Survey: Manufacturers Embrace AI Tools to Speed Product Innovation
GenAI tools are slated for further investment in the next two years to help accelerate design and engineering processes.
KEY TAKEAWAYS:
- 71% said GenAI tools are key in generating new product ideas.
- 90% expect GenAI tools to accelerate their design and engineering process.
- But 56% said their design and manufacturing processes are only “somewhat integrated”.
Artificial intelligence tools, particularly those of the GenAI variety, are making significant inroads in the new product innovation process as manufacturers seek to accelerate engineering and design to bring innovations to market faster.
At the same time, many manufacturers still face hurdles in better integrating their design and manufacturing processes. And they are also challenged by older, legacy design systems that are hindering their quests for greater speed in getting new products in the hands of customers.
These are some of the key findings of the Manufacturing Leadership Council’s first ever survey on New Product and Business Model Innovation, a subject that was adopted as part of the Council’s Critical Issues agenda two years ago. The Critical Issues agenda outlines a set of technological, organizational and leadership competencies that manufacturers must master to successfully implement digital transformation.
Discovering New Ideas
In the new survey, 71% of respondents said that generative AI ideation tools are the most important digital technologies helping to generate and capture new product or new business model ideas. This finding is far ahead of the second place finding, customer and market data analytics platforms, which was checked off by almost 59% of respondents (Chart 1).
The widespread use of GenAI tools in the ideation process, just five years after the introduction of Open AI’s ChatGPT, underscores how quickly this technology is being adopted and used to increase speed and productivity. The effective use of these AI tools has clearly become a core competency for manufacturers to remain competitive.
As a result, manufacturers are prioritizing their investment decisions in favor of these technologies. Asked about their top three investment areas over the next two years, GenAI-based ideation, customer insight, and concept validation platforms; AI and analytics; and data infrastructure and integration topped the list (Chart 2).
The reason and expected outcome for this emphasis on AI tools is simple and straightforward – speed. Manufacturers want to shorten the ideation, concept validation, development and manufacturing steps in the innovation process to bring new products to market faster and reap the financial rewards of doing so. When asked to predict the impact of AI tools over the next five years, nearly all respondents – 90% — said they expected to accelerate their design and engineering processes. At the same time, they are expecting to reduce costs and generate revenue faster using these tools (Charts 3, 4).
SECTION 1: IMPACT OF DIGITAL TECHNOLOGIES
1 GenAI Tools Lead in Discovering New Ideas
Q: Which digital technologies does your organization currently use to generate and capture new product or business model ideas? (select all that apply)
2 AI, Infrastructure Lead Investment Intentions
Q: Which areas of your new product and business model innovation efforts will receive the greatest investment over the next two years? (select top three)
3 AI Expected to Accelerate Design, Manufacturing
Q: Looking ahead five years, where do you expect AI to have the greatest impact on your company’s product and business model innovation? (select all that apply)
4 Revenue, Cost Gains Linked to Digital Technologies
Q: What degree of impact has digital technology had on the following aspects of your new product and business model innovation efforts? (rate each on scale of Negative Impact/Neutral/Positive Impact)
The Front-End Process
But are aspirations ahead of the ability to execute on this vision of greater speed?
In assessing the new concept validation process, the survey revealed that a strong majority of respondents, 78%, still use direct customer interviews and focus groups to gather customer and market data. The second most prevalent method of gathering data, at 65% of the sample, is by using sales and CRM data. Third-party market research comes in third at 42%. Interestingly, social media and digital sentiment data are only being used by about 22% of respondents (Chart 5).
And when asked how they would rate their organization’s ability to capture, store and share customer insights and operational data in a searchable format, only 6.8% said they were “highly effective” in doing so.
Despite these factors, the good news is that nearly 63% of respondents report that it typically takes their organizations less than one year to move from initial idea to a validated, fundable concept. For nearly 29%, this process takes more than one year (Chart 7).
SECTION 2: DIGITAL FRONT-END INNOVATION
5 Traditional Methods Dominate Validation Process
Q: What sources of customer/market data does your organization use to validate new product concepts before committing to development? (select all that apply)
6 Technical Management of Customer Insights a Work in Progress
Q: How would you rate your organization’s ability to capture, store and share customer insights and operational data in a searchable format to inform new product and business model decisions? (select one)
7 Idea Maturation Takes More Than One Year for Many
Q: How long does it typically take your organization to move from initial idea to a validated, fundable concept? (select one)
Probing the Engineering Model
The most prevalent model for the product design process is a mixed bag of agile and traditional stage-gate approaches, according to the survey, with 46% reporting this mix and nearly 39% saying they are primarily using just traditional stage-gate processes.
A majority, 55%, say they are using digital model-based tools such as digital twins, simulation, and virtual prototyping for select product lines or projects. But another 18% say they plan to use these technologies within two years (Charts 8,9).
Model-based engineering coupled with the use of these digital tools has had a significant, positive impact in reducing design cycle time, reducing physical prototyping costs, and improving design quality. But it has far less effect in enabling mass customization, the survey shows (Chart 10).
Where there is clear room for improvement is in the handoff from design and engineering to manufacturing. When asked how well integrated these processes are today to ensure manufacturability, a majority, 56%, said their processes were “somewhat integrated”. Only 5.9% said they were fully integrated (Chart 11).
SECTION 3: AGILE, MODEL-BASED ENGINEERING
8 Use of Model-Based Tools is Widespread
Q: Does your organization use model-based engineering (e.g., digital twins, simulation, virtual prototyping) in product design? (select one)
9 Product Design Process is a Mix of Traditional/Agile Stage-Gate
Q: How would you describe your organization’s product design process today? (select all that apply)
10 Model-Based Engineering Has Positive Impact on Design Process
Q: What sort of impact has model-based/digital engineering had on the following aspects of your product design process? (rate each on scale of Negative Impact/Neutral/Positive Impact)
11 The Design/Manufacturing Process is ‘Somewhat Integrated’
Q: How well integrated is your product design/engineering function with manufacturing operations to ensure manufacturability from the earliest design stages? (select one)
Technology-Enabled Commercialization
Even though design to manufacturing integration is a work in progress in manufacturing companies, respondents report that the process for ensuring that new products can be reliably produced at scale is solid. Approximately 64% of the survey takers said this process is either highly mature or moderately mature (Chart 12).
In addition, more than 60% said their supply chains are well aligned with their new product introduction process. And about half of all new product introductions experience only minor delays due to manufacturing or supply chain readiness issues compared with the planned launch schedule (Charts 13,14).
SECTION 4: TECHNOLOGY-ENABLED COMMERCIALIZATION
12 Strong Majority Says Manufacturing Readiness Process is Mature
Q: How would you rate the maturity of your organization’s manufacturing readiness process (i.e., ensuring new products can be reliably produced at scale)? (select one)
13 Supply Chains Are Well Aligned With Product Process
Q: How well aligned is your supply chain with your new product introduction process? (select one)
14 Half of New Product Introductions Experience Minor Delays
Q: What percentage of your new product introductions experience delays of more than 10% beyond the planned launch schedule due to manufacturing or supply chain readiness issues? (select one)
Challenges and Opportunities
Manufacturers appear to understand what’s needed to close the gap between aspirations and execution, a situation and challenge that always exists when new technologies are implemented and operated. And the key factors tend to be predicable and usual.
In the new product innovation process, survey respondents’ top three challenges were legacy engineering and design systems, workforce talent and skills, cultural resistance to change, and the persistent problem of organizational siloes (Chart 15).
When it comes to the digitalization and use of technologies such as AI tools in the new product design and engineering process, manufacturers are looking for improvements and gains in time to market, cost, revenue, and quality. These can be thought of as standard metrics of progress.
But progress should also be measured by whether advanced technologies take the business to a new level of innovation. When asked to what extent their companies are embracing new business models such as product/equipment-as-a-service, outcome-based pricing or data monetization, manufacturers appear to be seeing opportunities.
While many are not currently exploring these new business models yet, a noticeable and growing percentage are exploring, piloting or actively implementing some of them. For example, more than one-fifth of respondents indicated that their companies are either actively implementing, piloting or evaluating product-as-a-service or equipment-as-a-service business models. And about 32% said they are going through these phases with outcome-based or performance-based product pricing (Chart 16). These findings bode well for a time when manufacturers cross the bridge from incremental improvements in traditional measures of performance to realizing digitalization’s full potential to reinvent their businesses based on new products and business models that create lasting competitive advantage into the future. M
SECTION 5: BUSINESS MODEL INNOVATION, IMPACT AND BENCHMARKING
15 Legacy Systems Constrain Innovation
Q: What are the most important challenges hindering your organization’s new product and business model innovation efforts? (select top three)
SECTION 6: CHALLENGES AND FUTURE OUTLOOK
16 A Minority is Embracing New Business Models
Q: To what extent is your organization embracing the following digitally enabled business models (e.g., product-as-a-service, outcome-based pricing, data monetization)? (select one option for each business model)
Author bio:
David R. Brousell is the Founder, Vice President and Executive Director, Manufacturing Leadership Council.
