Accelerated New Product Development: Lessons for Leaders from“China Speed” by Dr. Robert G. Cooper
Feb 03, 2026
Dr. Robert G. Cooper, Creator of Stage-Gate®
January 30, 2026.
The “China Speed” phenomenon has exposed a harsh truth many in the West have long suspected: too many executives and managers have been slow to adopt the modern methods that drive real product innovation. Product innovation has always been the engine of competitive advantage and market leadership—but the Chinese have redefined how fast and effectively it can be done.
Just a decade ago, the prevailing belief was that Chinese firms excelled mainly at copying Western products quickly. Today, they are setting the pace in multiple industries— innovating, iterating, and scaling at speeds that leave many Western competitors struggling to keep up.
The rise of “China Speed” has forced business leaders across North America and Europe to reconsider how their organizations conceive, develop, and launch new products. The term itself captures the extraordinary velocity of innovation now characteristic of leading
Chinese firms [1].
Meanwhile, outside of the digital and IT sectors, physical product innovation in much of the West has slowed dramatically. Excessive risk aversion, layers of regulation and bureaucracy, cultural pessimism, and an overreliance on incremental gains have all contributed to stagnation [2]. As a result, many companies have resisted adopting 21stcentury product development practices—such as Agile, Iterative Development, Lean principles, and AI-driven tools—that are now fueling China’s rapid progress.
Ironically, most of these “Chinese methods” have Western origins. They were first developed in North America and Europe but failed to gain widespread adoption. Many are core components of the modern, fifth-generation Stage-Gate® framework [3]. The ten key advances driving accelerated innovation in China—several already being used by the most forward-thinking Western firms—should now be on every manager’s radar.
These include:
1. Iterative Development
2. Lean Development
3. Agile-Stage-Gate® Hybrid Model
4. Self-Managed, Effective Project Teams
5. Parallel Processing
6. AI-Powered Development
7. AI-Agentic Stage-Gate®
8. Semi-Autonomous Gates
9. How to Deploy AI
10. Tailored to Different Project Types.
1. Iterative Development
“Build and test” is a key mantra in modern NPD Stage-Gate® systems [3]. The most effective way to perfect and validate a product design is through a series of rapid “trial and error” iterations—experiments—building something, testing it technically and with users, learning, and then revising.
The principle of “iterate and learn” is hardly new: Thomas Edison exemplified it in his countless experiments leading to the electric light. Perhaps that is why the light bulb remains a universal symbol of innovation. Yet today’s development processes too often drift toward excessive analysis and risk aversion, slowing the experimentation that actually drives discovery. In the original Stage-Gate® system, a key premise was a sharp, early, and fact-based product definition completed before entering the Development stage. The logic was simple and compelling: a well-defined product concept accelerated technical work and reduced costly iterations later. But today’s environment is far more dynamic and uncertain. For innovative products, both market needs and technical solutions are often unclear at the outset. Customers may struggle to articulate what they value: as Steve Jobs observed, people don’t know what they want until you show it to them. So, build something and demo it! Further, the best technical solution frequently emerges only through trial and discovery. In such settings, even extensive front-end market research and technical assessment can miss the mark.
Figure 1. The Stage-Gate® process with gates and stages, showing the iterations in all stages—the white circles — built in.
Modern product development calls for iterative learning—a structured process of experimentation and feedback rather than freezing the early product definition. Fastpaced teams move toward a finalized product by building a series of iterative steps or loops—” build-test-feedback-and-revise,” shown as white circles in Figure 1—into their project [4]. They build product models and early prototypes, test them technically and with users, learn from the results, and refine their concepts. The emphasis shifts from predicting the perfect product to evolving it through evidence and experimentation.
Iterations should not be confined to just the Development and Testing stages in Figure 1. Even before physical development begins, teams can experiment by using 3‑D drawings, digital twins, or virtual prototypes to explore and validate designs early. This approach accelerates learning and leads to solutions that resonate with customers and work technically.
2. Lean Development
Lean Development means streamlining the new‑product process by systematically removing waste and inefficiency at every opportunity. This approach, proposed by Fiore decades ago, adapts value stream analysis from Lean Six Sigma—traditionally applied on the factory floor—to the NPD process [5].
The first step is to map the current NPD value stream, capturing major process steps, queues, hand‑offs, rework loops, and decision points, and identifying all work that adds no value. In NPD, the value stream is defined as the end‑to‑end linkage of all value‑added and non‑value‑added activities associated with creating a new product.
This mapping can be done by an NPD task force or by project teams starting out on their own projects. Sub-teams then analyze this map to identify the root causes of time‑wasters and inefficiencies, often using cause‑and‑effect tools such as a fishbone (Ishikawa) diagram, and then develop solutions. Targeted improvement actions may include eliminating unnecessary reviews, reducing the number of unneeded field trial cycles, overlapping tasks, and better synchronizing cross‑functional activities [3].
Although Fiore’s lean‑development variant is less sensational than other methods such as Agile, it has proven practical and robust in many NPD settings. Case examples show that disciplined value stream mapping and waste removal can cut development time substantially—often in the range of 20 to 40%—while also reducing engineering rework and improving on‑time project completion [6].
A second version of “lean” is that popularized by Ries, more specifically “Lean Startup” [7]. Lean Startup applies lean thinking to innovation: the goal is to minimize waste (product features, effort, and time that do not create value) by validating product assumptions with real customers as early as possible.
Instead of long, front‑loaded planning and “big bang” launches, teams cycle quickly through hypotheses, experiments, learning, and adaptation. Lean Startup treats innovation as a series of experiments with feedback loops (Build‑Measure‑Learn) to discover what customers really value before fully scaling a product. The result of each “Build” is an MVP (Minimum Viable Product), an early product version that works—the simplest version that delivers core customer value while enabling teams to gather validated feedback with minimal effort.
This cyclical approach is very similar to Iterative Development outlined above, although the iterations in Section 1 can also include concepts, virtual products, digital twins, and early prototypes. Chinese firms also emphasize an MVP mindset, launching “good‑enough” working products that satisfy core needs in order to seek rapid customer feedback [1].
Risks and challenges exist, however [8]. If the MVP is substandard and performs poorly, it can damage the eventual product’s image at full launch. It is also expensive to pivot the design in the case of a complex physical product—for example, a jet engine—versus making changes to the design of a software product.
3. Agile-Stage-Gate® Hybrid NPD
Agile development methods emerged in the 1990s in the software world to deal with IT projects that have dynamic requirements and uncertain information. Agile emphasizes short sprints, frequent builds, continuous customer feedback, evolving backlogs, and empowered, dedicated teams. Recognizing the potential benefits, many companies have adopted Agile principles for physical products by integrating them into some of the stages of Stage-Gate® [9].
In an Agile‑Stage‑Gate® hybrid model in Figure 2:
- Stage-Gate® defines the overall pathway structure—the stages, gates, and major tasks and deliverables.
- Within the Development and Validation stages, teams execute work in Agile sprints, delivering incremental prototypes or product versions in weeks rather than months. (Some firms use sprints for all stages of Stage-Gate®).
- Sprints are usually short—about four weeks for physical products, two weeks for software—but shorter than the iterations described in Section 1. There can be several sprints within an iteration in order to deliver a testable product version.
- Each sprint consists of a sprint planning meeting, execution of the work, a demo of results, and a sprint retrospective.
- Traditional Gantt charts and plans give way to sprint backlogs, velocity tracking, and frequent demos to stakeholders and customers. The “plan” is constantly being changed and updated.
- Agile requires autonomous, self‑managed, dedicated project teams; daily stand‑ups or morning Scrums provide effective within‑team communication [10,11].
Figure 2. The Stage-Gate® process with gates and stages, showing the iterations in all stages—the white circles — built in.
Adopting Agile is not in conflict with Stage-Gate®; rather, the two systems are complementary [12]. Stage-Gate® provides governance, a roadmap for the project team through to launch, and portfolio discipline. Agile brings speed, adaptability, and continuous learning. Together, they deliver “agility with discipline” [13] —a combination increasingly necessary in complex innovation environments.
Agile is not just for software developers. Manufacturers have adopted and used most of the elements of Agile within their existing systems with very positive results—increased customer satisfaction, time reduction, and reduced risk of failure [14]. By using Agile‑Stage‑Gate®, leading physical product firms such as Honeywell, GE, and LEGO have cut time‑to‑market and responded more quickly to changing customer requirements [15].
Additionally, Agile‑Stage‑Gate® works well for connected or IoT products that involve both physical product and software development teams and workstreams, according to a GAO study report to the U.S. Congress [16]: leading firms are using more effective approaches, “such as a model that combines aspects of Agile and Stage-Gate®.
4. Self-Managed, Effective Project Teams
Effective project teams are among the most powerful levers managers have to boost new product performance, yet they are often lacking and poorly designed. When projects go off track, post‑mortems reveal familiar issues—work scattered across functional “fiefdoms,” poor cross‑functional communication, and little shared ownership of outcomes.
Strong performers take a different approach [17]. They form clearly defined, cross‑functional teams that include R&D, marketing, sales, operations, and supply chain, holding this team accountable from early definition through launch. These teams act almost like a small business within the business—integrating diverse expertise to make faster, better decisions and closing the cracks that usually appear at functional hand‑offs.
High‑performing NPD teams combine the right composition with the right working conditions. Members bring complementary skills and prior experience with complex projects, and they stay with the project long enough to build trust, shared context, and continuity. Team tenure matters: keeping turnover low minimizes re‑learning during later stages. Realistic resourcing is equally important. While fully dedicated Agile‑style teams are ideal, they are often impractical in manufacturing and physical‑product settings where natural waiting times—for tooling, testing, or validation—make 100% dedication inefficient. A more workable model is the “almost dedicated” team, where core members devote roughly two‑thirds of their time to one project and participate in just one or two others, but not six others!
Leadership and governance complete the picture. Each project needs a capable, visible leader to define goals, shape the product concept, coordinate work, and advocate for resources, while maintaining team morale and cohesion. The most effective leaders act transformationally, encouraging open dialogue, shared purpose, and collaborative problem‑solving.
Senior management, in turn, must adopt an “eyes‑on, hands‑off” stance: providing clear strategic direction, reviewing progress at gates, but avoiding micromanagement. Clear goals, robust communication, and post‑launch accountability for business results are consistently linked to higher success rates and faster time‑to‑market.
5. Parallel Processing
Parallel processing (concurrent work) has emerged as an effective way to accelerate projects. Instead of waiting for each task or stage to be fully completed before starting the next, a well‑designed Stage-Gate® system encourages overlapping tasks and, where justified, overlapping stages [18]. Projects move forward as soon as critical information is sufficiently reliable, rather than “waiting for perfect data.”
Long lead‑time activities, such as equipment procurement, tooling, or market launch preparation, can be initiated earlier once risk is acceptable, even while some product testing continues. But it is a calculated risk. So, do calculate the risk: determine the expected value of both options—wait or move forward—considering each option’s consequences and their probabilities of occurring. Often the “move forward” option is the better choice.
The rapid development of COVID‑19 vaccines in the U.S. is a high‑profile illustration of this approach, as shown across the bottom half of Figure 3. Clinical, regulatory, and manufacturing workstreams were executed in parallel, supported by rolling FDA approvals and early manufacturing commitments, compressing the expected timeline from 10 years to under a year [19].
Figure 3. Parallel processing during the development of the COVID-19 vaccine accelerated the project by overlapping stages and moving decision points forward (source: GAO [19]).
6. AI-Powered Development
Artificial Intelligence (AI) is transforming all aspects of business, particularly NPD. Large early‑adopter firms demonstrate that AI not only finds many applications in NPD but also offers substantial payoffs, such as 50% reductions in development times [20]. Thus, leading firms are building AI into their Stage-Gate® NPD processes.
By 2025, more than 40 unique AI applications from 400+ vendors existed for AI in NPD. The varied nature of the applications—from conducting market research to undertaking engineering design—is exciting and somewhat overwhelming.
Our positioning map for AI in NPD, showing many different AI applications in NPD, provides a simpler visual presentation of this complex landscape in Figure 4 [21]. The horizontal axis shows where the AI application occurs in the Stage-Gate® process, while the vertical axis depicts AI’s role as an originator versus a facilitator. In the context of Stage-Gate®:
Figure 4. AI-in-NPD positioning map showing where AI is used in Stage-Gate® and its role: facilitator or originator [21]
- Front end: AI tools and large language models (LLMs) support idea generation, opportunity scanning, concept screening, market research, and even building the business case at a fraction of the time and cost of traditional methods [22].
- Development and testing: Digital twins and advanced simulation models enable rapid technical and in‑use testing for design optimization without requiring physical prototypes. AI also designs and tests new molecules and drugs; robots in labs synthesize compounds; and AI predicts chemical reaction outcomes [23].
- Launch and post‑launch: AI assists with launch planning, pricing, sales‑force optimization, and marketing communications. AI also continuously monitors product performance and customers’ product usage, providing feedback data for the development of product improvements [24].
Despite the reported benefits, AI adoption for NPD has been low. Only 23% of U.S. and EU firms were using AI for any task in NPD by early 2024, which rose modestly to 28% by 2025 [25]. A major reason for hesitation is that AI installations often do not yield the promised results—the AI-adoption failure rate for business is as high as 80% [26].
7. AI-Agentic Stage-Gate®
AI agents are a major step beyond today’s prompt-driven generative AI described just above [27]. These new systems have agency: they can interpret their environment, make decisions, execute tasks, and adapt with minimal human guidance [28].
In NPD, this means an AI agent can autonomously execute an entire stage in Stage-Gate® , for example, the “Build Business Case” Stage in Figure 5 [29]. The agent acts as an orchestra leader to integrate the various AI tools or instruments that undertake a market analysis, a VOC synthesis, the technical feasibility, and the financial modeling to create the business case – ultimately compressing this stage from weeks to hours. This time compression means that with some stages requiring minimal resources, the magnitude of the investment decision is reduced, and thus fewer gates—and therefore fewer stages— are needed, as shown in the AI Agentic Stage-Gate® model in Figure 5,
Figure 5. The new AI Agentic Stage-Gate® model, accelerated and compressed to just three stages by using AI agents to orchestrate stages and AI to semi-automate gates.
Stage-Gate≈ Agentic may seem like science fiction, but estimates suggest a Build Business Case AI Agent is one to four years away. An early version of such an agent to orchestrate Stage 1 in Figure 5 has already been trialed [29]. And Microsoft’s RD-Agent executes iterative R&D workflows with substantial autonomy from idea generation through refinement, acting as a research copilot to manage multi-step R&D projects [30].
8. Continuous Semi-Autonomous Gates
Gates no longer need to be discrete decision nodes at a point in time. Rather, each project should have its own database containing all information on the project from concept definition through to an updated business case. As new data becomes available—for example, learnings from iterations—the database is continuously updated.
Real‑time dashboards display key metrics on an ongoing basis so that project team members and management can stay apprised [31]. Typical metrics include:
- Project progress (e.g., the burndown chart for the iteration);
- Expected launch date;
- Current economic value of the project going forward; and
- Productivity Index (increase in value for each additional workday spent on the project) [32].
This up‑to‑date, per‑project database makes possible a series of mini‑gates in real time— not face‑to‑face scheduled meetings like traditional gates, but quick “health checks” of the project. Negative information—for example, a downturn in the Productivity Index— could trigger the need for a traditional gate meeting between the project team and management to make tough decisions and possibly pivot or rethink the project.
Ultimately, AI‑Agentic Stage-Gate® will semi‑automate even the gate decisions by applying AI evaluation tools such as AI‑PRISM to real‑time data streams [33]. The result will be semi‑autonomous, continuous, real‑time gates with minimal manual preparation and human work.
9. How to Deploy Artificial Intelligence
Although AI promises great efficiencies and improved effectiveness, deploying AI within the business has been a challenge. Firms currently face a very high failures rate of AI adoption-and-deployment projects—as high as 80, even 95%, according to reliable research results from the Rand Corporation, MIT, and an S&P Global study [34].
AI adoption-and-deployment projects share many characteristics with new product projects. Both are risky initiatives with significant failure rates. Both have customers, but with AI the “customer” is internal user group. And both require development work, but with AI solutions, the majority come from external vendors. Not surprisingly, many of the reasons for AI adoption failure are the same as for new product failure: a lack of understanding of user needs, technical difficulties with the product, a poorly executed launch, and a siloed approach [35].
Mitigating actions are much the same as those for NPD failure. An IBM report recommends a seven-step “stage-gating” process for AI adoption in order to validate business impact before scaling [36]. Similarly, an MIT study of European firms emphasizes the need for incremental funding and evidence-based decision gates to minimize waste and risk [37]. Forward-looking firms are therefore applying a tailored Stage-Gate® approach to AI adoption, similar to the model in Figure 1 but with only four stages [38], as in Figure 6.
Figure 6. A Stage-Gate® model for adoption and deployment of AI in the business [39].
If your business has faced challenges in adopting AI tools, maybe it’s time to consider a more structured and professional approach. This AI-adoption Stage-Gate® model provides a practical roadmap to turn experimentation with AI into successful pilots and broader deployment [39].
10. Stage-Gate® Tailored to Your Diverse Portfolio
Not all innovation projects justify a full five-stage process as in Figure 1. Many portfolios contain large numbers of smaller, lower-risk initiatives, such as product improvements, Figure 6. A Stage-Gate® model for adoption and deployment of AI in the business [39]. cost reductions, and customer specials. To avoid over-managing these projects, streamlined versions of Stage-Gate® exist, tailored to the activities and governance best matched for these lower risk developments, as shown in Figure 7 [1].
Specialized Stage-Gate® models now exist for Production Process Projects, New Service Projects, and others shown in Figure 7—customized processes for different types of developments for maximum efficiency and effectiveness. There’s even a process to handle sustainably projects—Eco-Stage-Gate®—with all the regulatory and legal issues outlined in tasks within the stages, and sustainability criteria at the gates [40]. This enables organizations to adopt one common framework and language—Stage-Gate®— and then tailor it to apply across diverse portfolios.
Figure 7. Some of the alternate Stage-Gate® models to match the specific needs of different types of innovation projects.
An important category of R&D projects emphasizes the “R” in R&D—science or more fundamental research projects. The deliverable is not a new product, but new knowledge, a new capability, or a discovery. Such Technology Development projects follow the two‑ or three‑stage model across the top of Figure 6 [41]. ExxonMobil Chemical’s research model, for example, features two stages that precede its usual five‑stage new‑product model [42].
Conclusion
China Speed should be a wake-up call for Western managers, not a curiosity. The message is clear: we already own most of the methods that now power China’s rapid product innovation, but we have failed to use them with the same urgency, consistency, and leadership resolve.
The ten approaches outlined in this article—Iterative and Lean Development, Agile-StageGate® hybrids, empowered teams, parallel processing, and AI-enabled, agentic decisionmaking—are not exotic Chinese inventions. They are largely Western ideas that have been adopted, scaled, and integrated more aggressively in China than in many North American and European firms. The problem is not a lack of knowledge; it is a lack of will, priority, and leadership.
This is why China Speed matters: it shows what is possible when modern product development methods are treated as strategic imperatives rather than optional “tools” for the project-management toolbox. If Western firms continue to rely on outdated, linear, risk-averse approaches, they will keep falling further behind in time-to-market, learning speed, and innovation impact—even in industries they once dominated.
Western firms still have the technical expertise, capital, and global brands to lead, but only if they choose to lead in howthey innovate. The methods are available. The examples are in plain sight. What is missing in many organizations is decisive, courageous leadership. The question for every manager reading this is: will you let China Speed remain a warning—or will you treat it as the catalyst to redesign your own innovation system, starting now?
The Author: Dr. Robert G. Cooper
Dr. Robert G. Cooper is the creator of the industry standard Stage-Gate® NPD process and co-founder of Stage-Gate International. He is also ISBM Distinguished Research Fellow, Smeal Business School, Penn State University, USA; Professor Emeritus, DeGroote School of Business, McMaster University, Canada; Honorary Advisor, Snyder Innovation Management Center, Syracuse University, USA; and Crawford Fellow of the Product Development & Management Association (PDMA).
Bob has also helped hundreds of firms improve their new product process and results, including organizations such as ABB, Bosche, Danfoss, Dow, Dupont, Exxon, GAO, HP, IBM, Labatt’s, LEGO, P&G, PPG, Swarovski, Telenor, and Tetra Pak. He has published 11 books, including the "bible" for NPD, "Winning at New Products", and more than 170 articles on the management of new products. He has won the IRI's (Innovation Research Interchange) prestigious Maurice Holland Award three times for "best article of the year".
Cooper holds Bachelor's and Master's degrees in chemical engineering from McGill University in Canada; and a PhD in Business and an MBA from Western University, Canada. Website: https://www.bobcooper.ca
Contact: [email protected]
References
Many of the articles below by the author are currently available at no charge on his ‘safe’ website: www.bobcooper.ca or simply try Googling the article info below.
1. Cooper, R.G. (January 20, 2026) ‘China Speed’: Accelerated product development the Chinese Way. kHUB PDMA Knowledge Hub. https://community.pdma.org/blogs/robert-cooper/2026/01/16/china-speed-accelerated-product-development-the-ch or Google PDMA KHUB and search for “China Speed”
2. Hoffman, A., and Thiel, P. (June 17, 2024). The Future of Innovation with Peter Thiel. The World of DaaS. https://www.worldofdaas.com/p/peter-thiel-innovation
3. Cooper, R.G. (December 2022). The 5-th Generation Stage-Gate Idea-to-Launch Process. IEEE Engineering Management Review (50) 4: 43–55. https://doi:10.1109/EMR.2022.3222937
4. Cooper, R. G. (2003). Stage-Gate® New Product Development Processes: A Game Plan from Idea to Launch. In E. Verzuh (Ed.), The Portable MBA in Project Management (2nd ed., pp. 309–346). Hoboken, NJ: John Wiley & Sons.
5. Fiore, C. (2005). Accelerated Product Development. New York, NY: Productivity Production Press.
6. Drogosz, J. (Sept. 22, 2022). Why Value-Stream Mapping is Essential to Product and Process Development. The Lean Organization. https://www.lean.org/the-lean-post/articles/why-value-stream-mapping-is-essential-to-product-and-process-development/
7. Ries, Eric. (2011). The Lean Startup: How Today’s Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses. New York, NY: Crown Business.
8. Rekhi, S. Challenges with The Lean Startup Methodology. Reforge. https://www.reforge.com/blog/lean-startup-methodology-problems
9. Sommer, A.F., Hedegaard, C.E., Dukovska-Popovska, I., and Steger-Jensen, K. (2015). Improved Product Development Performance through Agile/Stage-Gate Hybrids: The Next-Generation Stage-Gate Process? Research Technology Management 58 (1): 34–45. https://doi.org/10.5437/08956308X5801236
9. Cooper, R.G. and Sommer, A.F. (Sept. 2016) The Agile–Stage-Gate Hybrid Model: A Promising New Approach and a New Research Opportunity. Journal of Product Innovation Management (33) 5: 513–526. https://doi.org/10.1111/jpim.12314
10.Cooper, R. G. and Fürst, P. (August 17, 2023). Deploying Agile for Physical-Product Development: Big Challenges and Clever Solutions. IEEE Engineering Management Review, 1 –15. https://doi.org/10.1109/EMR.2023.3304356
12. Karlstrom, D. and Runeson, P. (2005). Combining Agile Methods with Stage-Gate Project Management. IEEE Software (22) 3: 43–49.
13. Boehm, B. and Turner, R. (2004). Balancing Agility and Discipline: A Guide for the Perplexed. Boston, MA: Addison-Wesley.
13. Cooper, R. G. and Fürst, P. (Dec. 2023). Agile Development in Manufacturing Companies: Best Practices and Pitfalls. *IEEE Engineering Management Review* 51 (4): 65–76. https://doi.org/10.1109/EMR.2023.3304792
15. Cooper, R. G. and Sommer, A. F. (Mar-Apr 2018). Agile-Stage-Gate for Manufacturers—Changing the Way New Products Are Developed. Research-Technology Management (61) 2: 17–26. https://doi.org/10.1080/08956308.2018.1421380
16. GAO (July 27, 2023), Report to Congressional Committees: LEADING PRACTICES – Iterative Cycles Enable Rapid Delivery of Complex, Innovative Products. U.S. Government Accountability Office, GAO-23- 106222, page 8. https://www.gao.gov/products/gao-23-106222
17. Cooper, R.G. (2023). New Products—What Separates the Winners from the Losers and What Drives Success. In: The PDMA Handbook of Innovation and New Product Development, 4th ed., edited by Bstieler, L. and Noble, C.H. Chapter 1: Hoboken, NJ: Wiley. The PDMA Handbook of Innovation and New Product Development: Bstieler, Ludwig, Noble, Charles H.: 9781119890218: Amazon.com: Books
18. Cooper, R.G. (March 2021). Accelerating Innovation: Lessons from the Pandemic, Journal of Product Innovation Management (38) 2: 1–11. Available at (open access): National Library of Medicine, National Institute of Health (NIH): https://pmc.ncbi.nlm.nih.gov/articles/PMC8014561/
19. GAO (May 2020). COVID‑19 Vaccine Development. U.S. Government Accountability Office. https://www.gao.gov/assets/710/707152.pdf
20. Cooper, R. G. (February 2024). The Artificial Intelligence Revolution in New-Product Development. IEEE Engineering Management Review (52) 1: 195–211. https://doi: 10.1109/EMR.2023.3336834. Link: The Artificial Intelligence Revolution in New-Product Development | IEEE Journals & Magazine | IEEE+ Xplore
21. Cooper, R.G. and McCausland, T. (January 15, 2024). AI and New Product Development. ResearchTechnology Management (67) 1: 70-75. https://doi.org/10.1080/08956308.2024.2280485
22. Cooper, R.G. (2025). The NPD Game Is Won or Lost in the First Five Plays: How AI Can Help in Product Innovation. IEEE Engineering Management Review. https://doi:10.1109/EMR.2025.3540373
23. Cooper, R.G. (May 2024). The AI Transformation of Product Innovation. Industrial Marketing Management 119: 62–74. https://doi.org/10.1016/j.indmarman.2024.03.008
24. Cooper, R. G. (January 3, 2025). What Is AI and What Can It Do in NPD for You and Your Business? PDMA kHUB. https://community.pdma.org/knowledgehub/bok/product-innovation-process/what-is-ai-and-what-can-it-do-in-npd-for-you-and-your-business
25. McKinsey & Co. (March 12, 2025). The State of AI: How Organizations Are Rewiring to Capture Value. QuantumBlack AI. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-how-organizations-are-rewiring-to-capture-value
26. Cooper, R.G. and Brem, A.M. (2025). Insights for Managers About AI Adoption in New Product Development. Research Technology Management (67) 6: 39–46. https://doi.org/10.1080/ 08956308.2024.2418734
27. IBM (2025). What is Agentic AI? IBM Think. https://www.ibm.com/think/topics/agentic-ai 28. Mantia, L., Chatterjee, S., Lee, V.S. (Oct. 24, 2025). Designing a Successful Agentic AI System, Harvard Business Review. https://hbr.org/2025/10/designing-a-successful-agentic-ai-system
29. Cooper, R. G. (December 10, 2025). Stage-Gate Agentic: The Coming Revolution in the New Product Process. PDMA KHUB 2.0 online journal: https://community.pdma.org/knowledgehub/bok/productinnovation-process/stage-gate-agentic-the-coming-revolution-in-the-new-product-process
30. Microsoft (January 2, 2025). RD-Agent: An Open-Source Solution for Smarter R&D. Microsoft Research Lab – Asia. https://www.microsoft.com/en-us/research/articles/rd-agent-an-open-source-solution-for-smarter-rd/?msockid=100a44ecd30b692114d75230d2f668a5
31. Jira. (2026). Project Dashboard: Track Projects and Key Metrics. Atlassian blog. https://www.atlassian.com/software/jira/features/project-dashboard
32. Cooper, R.G. and Sommer, A.F. (2023) Dynamic Portfolio Management for New Product Development. Research-Technology Management (66) 3: 19 –31. https://doi.org/10.1080/08956308.2023.2183004
33. Cooper, R.G. (February 28, 2025). AI-PRISM: A New Lens for Predicting New Product Success. PDMA kHUB 2.0 online journal: https://community.pdma.org/knowledgehub/bok/product-innovation-process/ai-prism-a-new-lens-for-predicting-new-product-success
34. Cooper, R.G. (April 2025). Adopting AI for NPD: A Strategic Roadmap for Managers. ResearchTechnology Management (68) 3: 41–46. https://doi.org/10.1080/08956308.2025.2466980
35. Cooper, R.G. (June 2024). Why AI Projects Fail: Lessons from New Product Development. IEEE Engineering Management Review 52 (4): 15–21. https://doi:10.1109/EMR.2024.3419268 https://ieeexplore.ieee.org/document/10572277
36. Gidwani, S. and Bhattarai, M. (July 10, 2025) Measuring AI Outcomes for Business Success: A 7-Step Stage Gating Framework. IBM website: 7-step stage gating framework for measuring AI outcomes | IBM
37. Fonstad, N.O., Mocker, M., and Salone, J. (Oct. 6, 2025). How to Drive Digital Innovation Without Wasting Resources. Harvard Business Review, https://hbr.org/2025/10/how-to-drive-digital-innovation-without-wasting-resources
38. Cooper, R.G. (2025). Adopting AI for NPD: A Strategic Roadmap for Managers. Research-Technology Management 68:3, 41–46. https://doi.org/10.1080/08956308.2025.2466980
39. Cooper, R.G. (Sept. 2025). A strategy for Adopting AI in NPD. PDMA kHUB 2.0. https://community.pdma.org/knowledgehub/bok/strategy/a-strategy-for-adopting-ai-in-npd-article
40. Cooper, R.G. (October 2025). Eco-Stage-Gate: Building Sustainability into Product Innovation.” IEEE Engineering Management Review 53 (5): 25–32. https://doi.org/10.1109/EMR.2024.3493492
41. Cooper, R.G. (Nov-Dec 2006). Managing Technology Development Projects –Different than Traditional Development Projects. Research-Technology Management 49 (6): 23 –31.
42. Cohen, L.Y., Kamienski, P.W. and Espino, R.L. (1998). Gate System Focuses Industrial Basic Research. Research-Technology Management, 41 (4): 34–37
This article is adapted from the original Jan 30, 2026 article “Accelerated New Product Development: Lessons for Leaders from ‘China Speed’” by Dr. Robert G. Cooper. All rights to the original content remain with the respective copyright holders.
