Highlights:

  • To successfully scale their AI initiatives, organizations must implement a robust data management strategy that covers all aspects of the data lifecycle, including collection, storage, integration, and cleaning.
  • Companies have begun to establish AI Centers of Excellence (AI CoE) to bridge the gap between executive decision-making and AI implementation within an organization.

Artificial intelligence (AI) is revolutionizing numerous industries and business practices with its multiple applications. Companies should scale their AI initiatives and deeply incorporate AI applications into their business processes to fully realize AI’s potential. Accenture claims businesses that use AI at scale see a 3x higher return on their AI investments than those that only use it as a proof-of-concept.

For many businesses, scaling AI initiatives from the proof-of-concept stage to the enterprise level presents a significant challenge. This is so that AI can be used at scale, which goes far beyond simple plug-and-play AI models. This article examines four essential steps for companies wishing to implement AI on a large scale successfully.

  1. Spend money on a data strategy

Data should be the foundation of any organization-wide AI strategy because it is the lifeblood of AI and Machine Learning (ML) models. Managing, purifying, maintaining, and using data as AI is scaled becomes even more challenging. Therefore, it isn’t very easy to implement AI at scale across an organization without appropriate methods and tools for managing the various aspects of a data lifecycle.

Some difficulties in handling big data include:

Data silos: These are the areas of the company where one or more departments have access to data but where the rest of the business is cut off. As a result, the organization’s efficiency and transparency suffer.

Incompatible Data: Data collected from various sources and formatted differently must be standardized before use.

Inaccurate data: Large datasets invariably contain incorrect, outdated, and other problematic data that must be cleaned before an accurate analysis.

To successfully scale their AI initiatives, organizations must implement a robust data management strategy that covers all aspects of the data lifecycle, including collection, storage, integration, and cleaning.

Automation is a critical component of a scalable data management strategy. To efficiently automate data orchestration, organizations should implement DataOps practices.

Many tools also cover different aspects of data lifecycle management or the entire process.

  1. Use MLOps to streamline AI processes

In the same way, you should standardize and streamline data-related processes. You should standardize and streamline how you build, deploy, and manage your models. Creating a few ML models that work well for specific business problems is relatively simple. Still, things can quickly become complicated if you want to implement AI systems across the enterprise.

This is because:

  • Building machine learning models requires a lot of trial and error to find the best models, datasets, hyperparameters, codes, and so on. This is particularly difficult for complex models trained on large datasets, such as deep learning, natural language processing (NLP), or computer vision.
  • Building a model differs from using it: Only 36% of businesses could move beyond the pilot stage with an ML model.
  • All models should be monitored in real-time to ensure that model decay does not occur.

Managing these processes with data scientists from various departments working in silos would severely limit an organization’s ability to scale its AI projects.

This is why businesses have begun to use MLOps practices to standardize and automate processes associated with developing and managing ML algorithms. MLOps assists organizations in the following ways:

  • Using automated pipelines, you can shorten the machine learning lifecycle.
  • Create a unified framework for stakeholders to follow to improve communication and collaboration.

  1. Form multidisciplinary teams

A small team of data scientists may be sufficient to manage a couple of models.

However, when it comes to scaling AI, it requires a diverse range of expertise, encompassing data engineering, IT and cybersecurity proficiency, project management, and more. More importantly, technical staff must be linked with business professionals who will determine specific use cases based on business requirements.

Companies have begun to establish AI Centers of Excellence (AI CoE) to bridge the gap between executive decision-making and AI implementation within an organization. These business units bring together technical experts from various departments to coordinate and oversee organization-wide data science and AI initiatives.

  1. Create an enabling company culture

Scaling AI necessitates the adoption of innovative tools and technologies, alongside the imperative need to adapt the company’s culture and work methodologies. This comprehensive approach is essential because:

  • Employees may be afraid of being replaced by AI, slowing the transformation.
  • A large-scale AI transformation necessitates acquiring new skills because it fundamentally alters how employees interact with machines.

To address these challenges, businesses should:

  • Create opportunities for employees to retrain and gain new skills
  • Restructure workflows, policies, and business processes
  • Enhance top-down communication to ensure everyone is aware of the changes, their causes, and the expectations

Conclusion

Organizations must investigate the potential of AI and approach their AI investments strategically. Organizations can use AI for more than just accelerating and automating current processes. AI can help businesses take advantage of fresh opportunities and increase their influence among stakeholders such as stakeholders, customers, and employees.

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