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Unlocking the power of AI #3 – Best practices for AI implementation in business

Explore AI implementation challenges & risks. From data quality to cybersecurity, uncover key factors shaping the AI landscape.

Welcome to the third part of our series on harnessing the potential of AI. If you haven’t read parts 1 and 2, you can do so here and here. In this section, we’ll focus on essential best practices to implement AI in business. AI holds the promise of transforming businesses, yet realizing its full potential necessitates deliberate planning and precise execution.

Best practices for implementing AI in business

Using AI in business can revolutionize how tasks are done. Nevertheless, it takes careful planning to do it right.

1) Establishing clear business objectives

Before integrating AI into operations, organizations must define precise objectives and areas where AI can produce the most substantial benefits. This may require conducting an analysis of existing business processes.

2) Start small and expand as needed

It is wise to start with a small-scale project before going for a broader implementation. This approach enables businesses to carefully assess and refine their AI systems, guaranteeing that the intended outcomes are achieved.

3) Focusing on data quality and bias mitigation

The efficacy of AI algorithms relies on the quality of the data used for training them. As a result, businesses must prioritize data integrity and try to eliminate bias. This endeavour may involve data cleaning, and the use of techniques like data augmentation to improve data quality.

4) Investing in infrastructure and knowledge

The successful implementation of AI needs a dedicated infrastructure and a skilled workforce. Businesses must allocate the requisite resources, which may involve investing in high-performance computing infrastructure and recruiting personnel with expertise in AI and machine learning.

5) Ensuring transparency and accountability

AI systems can be intricate and their decision-making processes may not always be readily apparent. Businesses must take measures to ensure that their AI systems are transparent and accountable. This could involve the use of techniques such as explainable AI, aiding users in comprehending the logic behind AI-driven decisions.

6) Performance monitoring and adaptation

AI systems are dynamic and their performance may change over time. As a result, businesses must monitor the performance of their AI systems and be prepared to adapt as necessary. This may involve periodic retraining of AI models, refining business processes, or adopting new algorithms to ensure ideal performance.

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Your content and data are the foundation upon which your business operates, and critical decisions are made. Recent advancements in AI in areas such as image and natural language processing have enabled a whole new level of automatic extraction of information and data analysis that power the automation of key business processes not possible until now.

  • Process your data with different AI engines, integrating the results.
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  • Generate new content and document versions based on AI results​.
  • Store extracted information in metadata, enabling further processing and process automation.
  • On cloud or on-premises – in case you don’t want data to leave your private infrastructure.
  • Compatible with several different ECM providers
  • Ability to develop custom AI models to target your specific needs and data.

AI is essential to remain relevant!

The adoption of AI in modern organisations is essential to remain relevant and competitive, optimising efficiency, empowering new business opportunities and freeing critical human resources to specific value-added tasks.

Download here our TML – Texter Machine Learning – Datasheet:

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