a piecemeal basis, with each department or subgroup experimenting independently. These pilots often lead nowhere, a phenomenon called “pilot paralysis.”

Finally, adopt and utilize a systematic roadmap for AI implementation. AI adoption is a complex journey, and venturing into it without a clear map is a recipe for failure. Investigate and commit to a structured AI roadmap, such as the RAPID process, which has proven effective in technology deployment for years. [14] This model outlines essential stages, tasks, decision-points, criteria, and roles, ensuring a strategic approach to AI in your business and NPD.

Start the Journey Now!

We predict a 13-year window for AI adoption, with the peak before the decade's end. [15] The clock is ticking, and now is the time to embark on this transformative journey.

Yes, there will be risks, costs, and uncertainties. But standing still is far riskier.

The only way to avoid obsolescence is to embrace innovation. And AI, the cornerstone of the 4th Industrial Revolution, is the most significant innovation of our time.

ABOUT THE AUTHOR

Dr. Robert Cooper, Professor Emeritus, McMaster University, Canada ISBM Distinguished Research Fellow at Penn State University A world expert in the field of management of new-product development and product innovation, Dr. Cooper has written 10 books on the topic and more than 170 articles. Bob is the creator of the globally-employed Stage-Gate (trademarked) process used to drive new products to market; a Fellow of the Product Development & Management Association; ISBM Distinguished Research Fellow at Penn State University. He is a noted consultant and advisor to Fortune 500 firms, and also gives public and in-house seminars globally.

LinkedIn: Dr. Robert G. Cooper
Website: www.bobcooper.ca

References

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