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Cracking the AI Implementation Code by Operationalizing AI

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AI is increasingly affecting our daily lives as more and more tools are using AI.

There is no doubt that enterprises are taking a serious interest in adopting artificial intelligence and machine learning. But the knowledge of how it must be deployed to accelerate automation and transform business processes is still in its nascent stages.

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AI is making inroads into our lives. According to a new KPMG survey of industrial manufacturing business leaders, the next two years will see AI technology having varying impacts on industry needs: 21% for product design, development, and engineering, 21% for maintenance operations and 15% for production/assembly.

In fact 61% of business leaders believe that increased productivity is the most significant potential benefit of AI adoption .

Despite the interest, enterprises across industries still struggle with the process of AI implementation to achieve predicted business benefits.


As companies race to digitize and embrace edge technologies, the transition often requires business leaders to shift their thinking from traditional software engineering expectations.

There are many reasons AI and machine learning models don’t necessarily pay off.

Some of these include:

1.Lack of Qualified Data Scientists

Data science is an essential aspect of developing a suitable machine learning methodology. But the growth of data processing in AI has led to a demand for data scientists who can help turn raw data into business value. This shortage can be overcome by either outsourcing the ML model development or training employees already working with data in ML model programming.

2.Poor Data Quality

AI and machine learning tools rely on clean data to train algorithms. And businesses that do not have control over their data quality or data management will struggle to make their AI initiatives successful. Data engineering enables enterprises to maximize the value of their data assets. By working towards enabling cleaner data sets, businesses can deploy machine learning algorithms to design accurate predictive analyses.

3.Undefined End Results

What performance metrics are to be measured when developing and selecting machine learning models? Businesses often fail to know the desired level of performance before an AI project begins, leading to a mismatch between model results and expectations. Understanding the project deployment maturity levels can help leaders understand the progress needed to adopt AI successfully.

4.Difficulties in replicating ML model results

Incremental data and different environments often cause ML models to perform differently. ML models need to be updated or refreshed to account for data drift, deterioration or anomalous data.  Rather than upgrading the ML model every time, businesses need to create repeatable modelling processes to ensure continuous learning happens during production.


Operationalizing AI involves combining ML learning methodologies with software engineering principles to create a production-grade solution. Using established frameworks can help companies find a starting point to formulate best practices to go forward.

Atul Varshneya, VP of the Artificial Intelligence Practice at Tavant, has detailed an approach and points to consider for businesses looking to operationalize their AI initiatives.

If you are looking for ways to move your machine learning projects from experimentation to execution, watch this recorded webinar.

Are you looking to overcome the challenges in operationalizing AI for your business?

If yes, then reach out to us at [email protected].


Impact of AI on industrial manufacturing (kpmg.us)

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