Decoding the Key Ingredients of an Effective AI Strategy

Embracing Operational AI

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For any business, embracing Artificial Intelligence (AI) can be both an exciting leap into a technologically advanced future and a colossal challenge to overcome. Having advised on AI strategy for a globally prominent technology company as part of my experience with an AI transformation consultancy, I am well-positioned to share my insights on this transition and how large firms can harness AI's potential.

Corporations are increasingly taking the plunge from their previous low maturity state to an AI-centric model. This evolution isn’t without its share of hurdles, but the rewards of an effectively implemented AI strategy are enormous.

Many organizations have already transitioned into the phase of Operational AI. Here, AI isn't just a concept but an integrated part of their business operations with multiple projects in full swing. These projects are yielding considerable value, both in terms of enhancing processes and enriching products, setting a new standard within the organization and kindling an ongoing process of refinement.

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The Strategic Importance of Data Culture

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Yet, before contemplating AI, it is paramount to cultivate a robust data culture within the organization. Whether this is achieved through curated data pipelines or the construction of data lakes, a solid foundation of data culture is vital to allow the AI ecosystem to function effectively.

The organizations blessed with high-quality data lakes are often in a position to train superior AI systems. The more widely these systems are utilized, the more data they feed back into the data lakes, thereby enriching them further. It's a self-reinforcing cycle, where the organizations with the most comprehensive data train the most efficient AI systems, and in turn, these AI systems generate even more data.

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Building Data Domination and Proprietary Assets

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This escalating race for data supremacy has strategic implications. Once a company has accumulated enough data and streamlined its real-time data collection processes, it becomes challenging for new entrants to compete without accessing the data repositories of these established players. In the context of data and AI, the advantage of being a pioneer is even more significant. The ability to access a vast and relevant data set renders new data collection endeavors time-consuming and commercially difficult.

This pursuit of data domination is not about amassing a vast quantity of data. Rather, it's about owning proprietary data that is hard to replicate and hence, immensely valuable. This type of data is seldom available for free. It's about nurturing a unique repository of proprietary data that will gradually confer market strength and a defensive edge over time.

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Cultivating Strategic Partnerships and Leveraging Open Data

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Data domination allows larger organizations to experiment with various AI-centric concepts. Smaller companies, often constrained by budget limitations, may not have the liberty to take such risks. However, the earlier you embark on this journey, the harder it will be for your competition to overtake you.

A crucial but often overlooked aspect is the development of strategic partnerships. By forming alliances with other organizations, academic institutions, data providers, or governmental departments, your company can acquire exclusive access to unique data sets and the benefits they offer.

Moreover, it's common to find that while large corporations have plenty of data, they often struggle to build efficient AI systems. Conversely, AI startups may lack the high-quality data sets necessary to train their models. This creates an environment conducive to collaboration, where both entities can benefit from each other's strengths.

Interestingly, not many large firms leverage open data. However, many AI systems are primarily or even solely trained on open or publicly accessible data, such as text from open sources like Wikipedia or social media platforms like Twitter.

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Finding Balance in Business Models and Intelligent Products

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The future may see a shift towards new methods to optimize data. Techniques such as data augmentation or transfer learning can help companies execute their projects with less data.

Shifting from traditional business models to innovative ones is an essential aspect of an effective AI strategy. One method to gather useful data and strengthen your market position is by creating a product that is valuable even without machine learning and then selling it to gather data. This "data trap" strategy involves offering users substantial value from the start and using the resulting data exhaust to collect large amounts of information.

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Creating New Revenue Streams and Selling Knowledge

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AI also presents an opportunity to create new revenue streams. By using the information available due to AI, companies can identify areas where the combination of internal and external data provides access to a new market. This strategic shift in business models helps build long-term competitive advantage.

AI's knowledge capture ability creates a possibility for businesses to transition from selling physical products to selling knowledge. For example, an advanced manufacturing plant could leverage AI systems to offer its knowledge or processes to its competitors in the same sector.

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Building a Culture Around AI

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AI is more than just a technological solution. It requires a cultural shift within the organization. Promoting data sets and challenges can be a powerful lure for potential hires. While AI provides unique abilities to open new markets and create new revenue streams, its benefits are maximized when implemented at scale across an enterprise.

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Key Takeaways

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• Developing a robust data culture is fundamental to an effective AI strategy.
• Building data domination by accumulating proprietary, hard-to-replicate data is essential.
• Forming strategic partnerships and leveraging open data can provide competitive advantages.
• Shifting from traditional business models to innovative ones is key to a successful AI strategy.
• AI's knowledge capture ability allows for the transition from selling physical products to selling knowledge.
• Implementing AI at scale across an enterprise maximizes its benefits.
• AI is more than just technology, it requires a cultural shift within an organization.

Remember, creating a strategy with AI matters as much — or even more — than a strategy for AI when it comes to exploring and exploiting strategic opportunities.

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