Home AI News Closing the AI Adoption Gap: Strategies for Business Success

Closing the AI Adoption Gap: Strategies for Business Success

Table of Contents

Introduction
The Gap Between Ambition and Execution in AI Adoption
The Stages of AI Adoption in Business
- Early Implementers of AI
- Pioneers
- Investigators
- Experimenters
- Passive Companies
Strategies for Data Acquisition and Implementation
Tools to Scale Up AI Applications
- Google AI Hub
- Kubeflow Pipelines
Priorities for AI Implementation
Building a Portfolio of Reusable Building Blocks
Summary of AI in Business
Conclusion

AI Adoption in Business: Narrowing the Gap Between Ambition and Execution

Artificial Intelligence (AI) has been hailed as a Game-changer for businesses, providing a real competitive advantage. However, there remains a significant gap between ambition and execution when it comes to AI adoption. According to a global survey conducted by the Boston Consulting Group and the MIT in 2017, less than 40 percent of companies reported having an ongoing AI strategy, despite more than 80 percent believing in its potential. Unfortunately, this gap has not decreased since then. In fact, a new survey by the Boston Consulting Group and the MIT reveals that pioneers of AI are still increasing their investments, widening the gap with other companies.

The Gap Between Ambition and Execution in AI Adoption

The survey conducted by the Boston Consulting Group and the MIT highlights the disparity between companies' belief in the competitive advantage offered by AI and their actual implementation efforts. Despite the overwhelming Consensus on its potential, only a fraction of companies have a well-defined AI strategy in place. This lack of execution puts these companies at a disadvantage, as early implementers of AI are already reaping the benefits at scale.

The Stages of AI Adoption in Business

To understand the current state of AI adoption in business, it is crucial to recognize the different stages companies fall into. These stages provide insights into the actions, strategies, and tools that can be used to bridge the gap between ambition and execution. The stages of AI adoption include:

Early Implementers of AI

This group comprises companies that are at the forefront of AI adoption. They understand the potential of AI and have already incorporated it into their business operations. Their focus is primarily on revenue-generating activities, rather than cost savings. However, such companies represent less than 20% of the surveyed organizations.

Pioneers

The pioneers are companies that have implemented some form of vertical or horizontal AI strategy. A horizontal strategy involves building an AI product or platform that can be utilized by various industries to solve problems more efficiently. On the other HAND, a vertical strategy focuses on one specific industry and addresses its unique challenges using AI.

Investigators

The group of investigators consists of companies that are researching and educating their workforce about the possibilities of AI. However, they have not yet actively experimented with AI applications. Blockers such as lack of human resources or a clear data acquisition strategy hinder their progress in implementing first experiments.

Experimenters

Experimenters are companies that learn by doing. They conduct small-scale tests within a controlled environment but have limited understanding of AI. Blockers, such as difficulty in matching business problems with appropriate AI solutions or refining assumptions, prevent them from fully scaling up their applications.

Passive Companies

The passive group comprises companies that neither experiment nor investigate AI applications actively. These companies may not need AI in their existing business models or believe that competitors cannot discover applications earlier than them. However, it is essential for these companies to start considering the priorities highlighted in reports, such as personalization, trust, workforce, and data acquisition.

Strategies for Data Acquisition and Implementation

Implementing robust data acquisition and implementation strategies is crucial for successful AI adoption. Companies must develop an AI strategy, build an in-house AI team, and train their workforce to execute this strategy effectively. Additionally, creating a portfolio of reusable building blocks is vital to leverage previous data science projects and enable widespread AI adoption.

Tools to Scale Up AI Applications

To simplify and streamline the adoption of AI, Google offers two valuable tools: AI Hub and Kubeflow Pipelines. The AI Hub provides businesses with a centralized platform where they can access and share machine learning resources. This hub allows for collaboration within organizations while ensuring data security. Kubeflow Pipelines, on the other hand, enables companies to build and Package machine learning resources into reusable applications, driving adoption across departments.

Priorities for AI Implementation

Based on a PwC survey, developing AI models and data sets that can be utilized across the organization is a top priority for executives in 2019. This capability allows companies to maximize the efficiency of their AI initiatives and promote collaboration between different teams. By focusing on building reusable AI components, organizations can increase the speed of deployment and ensure that valuable insights are shared throughout the company.

Summary of AI in Business

Despite the gap between ambition and execution in AI adoption, businesses are waking up to the immense potential AI offers. Early implementers and pioneers are already reaping the rewards, while others are investigating and experimenting with AI. With the right strategies for data acquisition and the use of tools like the AI Hub and Kubeflow Pipelines, companies can bridge this gap and scale up their AI applications. However, it is crucial for all companies to understand the priorities and build a portfolio of reusable building blocks to fully leverage the power of AI in their organizations.

Conclusion

AI adoption in business has come a long way, but there is still work to be done in narrowing the gap between ambition and execution. Companies need to develop and implement robust strategies for data acquisition, invest in the right tools, and prioritize the development of reusable AI components. By doing so, businesses can unlock the true potential of AI and gain a competitive edge in the rapidly evolving digital landscape.

Highlights

The gap between ambition and execution in AI adoption in business is still significant.
Early implementers and pioneers are leading the way in reaping the benefits of AI at scale.
Investigating and experimenting with AI is essential for closing the gap.
Robust data acquisition and implementation strategies are crucial for successful AI adoption.
Google's AI Hub and Kubeflow Pipelines are valuable tools for scaling up AI applications.
Developing reusable AI components is a top priority for executives.
Bridging the gap between ambition and execution requires understanding priorities and building a portfolio of reusable building blocks.

FAQ

Q: What is the current state of AI adoption in business? A: The gap between ambition and execution in AI adoption remains significant. Only a fraction of companies have a well-defined AI strategy in place, despite the majority believing in its potential.

Q: What are the different stages of AI adoption? A: The stages of AI adoption include early implementers, pioneers, investigators, experimenters, and passive companies. Each stage represents a different level of engagement with AI technology.

Q: How can companies bridge the gap between ambition and execution in AI adoption? A: Companies can bridge the gap by implementing robust data acquisition and implementation strategies and leveraging tools like Google's AI Hub and Kubeflow Pipelines. Additionally, developing reusable AI components and understanding priorities are vital for success.

Q: What are the top priorities for executives in AI implementation? A: Developing AI models and data sets that can be used across the organization is a top priority for executives. This enables maximum efficiency and collaboration within the company.

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