Three mantras to ease the pain that AI adoption could present

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Three mantras to ease the pain that AI adoption could present
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AI is perhaps the most celebrated buzz word in the enterprise technology world – at least in the last decade. It has transformed, and will continue to transform the way we work, live and experience. This technology has the potential to add about $15.7 trillion to the world economy by 2030.
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While AI comes in-built in many products and services that enterprises use today, matured use-cases of the technology aren’t mainstream just yet. Only about 26% of organizations have AI projects in production, according to a global report released by O’Reilly.

Scaling AI or deploying AI across the organization is perhaps one of the biggest challenges that enterprises face today. This is proving to be a roadblock in realizing the full potential of AI and ML. So, what exactly is holding back the adoption of full-fledged and scalable AI within enterprises?

Here are some of the common challenges and the ways to address them:


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Lack of business-driven objective: Organizations often start working with AI to address point problems. It works out well in the beginning and results are immediate. As they try to scale up, complexities mount. For example, AI requires customization at every step and teams will need to train each deep learning model as they move along with more use cases and applications. That’s exactly why most AI projects end up being time consuming, expensive, and overwhelming for teams.

The fundamental challenge in scaling AI starts from this lack of an overarching vision.

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It's critical for organizations to set long-term business driven objectives and KPIs while moving AI to production. Introducing AI to a key business function would also require a solid strategy to deal with the ensuing organizational change. It is equally important to have the C-suite buy-in for successful completion of AI projects.

The data hole: Lack of quality data and clean data can derail any ambitious AI project. It’s simple - you get back what you give. Machine learning-based systems depend heavily on data to build the predictive capabilities. Inaccurate or incomplete data can quickly lead to the failure of AI projects even before organizations realize.

Harnessing and preparing the data is a humongous task for organizations that rely on traditional data management methods. To add to the data woes, explosion of unstructured data leads to the classic problem of ‘too much data’. Data silos and distributed data sources within the organization are other constraints to deal with.

Ensuring data quality for both training data and working data is essential for the success of machine learning models. Involve data experts right at the beginning, focus on data analysis and review and invest on modern tools to ensure that you don’t go down the data hole.

The AI skills gap: Shortage of skilled resources is a key barrier that organizations face in advancing their AI projects.

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About three in four Indian enterprises think that lack of skills in digital technologies such as AI/ML impede innovation. Globally, a large percentage of organizations, including early adopters, struggle with the AI skills gap. And this gap is evident at all levels of the AI field – right from AI researchers to data scientists to business leaders.

AI-ready talent will continue to be in high demand in the coming days. Organizations need to adopt a balanced approach of ‘hiring and retraining’ to ensure sustainable availability of talent. It’s time that organizations prepare for a future in which employees work side by side with AI.

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