AI in Product Development: How It’s Transforming the Future of Innovation

AI in product development is a big player in shaping how these products are designed and built; thus, the other processes are being streamlined, and quality is being improved with faster innovations. It opens Automation over routine jobs like testing, code generation, and defect detection, resulting in more rapid development and lower costs.

It can also be employed for project management purposes, for instance, by optimizing schedules, assessing risk, and allocating resources. AI in product development also improves product excellence by mining insight into customers from massive datasets for personalized features and smarter design decisions. On the other hand, generative AI can be a medium for great creativity by brainstorming design alternatives and new concepts, and an AI vision system can ensure a great product by defect detection on the shop floor in real time.

This translates into competition being won through a faster marketing adaptation, decision-driven teams, and data. Most certainly, AI is not taking over human creativity; it is there to complement it. By eliminating the rigors of manual effort, product teams can now really focus on empathy, strategy, and innovation. Human involvement is ensured through the human-in-the-loop paradigm, where AI is seen as a cognitive collaborator that enhances human judgment for user-centric design outcomes.

In essence, artificial intelligence product development services unfolds an entire life cycle transformation, enabling teams to create smarter, faster, and more impactful products.

What is AI in Product Development?

AI-enabled product development is giving a new face to the Chinese way of building, testing, and releasing products with respect toward China in the background. From automating tasks and generating insights to personalizing features, AI provides product teams with the ability to develop better and speedier solutions that fit perfectly in the customer’s life at every stage of the life cycle.

AI-Powered Automation

Among the other benefits AI-assisted systems offer in product development, Automation of repetitive tasks that consume a great deal of time stands out prominently. One may consider data analysis, execution of test scripts, and generation of boilerplate code as AI-assisted systems performing these mundane tasks for developers and designers. Such a process provides faster development and reduces human mistakes so that product teams spend more time on creative and strategic aspects of product development. The modern product pipeline is booming with tools like AI-assisted code generation and auto-test frameworks.

Data-Driven Insights

AI in product development plays a vital role in transforming raw data into actionable insights. Whether customer feedback, market trends, or usage pattern analyses, AI algorithms enable product managers to make evidence-based decisions about their products. Such insights form the basis of product design, feature prioritization, and market positioning so that a new release is aligned with customer needs and market demands. Those companies integrating AI at this level often figure out validations quicker and respond with a greater fit for the product market.

Enhanced Decision-Making

Innovative solutions directed by AI can be designed and deployed throughout the product life cycle to assess design trade-offs, spot emerging risks, and suggest pathways to take with design and development to reduce the level of uncertainty. It can also be used during the early design stages to measure the trade-offs between cost, usability, and performance; in later stages, it predicts testing or deployment constraints that might be encountered. This shall result in better-capitalized resources with a fast time to market.

Let’s Build Your Product from Start to Success

Schedule Call →

Accelerated Prototyping Testing

If your market is competitive, speed matters, and AI in product development really delivers when it comes to speeding up prototyping and testing. AI tools simulate product behavior in different conditions, predict possible points of failure, and automate quality assurance. This dramatically shortens the loop from design, build, and test. With AI-based simulations, you can find problems much earlier than with creating a physical prototype, so you save time and money. This pattern has shown significant efficiency gains in development in sectors such as automotive and aerospace.

Personalization of Product Features and Experiences

AI analyzes people’s behaviors to personalize the features, interfaces, and recommendations it offers. For digital products, that means a dynamic user experience tailored to individual needs and preferences. Be it an adaptive learning platform or a smart home DSC, AI in product development helps the DSC learn from the users and evolve. This evolution then further engages the users, so they are more satisfied and willing to stay with the product for the long run.

Real-World Examples

Several companies are already using AI-driven techniques to improve their products. Generative AI tools help designers explore multiple design variations, while AI-powered testing tools automate performance and security assessments. Predictive maintenance, powered by AI, allows physical products to self-monitor and proactively schedule service, improving reliability and customer trust. These use cases highlight how AI in product development is no longer a futuristic concept, but a present-day business imperative.

How AI in Product Development Is Revolutionizing Innovation

The product development procedure finds itself revolutionized mainly by AI, which automates tasks, optimizes processes, and drives big data analytics. From conception to launch, AI quickens timelines, develops the product, and creates fresh avenues in every stage of the development project. As organizations compete to get a more intelligent and user-centered solution into the market, AI in product development is fast becoming a strategic weapon for competitive advantage.

Let us consider a closer view of AI in product development across design, prototyping, process optimization, testing, decision-making, and innovation.

1. Enhanced Design and Prototyping

The design and prototyping stage is perhaps where AI finds the application with the most significant impact on product development. Design workflows traditionally are heavily manual processes with much iterative feedback, while AI makes this step more intelligent and automated.

Design Generating: Generative AI tools would be able to automatically generate hundreds of possible design variations based on specific performance targets, material constraints, and operating conditions. An engineer or designer would define inputs in terms of size, weight, strength, or thermal resistance, and the AI algorithm would rapidly explore the best possible configurations. Hence, it saves time while bringing in new and unexpected designs that a human team may not have envisioned.
Hyper-Personalized Designs: Another aspect of AI in product development is hyper-personalization. An AI system that studies user data, behavior, preferences, and even biometrics can customize a design; industries such as consumer electronics, fashion, and healthcare are now shaping this capability of embedding personalized experiences into the fabric of the product itself.
Rapid Prototyping: Automated prototyping is another significant contribution of AI in product development. Groomed by its AI-powered tools, it can take 3D modeling and simulation with little human input through iterative processes of testing and refining digital performance until ready for real prototyping, further reducing time to market and prototyping costs.

2. Optimized Development Processes

While AI brings intelligence and speed to the entire development lifecycle, it can also optimize coding, testing, debugging, and maintenance through Automation and advanced analytics.

Automating Repetitive Tasks: AI takes over mundane activities that could go on for hours through standard typing of code snippets, bug detection, or generation of documentation processes. Doing so increases output while ensuring a decrease in human error. Developers will thus be busy resolving more complex architectural challenges and innovating new features rather than wasting time writing trivial boilerplate code or conducting redundant testing.
Enhancing Testing: Testing traditional software and hardware consumes far too many resources. AI enhances this by stimulating test-case generation, carrying out simulations, and analyzing its own studies for any concerns. When AI is launched in product development, it ensures that defects are detected very early so that expensive fixes can be avoided late in the cycle. By speeding up QA and enabling continuous testing, AI further makes the final product more robust and reliable.
Predictive Maintenance: Predictive maintenance is another important software product of AI for physical products. By continuously analyzing sensor data and system performance, AI predicts equipment failure and recommends optimal maintenance schedules. This minimizes unplanned downtimes, increases safety, and extends product life.

3. Data-Driven Insights and Decision-Making

Another game-changing benefit of AI in product development is the ability to turn massive volumes of raw data into actionable insights. These insights support better decision-making throughout the product lifecycle.

Market Trend Analysis: AI tools are capable of scanning market data, competitor strategies, news feeds, and social media to predict and establish emerging trends. This allows companies to foresee customer demand shifting, forecast what products would be in demand, and make their roadmaps align with market expectations of the future.
Customer Behavior Analysis: One of the most essential things in product development is to understand users. Therefore, AI needs to be able to analyze customer feedback, app usage, support tickets, and online behaviors to reveal patterns and areas in which users have unmet needs. AI in product development allows companies to focus more on features that users respond most strongly to and that drive adoption and satisfaction.
Resource Allocation Optimization: AI-based solutions at an internal level may also help in internal decision-making from the perspective of risk prediction for projects, attending estimation, and the best allocation of resources in competing teams. This leads to smooth operations, less delay, and more cost-efficient execution.

4. Streamlined Project Management

Project management had to be efficient to ensure product success; AI, therefore, ensured new-class efficiency and precision in laying down projects, trajectory maintenance, and changes required at any moment in real-time.

Automated Scheduling and Risk Assessment: Here, AI in product development tools is meant to automatically generate development schedules depending on past project data on team availability and task complexity. They can forecast probable delay or risk, depending on ongoing performance metrics, and suggest minimizing ones. This makes project planning very dynamic and responsive.
Better Decision-Making: AI will provide optimal recommendations backed with real-time data among design alternatives or between the allocation of engineering hours. Product managers and leaders will not rely on their guts to make informed decisions; instead, they will weigh the odds of success or failure and, hence, depart with better product outcomes.
Optimizing Agile Workflows: AI in product development allows agile teams to optimize workflows better by detecting impediments, tracking velocity, and prioritizing backlogs. This results in higher transparency, shorter iterations, and deliveries occurring almost predictably.

5. Increased Innovation

Perhaps AI’s most exciting impact on product development is its ability to feed innovation. By removing bottlenecks and promoting new ways of thinking, AI serves as a real rocket launcher for new product ideas.

Generating New Ideas: AI models can scan current product databases, patent information, and trend reports to recommend whole new product categories or use cases that have yet to be explored. Teams can also use AI-powered brainstorming tools to generate a larger list of wild ideas at lightning speed.
Faster Iteration Cycles: With AI in product development, product teams are able to rapidly go through various ideation-prototyping-testing cycles. The increased experimental capacity due to the ability to simulate outcomes, virtually test scenarios, and gather customer feedback in real-time increases the possibilities for breakthrough ideas to be translated into production.
Competitive Benchmarking: AI in product development allows for continual benchmarking of an organization’s offerings against those of the competition. By grasping competitor feature sets and pricing and also understanding user sentiment, AI tools enable good roadmap decisions and differentiation of products in the market.

Real-World Applications and Examples

Many leading companies have already adopted AI in product development to maintain the benefit of being ahead in their respective fields. For instance:

Tesla uses AI for vehicle design optimization and over-the-air updates that incorporate user behavior changes.
Nike intends to use AI-based personalization so that customers can create their shoe designs using a foot scan.
Philips employs AI to facilitate the design and predictive maintenance of their healthcare devices.
Autodesk uses generative design algorithms to help engineers develop lighter and stronger components for the aerospace and automotive industries.

These examples exemplify how much AI can help product development, making it not only current but also evolving over time to accommodate changing consumer needs.

6. Intelligent Risk Management

In product development, AI assists with anticipatory risk management by analyzing real-time performance data and historical patterns to identify possible failure points. Usually, AI can predict where problems will likely occur so that teams may intervene early and mitigate those problems even during the early phases of the product life cycle. This lessens the opportunity for expensive errors, enhances product reliability, and smoothens product launches.

Risk Forecasting: AI, using machine learning models, can identify anomalies and assess project health with respect to the likelihood of delays or failures. It provides early warnings on key risks, whether technical, financial, or operational.
Continuous Monitoring: AI-based systems will continuously monitor system performance and product usage metrics, thus identifying any change or deviation from existing trends. This will allow organizations to react accordingly and try to realign their strategy to mitigate those evolving risks.

7. Smart Supply Chain Integration

Artificial intelligence in product development particularly bridges the gap in the design and testing phases, enhancing the supply chain. There is a smooth flow of components and materials during development.

Demand Forecasting: AI analyzes historical sales information, market trends, and consumer behavior to forecast demand accurately. Such plans are useful in production scheduling and procurement, avoiding product wastage or shortages.
Inventory and Logistics Optimization: AI tools use data on supply and delivery systems in real-time to optimize inventory levels and recommend reorder points. This ensures that materials are delivered just in time and production schedules are not delayed unnecessarily.

Start Your Product Development Journey

Schedule Call →

The Evolution: From Manual to Intelligent Product Development

Over the centuries, product development has undergone a huge transformation. Nowadays, it has gone from a very manual, experience-oriented process to an intelligent, data-oriented lifecycle. The introduction of digital tools was a watershed moment. Still, now AI-powered product development is actually transforming the whole scene by fast-tracking cycles, reducing errors, and enabling hyper-personalized solutions. This chapter takes you through the key evolutionary stages while signifying how the birth of an era in innovation begins with AI in product development.

From Manual to Digital

In the manual era, product development heavily relied on human intuition, limited market surveys, and paper-based design cycles. Processes were time-consuming and prone to inefficiencies, with long feedback loops and minimal data integration.

The digital transformation in the late 20th century introduced tools like CAD software, ERPs, and cloud computing platforms. This shift brought better documentation, streamlined project management, and increased visibility across the product lifecycle. Yet, it still demanded significant human oversight and could not keep up with growing complexity or market volatility.

The Rise of AI in Product Development

Today, AI in product development is a game-changer. With intelligent automation, real-time analytics, and machine learning models, companies can forecast demand, simulate performance, personalize user experiences, and predict design failures, all before a product hits the market. AI reduces human error, accelerates time-to-market, and enables data-driven decisions at every stage, from ideation to launch.

Here’s a comparative view of traditional vs. AI in product development:

Aspect	Traditional Product Development	AI Product Development
Process Speed	Slow, linear cycles	Accelerated with parallel, iterative improvements
Data Usage	Limited and siloed	Centralized, real-time data from diverse sources
Design & Testing	Manual prototyping, physical testing	Predictive modeling, virtual simulations
Customer Insights	Surveys, feedback loops	Real-time behavior tracking, AI-powered personalization
Decision Making	Human intuition-based	Data-driven, machine-assisted decision-making
Scalability	Time-intensive scaling, requires manual input	Automated scaling and adaptation across markets
Risk Management	Reactive and post-launch fixes	Proactive risk identification using AI models

Why This Shift Matters

It is not merely the furtherance of Automation that represents a giant leap toward AI in product development; intelligence goes beyond that. While AI equips teams to sharpen precision from guesswork, it also helps companies convert reactive development towards proactive innovation. For example, rather than waiting for user complaints after the launch, AI models may detect customer dissatisfaction from early behavioral signals and suggest changes immediately.

On the other hand, AI-driven systems let businesses make mass personalization, customizing features, interfaces, and user journeys for customer segments or even individual users. That much personalization was once considered impossible from a scaling perspective, but it is now feasible and profitable.

How to Use AI to Create New Products?

Companies have metamorphosed in how they create and manufacture products using artificial intelligence. However, in product development, the efficient application of AI demands a careful implementation strategy, which stages in phases. Below are three actionable steps that you can use to get started:

Step 1: Identify high-effort, low-value tasks

The first step is to identify those repetitive and time-consuming steps in your product development that add little to nothing from a strategic perspective. Such processes could include data entry, manual testing, sorting through customer feedback, and even elementary prototyping. Automate those aspects with AI applications such as NLP for sentiment analysis or generative design software, thereby freeing the product teams to undertake more creative, high-impact work. This is probably the best way to initiate AI applications into product development without an overwhelming restructuring of workflows.

Step 2: Start small and scale strategically

Rather than embracing AI throughout your entire product lifecycle, small pilots should be run. For instance, deploying AI prototyping tools or AI-based user behavior prediction models only during one phase can be tried. Measure the results, learn what worked, and evolve. With confidence built and ROI established, AI can be moved incrementally into R&D, design, and testing. This incremental adoption keeps things running smoothly and helps build internal AI maturity.

Step 3: Use AI to generate dictate

This way, AI can help generate design ideas, analyze market trends, or simulate product performance. Human input must complement AI output. Product innovation becomes smarter and more customer-centric when AI supports your vision rather than decides on it.

Using these three steps, you will be able to maximize the use of AI in product development with confidence and strategic intent.

How is AI Used in Product Design?

Artificial Intelligence is reshaping product design by stimulating creativity, accelerating workflows, and ensuring better user-centered results. The most substantial applications arise from generative AI: Designers no longer have to manually iterate but can take advantage of machine intelligence for rapid exploration and testing.

1. Generative Design

Depending on established parameters such as dimensions, materials, performance goals, or aesthetic preferences, AI algorithms can generate hundreds of design options. Designers will then be allowed to explore a much wider design space than traditional methods permit, thus saving hours while opening the door to innovation.

2. Wireframe and Layout Suggestions

Generative AI tools can help designers generate wireframes and UI layouts automatically based on the intended audience or use case. Thus, early-phase design becomes increasingly fast while repetitive tasks decrease in terms of distractions.

3. Rapid Prototyping

AI tools can create high-fidelity prototypes from the simplest sketches or text descriptions. These prototypes simulate user flows and interactions, thus facilitating easy validation of a concept before much money is sunk into its development.

4. User Behavior Analysis

AI can take user behavior data (clicks, scrolls, dwell time) and recommend design changes or personalization to the user interface in real time, thus crafting intuitive and practical experiences.

5. Aesthetic and Accessibility Optimization

AI systems can enforce rules for visual design (e.g., typography, color schemes) and recommend accessibility improvements based on WCAG guidelines, thus helping teams design more accessible products.

6. Predictive feedback

Before the product launches, AI predicts how the user might interact with a design. Therein lies the value of the feedback—it enables teams to preemptively address issues on layouts and flows that will cause usability problems.

How AI Tools Are Enhancing Every Stage of Product Development

AI-powered tools open various possibilities for product developers and increase the value at each stage of the product life cycle, from conception to the post-launch phase and maintenance. They could handle the most common tasks, derive insights from massive sets of data, and greatly simplify complicated workflows. Thus, what we get at the end of the chain is more efficiency, shorter time to market, and higher quality of the product.

The following section breaks down in more detail how AI tools for developers and product teams are changing at each stage of the product development process.

1. Ideation and Conceptualization: Turning Ideas into Opportunities

AI-Powered Brainstorming

Brainstorming, creative thinking, and aligning with market needs usually happen at the earliest stages of product development. AI tools like Miro Assist and ChatGPT now accompany human participants during such sessions.

For instance, ChatGPT can propose feature recommendations given customer personas, create user story templates, or help create basic PRDs. Miro Assist can support teams in visually laying out ideas and suggesting improvements to existing ones or simulating varying options as they relate to business objectives.

Competitive Analysis and Trend Discovery

AI really shines in processing vast amounts of information at high speed. An AI-powered tool will examine the competitors’ offerings, check for market trends, and identify a few gaps where your product could fit.

Machine learning-powered platforms analyze thousands of product listings, reviews, social media signals, and industry reports to identify burgeoning demands, allowing product teams to make more intelligent decisions before investing in building something.

Applying AI in product development at this stage helps eliminate the guessing factor and makes sure that things are aligned with market reality.

2. Design and Prototyping: Visualizing and Validating Ideas Early

AI-Assisted Design

Prototyping has always been an exhausting yet costly procedure by itself. This is where AI tools are significantly changing the design field by offering a bridge between ideas and wireframes or low-fidelity prototypes with just a few clicks.

These tools suggest design elements based on best practices, automatically align UI components, and change layout sizes for different screens. With all this, they are magic tools; until now, they were impossible to imagine because they convert the distance between an idea and its visualization into seconds, opening new roads to validate and iteratively craft design concepts.

AI-Powered User Feedback Analysis

Tools like Kraftful might ingest more than 100 sources of feedback, such as app reviews, surveys, or customer support channels. Using NLP, compared with similar terms and concepts from other customer feedback, these platforms spot common pain points, detect emotional tones, and identify feature requests.

This allows product teams to obtain structured insights that can immediately be acted upon to refine design decisions instead of going through hundreds of feedback entries. This ultimately leads to a more user-focused product.

With this kind of AI for product design, it loops to tightly link customer voice and product experience.

3. Development and Manufacturing: Speeding Up Production with Smart Tools

AI-Coding Assistants

In contemporary times, developers have turned toward intelligent code suggestions for speed and perhaps also consistency. Tools such as Tabnine or GitHub Copilot analyze existing code, documentation, and developer patterns and suggest context-aware code completions in return.

They reduce the frequency of errors, induct users into best practices, and sometimes spot reprehensible bugs even before running the program. Such tools decrease time-to-market even more and stabilize the software landscape, an environment especially favorable to agile methodologies.

This scenario stresses that these AI Developer Tools will help in the productive and effective running of any real-life project.

AI-Driven Testing and Quality Assurance

Testing can act as a bottleneck in product schedules, especially when confronted with complex use case paths or platform variations. Functionize and other such platforms bring AI into the QA life cycle by automatically generating test scripts, running predictive tests, and identifying flaky test cases.

Forsaking the tedious acts of writing long test cases, QA teams can now ask their AI counterparts to simulate user journeys and point out major issues. This translates to better test coverage, faster releases, and fewer bugs.

4. Maintenance and Lifecycle Management: Staying Ahead of Problems

Bug Detection and Resolution AI

Bug detection after product launch is a time-critical activity before end-users catch them. It is a monitoring tool powered by AI monitors that generate and analyze log files and track user sessions up to error rates in real-time.

Some platforms even go beyond and begin offering suggestions for probable root causes based on past big data and programmer behavior. This kind of veritable and proactive bug detection reduces downtime, paving the way for a greater user experience.

Predictive Maintenance AI

In hardware and IoT product development worlds, it is a game-changer for predictive maintenance. AI algorithms process the data garnered for system performance to detect wear and tear or anomalies that warrant failure.

For example, an AI model might observe temperature fluctuations or sudden abnormal usage patterns and infer that a component will fail within two weeks. This enables teams to schedule maintenance during times that have the least impact, thereby increasing reliability and reducing operating costs.

This is becoming a significant application of AI in product development, especially in manufacturing and industrial settings.

5. General Productivity and Collaboration: Keeping Teams Aligned

AI Meeting Transcription and Summarization

Communication is at the heart of product development. AI-powered tools like Otter.ai, Fireflies, and Notta capture meeting transcripts, identify key action items and provide a summary of the discussions.

This is especially helpful for remote and hybrid teams, as it ensures that they remain on the same page without a few hours of alignment calls. It also helps maintain records of design decisions, project updates, and roadmap changes.

AI for Project Management

Many modern project management tools, such as ClickUp, Motion, and Monday.com, now include AI capabilities, enabling them to assign tasks based on priorities and workload and predict a timeline lag.

Project managers can decide on resource allocation based on these indicators and further work to ensure that the project is delivered on time. Further, these tools use machine learning to analyze communication patterns within teams and offer suggestions to increase collaboration.

Along those lines, AI in product development not only stays within technical aspects but also promotes the operational backbone of the teams.

6. AI for Data Analysis and Decision-Making

Gone are the days when manufacturers made decisions simply on intuition; today, product teams usually hope for some user data on which to base their decisions. On the other hand, analytics tools, assisted by AI, like Amplitude, Mixpanel, or Julius AI, assist by monitoring user behavior, segmenting audiences, and revealing unexplored patterns.

This software includes dashboards and predictive models that show features that truly work, where users usually drop, and how various cohorts react. This gives teams the capacity to iterate quickly and with the utmost confidence.

The combination of making these decisions through these tools ensures your product roadmap never strays away from data and user expectations.

7. AI for Documentation and Knowledge Sharing

Writing documentation can sometimes be a very tedious task, along with release notes and product updates. Tools like ChatGPT try to draft text from specifications, meeting minutes, and task updates.

Thus, relatively less time should be given to side issues and to ensure consistency in communication among product teams, stakeholders, and end-users.

Conclusion

AI in product development is reshaping how businesses dream up, create, and market new solutions. By automating mundane tasks, providing deeper insights through data, and setting the groundwork for customized user experiences, AI enables teams to hit the ground running, operate intelligently, and move swiftly with precision. AI is, in fact, viable for generative design, rapid prototyping, predictive testing, and agile project management, thus making it possible to perfect every phase of the development lifecycle.

Contrary to supplanting human creativity, it sneaks in more of it, where product teams are liberated from tedious tasks to concentrate on innovation and strategy. Companies that keep it simple from the beginning opt for high-effort tasks toward automation and provide a thoughtful escalation plan can get faster into the market and yield better-quality products made in deeper alignment with customers.

As markets evolve and competition intensifies, staying ahead is no longer a choice-it is a decision. Implementing AI in the realm of product development is definitely more about being the pioneer for building smarter, user-centric products that set the stage for the future rather than being a follower.

If you are looking for AI in product development, you can visit us here.

Frequently Asked Questions

Q1. What is the role of AI in product development?

By analyzing vast quantities of data coming from social networks, customer review platforms, and domain-specific magazines, AI discovers market gaps and impacts important realized insights into consumer behavior and preferences. During design, AI supports scenario-quick prototyping and iterative improvements so that the team can refine concepts very well.

Q2. How is AI used in product design?

Generative AI is an interesting tool that product designers use to create specific design elements, such as layouts, wireframes, interactive prototypes, or complete design concepts. This helps speed up ideation, inject some creativity, and smooths out the entire design process.

Q3. Will AI replace UI/UX?

UX designers, writers, strategists, and UI artists will most likely never face being superseded by AI. AI forces tasks upon us when assisting, but it misses the emotional intelligence, creativity, and contextual knowledge that human professionals consider. Technologies not yet able to match it stand in awe before human ingenuity because the brain contains about 86 billion neurons (The Proceedings of the National Academy of Sciences). Therefore, AI might assist human creativity, but no machine can replace human insight.

Q4. How is AI used in R&D?

With generative AI in R&D, research and development are being changed by analyzing massive datasets, forecasting experiment results, and proposing novel hypotheses. This is accelerating the pace of discovery and giving the entire science a new orientation. This transformation is pushing the limits of R&D teams and revolutionizing the innovation process.