How AI Can Revolutionise Your Company (But There's a Catch)

Companies are constantly seeking new ways to innovate quickly, efficiently, and productively. One question that frequently arises is whether deploying artificial intelligence (AI) can help drive innovation. Our research shows that while AI can be a powerful tool, its success depends largely on how it is implemented within an organisation.

Covered in this article

The Challenge of Innovation
Why Products Become Obsolete
How AI Can Help
Focus on Recombination
Applying AI to Innovation Processes
Conclusion
FAQs About AI For Your Business

The Challenge of Innovation

Innovation is a cornerstone of business growth and sustainability, yet it poses significant challenges that can be difficult to overcome. The landscape of corporate innovation has evolved dramatically over the years, with modern companies facing unique obstacles that their predecessors did not encounter. Understanding these challenges is crucial for any organisation striving to maintain its competitive edge and foster continuous improvement.

Decline in Traditional Innovation Metrics

One of the most striking indicators of the innovation challenge is the marked decline in traditional metrics of innovation, such as scientific paper production and patenting. Between 1945 and 2010, the production of scientific papers and patents fell dramatically, with scientific paper production dropping by 91 to 100% and patenting by 78 to 92%. This decline suggests that the traditional avenues of innovation are not as prolific as they once were, raising concerns about the future of technological and scientific advancement.

Complexity and Overlap of Knowledge

Despite the decline in traditional innovation metrics, the volume of scientific knowledge has grown exponentially. Fields have become more specialised, and the overlap between different domains of knowledge has increased. This complexity makes it challenging for companies to stay abreast of the latest developments and integrate them into their innovation processes. The sheer amount of information available can lead to analysis paralysis, where decision-makers are overwhelmed by the data at their disposal and struggle to identify actionable insights.

High Costs and Long Development Cycles

Innovation often requires substantial investments of time and resources. Developing new products, technologies, or processes can be a lengthy and costly endeavor, with no guarantee of success. Companies may spend years and significant capital on research and development (R&D) only to find that their innovations are quickly surpassed by competitors or fail to meet market needs. This high-risk, high-reward nature of innovation can be a deterrent, especially for companies with limited resources.

Rapid Market Changes

The pace of market evolution has accelerated, driven by technological advancements and changing consumer preferences. Products that once dominated the market can quickly become obsolete as new technologies and trends emerge. Companies must be agile and responsive to these changes to avoid falling behind. For example, the rise of electric vehicles has necessitated new standards and innovations in related industries, such as materials used in car manufacturing. Companies that fail to adapt to these shifts risk losing their competitive advantage.

Balancing Incremental and Radical Innovation

Companies must strike a balance between incremental and radical innovation. Incremental innovation involves making small, continuous improvements to existing products or processes, which can lead to steady progress and short-term gains. However, focusing solely on incremental innovation can prevent companies from achieving breakthrough advancements that drive significant long-term growth. Radical innovation, on the other hand, involves developing entirely new products or processes that can disrupt markets and create new opportunities. While radical innovation holds the potential for substantial rewards, it is also inherently riskier and more difficult to achieve.

Navigating the Deluge of Data

Companies have access to unprecedented amounts of data. While this data can provide valuable insights and drive informed decision-making, it also presents challenges in terms of data management and analysis. Organisations must develop the capabilities to sift through vast datasets, identify relevant information, and leverage it effectively. This requires advanced data analytics tools and skilled personnel who can interpret and apply the insights gained from data analysis.

Overcoming Organisational Silos

Innovation often requires cross-functional collaboration and the integration of diverse perspectives. However, organisational silos can impede this process by creating barriers between departments or teams. Silos can lead to fragmented efforts, duplication of work, and a lack of communication, all of which hinder innovation. Companies must foster a culture of collaboration and knowledge sharing to break down these silos and encourage innovative thinking across the organisation.

Why Products Become Obsolete

Products can become obsolete for several reasons:

1. Short Product Lifecycles: Breakthrough products often have brief periods of market dominance before competitors introduce similar offerings. For example, the drug Imatinib for Chronic Myeloid Leukemia was soon followed by other effective treatments, eroding its market share.

2. Changing Business Contexts: As market demands evolve, products that once met industry standards may no longer suffice. Cooper Standard, for instance, had to enhance its car sealants to meet the higher performance requirements of electric vehicles.

3. Explosion of Potential Combinations: In industries like tyre manufacturing, the sheer number of possible material combinations makes innovation complex and time-consuming. AI can help manage this complexity by predicting performance and narrowing down viable options.

How AI Can Help

AI's ability to manage vast amounts of data and predict outcomes can significantly aid in innovation. Companies like Moderna, Cooper Standard, and Pirelli have successfully utilised AI to enhance their product development processes:

• Moderna: Leveraged AI to accelerate vaccine candidate development.
• Cooper Standard: Used AI to advise on new polymer recipes, reducing R&D feedback loops by 70-80%.
• Pirelli: Implemented AI algorithms to select new tyre materials.

These examples demonstrate AI's potential to accelerate innovation by facilitating quick adaptations and optimising complex processes.

Focus on Recombination

Our research indicates that AI is most beneficial when used to combine and extend existing technologies rather than pursuing radical innovations. Companies that excel in process improvement and technology recombination tend to benefit the most from AI. Firms using AI for these purposes have shown a 3-7% increase in productivity and produce approximately three more new patents annually.

Conversely, AI is less effective for incremental improvements or radical innovations that require human creativity and intuition. Misapplying AI in these contexts can lead to reduced performance and wasted resources.

Applying AI to Innovation Processes

To leverage AI effectively for innovation, leaders should consider the following questions:

• Are you a fast follower? AI can help improve existing products and create new ones by recombining elements from prior successes.
• Are you struggling with data overload? AI can help synthesise broad and diverse data to find innovative solutions.
• Are you overwhelmed with choices? Investing in AI projects that support recombination and hiring AI talent can streamline decision-making.
• Do you depend on radical innovation? While AI may not directly benefit radical innovation, it can support follow-up projects and help safeguard and build upon initial breakthroughs.

Conclusion

AI is a promising tool for driving innovation, but its success depends on how it is used. Companies that focus on synthesising, combining, and extending existing technologies will find AI to be a valuable asset. However, it is crucial to align AI initiatives with the company's specific innovation goals and processes.

To explore how AI and business process review and automation can help your company innovate and stay ahead of the competition, contact Velocity today. Our experts are ready to help you harness the power of AI for your business.

FAQs About AI For Your Business

1. What are the main challenges of innovation in today's business environment?

The main challenges include the decline in traditional innovation metrics, the complexity and overlap of knowledge, high costs and long development cycles, rapid market changes, balancing incremental and radical innovation, navigating the deluge of data, and overcoming organizational silos.

2. Why has there been a decline in scientific paper production and patenting?

The decline is attributed to several factors, including the increasing specialisation of scientific fields, which makes it harder to produce groundbreaking work, and the complexity of modern scientific knowledge, which requires extensive collaboration and resources.

3. How does AI help manage the complexity and volume of data?

AI can ingest and analyze vast amounts of data more efficiently than humans, helping organisations to synthesize information, identify patterns, and make informed decisions. This can streamline R&D processes and reduce the time and cost associated with innovation.

4. What is the difference between incremental and radical innovation?

Incremental innovation involves making small, continuous improvements to existing products or processes, leading to steady progress and short-term gains. Radical innovation involves developing entirely new products or processes that can disrupt markets and create new opportunities, but it is riskier and requires more creativity and resources.

5. How can companies overcome organisational silos to foster innovation?

Companies can foster a culture of collaboration and knowledge sharing by encouraging cross-functional teams, promoting open communication, and using collaborative tools and platforms. Breaking down silos helps integrate diverse perspectives and accelerates the innovation process.

6. What role does AI play in addressing innovation challenges?

AI can help companies manage and analyse large volumes of data, streamline R&D processes, predict performance, and identify new opportunities. It is particularly useful for exploring combinations of existing technologies and enhancing incremental innovation, although it is less effective for radical innovation.

7. Can AI completely replace human creativity in innovation?

No, AI cannot replace human creativity, especially in radical innovation. AI is a tool that can enhance and support human efforts by managing data and predicting outcomes, but human intuition, creativity, and problem-solving skills remain crucial for breakthrough innovations.

8. What should companies consider when implementing AI for innovation?

Companies should consider their specific innovation goals, whether they are fast followers or depend on radical innovation, their current data management capabilities, and the need for cross-functional collaboration. Investing in AI talent and projects that support the recombination of existing technologies is also important.

9. How does AI support fast followers in innovation?

AI helps fast followers by improving existing products and creating new ones through the recombination of elements from prior successes. It enhances their ability to synthesise broad and diverse data, making it easier to adapt and innovate quickly.

10. What is the importance of balancing incremental and radical innovation?

Balancing incremental and radical innovation is important because it allows companies to achieve steady progress while also pursuing breakthrough advancements that can drive significant long-term growth. Focusing too much on either type can limit a company's potential for sustained success.