Why the Role of the AI Business Translator is Much in Demand

Organizations all over the world are increasingly depending on AI and data to improve their working processes, transform business models, drive innovation and hone in on new opportunities. Intelligent business systems are becoming the critical differentiator for many companies, from simple automation to predicting future risks.

Yet, till now, 80% of AI projects never reached deployment, and of those that did, only 60% were profitable.

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There are various reasons why AI projects fail, such as:

  • Didn’t define a specific problem to solve
  • Lacked high-quality data
  • Data was biased
  • Didn’t put together the right team

The Right Data and AI Team

Among the 20% of AI projects that do succeed, 92% have one thing in common. Their success stories involve a multi-disciplinary team.

These cross-functional, agile teams include data engineers, data architects, data-visualization experts, business and use case experts and business translators.

The role of the business translator has emerged critical in achieving impactful results in enterprise AI initiatives.

What is an AI business translator?

AI business translators play the critical role of bridging the gap between technical experts on an AI team, such as data scientists and data engineers and functional experts, such as executives from various verticals like marketing, manufacturing, supply chain.

AI business translators don’t require deep technical expertise in data analytics or AI modeling, but they do possess domain knowledge. They utilize this expertise to structure and prioritize the business problems that the AI tool will solve so as to generate the highest return on investment for the organization.

The McKinsey Global Institute estimates that demand for translators in the U.S. alone will be somewhere between two and four million by 2026.

Why is the role of the AI business translator critical?

Operationalizing AI is fraught with challenges for most companies, and for traditional businesses, the road to digital transformation is even rockier. Older companies are entrenched in their ways of working, are afflicted with legacy infrastructure and have digitally immature employees.

The success of AI and analytics initiatives will depend on overcoming these challenges — which requires an understanding of how to help the company veer towards digitalization, support and advocacy from all employees and strategic leadership.

Business leaders and AI translators need to help center data and AI in all aspects of decision-making—from strategy to operations, supported by business goals.

The AI business translators will join together the business vision and goals into data and AI requirements, oversee project execution and ensure that the project outcomes are integrated into business processes.

Without the AI business translator, the distance between the technical experts and their business counterparts will widen. Translators need to strategize and drive the business impact of the AI initiative while leveraging the opportunities in data and AI.

What are the responsibilities of an AI business translator?

Business translators need a solid foundational knowledge of AI and data strategy to realize how to use them to solve business problems. But they are not the ones responsible for training coding or modeling AI.

The crux of the AI translator role depends on how well they can hone in on the opportunities present within an organization to leverage data and AI.

The AI business translator will be responsible for:


 

  • Formulating business problems to solve and prioritizing them based on value
  • Provide use cases and context to the data that will be used in the AI deployment
  • Define the metrics and run test cases for business problems that will integrate with AI
  • Interpret the results and provide inputs based on the outcomes of the algorithms
  • Adapt business processes based on AI outputs

What are the skills an AI business translator needs?

The AI business translator wears many hats, such as leader, communicator, project manager, industry expert. Based on the multitudes in this role, business translators need:

 

  • Domain Knowledge

    Domain knowledge is essential for business translators and should have a deep understanding of the specific industry and the company to discern the value of data and AI in the business context.

    AI translators should know the operational metrics of the business, such as profit, loss and revenue, as well as common use cases, such as personalized marketing or inventory management.

 

  • Understanding of Data and AI

    Apart from domain knowledge, business translators need an understanding of AI and ML, data strategy, strong quantitative and problem-solving skills.

    While they aren’t required to build quantitative models, AI translators need to know the types of models and technologies that would be most suited to solving a business problem — such as deep learning versus logistic regression.

 

  • Project Management Skills

    AI business translators will need to direct AI initiatives from the ideation stage through implementation and adoption. Hence, an understanding of the life cycle of the AI project and its challenges will be crucial.

    As more organizations adopt AI, both business leaders and data professionals are struggling to articulate their needs as they work together in enterprise AI projects. AI business translators will play the critical role of creating a common language that will drive the whole initiative forward and help organizations adopt AI more seamlessly.

Sign up for the MIT SAP Data Strategy: Leverage AI for Business course to build a foundation for the role of an AI business translator by learning how to apply data strategy and AI to solve real-work business problems.

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