3 Vital Steps for Successfully Implementing AI for Enterprise Management

 3 Vital Steps for Successfully Implementing AI for Enterprise Management

AI for Enterprise

The prospect of using artificial intelligence (AI) to make enterprise processes more efficient is very tempting for business leaders. Interest in using AI for enterprise management has grown significantly since 2019.

In 2019, only 14% of the organizations that took part in a Gartner survey had deployed AI solutions. In 2021, that figure is well over 24%. However, for unprepared companies, rushing the AI implementation process will only lead to more organizational problems.

For AI to add true value to a business, leaders must ensure their workforces understand the technology. Once they understand how AI can help specific business avenues, deploying AI solutions for better enterprise management will become easier.

This article discusses three vital steps all businesses must take before deploying AI-powered enterprise management tools:

1. Create a Standard of AI Understanding in the Workforce 

The success of early AI projects depends on how the entire company responds to these technologies. Unless human operators are kept in the loop, successful AI implementation is impossible. That is why business process managers, data scientists, etc., all must collaborate.

Once a basic standard of AI understanding is established in a company, AI deployment will become a less complex process. Here are some steps top companies take to educate their workforces about AI:

  • Create support systems to clarify confusion regarding the impact of AI on different business processes. Create a team of AI experts who can explain to workers how this technology eliminates repetitive/administrative tasks.
  • Ramp up AI education for all workforce members – from HR professionals to the company’s top C-Suite execs.
  • Match your AI development strategies with key workforce needs. Adopt, deploy, and customize AI tools that specifically help employees meet their business goals.

2. Build Data Platforms

In 2020, Forrester Research surveyed the world’s leading data and analytics professionals. 90% of them said that, using data-backed insights is a business priority. 91% of them said that, harnessing and optimizing those insights is a key challenge for their organizations.

To implement AI for enterprise management solutions, businesses must reshape their organizational designs. They should start prioritizing the development of agile data governance and data management systems that mean:

  • Building modernized data platforms that can streamline data collection processes.
  • Build systems for collecting, storing, and structuring data.
  • Use AI-powered tools that provide automated reports and send analytical insights after processing these data sources.

Many top companies have already created such streamlined data management systems for their companies. Smaller companies can rely on third-party, AI-powered data management tools and AI accelerators to achieve similar capabilities.  

3. Keep Launching Focused, Small-Scale AI Pilot Projects

All it takes is one/two successful AI pilot projects for business stakeholders to become convinced about investing in full-scale AI adoption. The purpose of AI pilot projects is to measure the potential impacts and risks of full-scale AI implementation in the future. Here is how businesses should approach AI pilot projects:

  • Choose projects that can be completed quickly (ideally within 6 months or less) and have high chances of success.
  • The pilot project should achieve some quantifiable results. Results help convince business leaders to invest in future AI projects.
  • Select company-specific pilot projects to determine the true value AI brings to your commercial needs. E.g., a retail brand should focus on launching AI pilot projects designed to boost customer experiences.

Using AI for enterprise management becomes much easier when organizations take these preparatory steps.

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