10 steps to adopting artificial intelligence in your business

adopting artificial intelligence in your business

10 steps to adopting artificial intelligence in your business

  • Artificial Intelligence (AI) is a potent force in tech industries that is taking the prime position in a variety of sectors including retail and manufacturing. Modern products are integrated with virtual assistance while chatbots readily answer customers from the online office supplier’s site to web hosting service provider’s support page.

Here are 10 steps as to how AI can be incorporated in your business:

Familiarize with AI

  • You should take advantage of available resources and online information to familiarize yourself with AI’s basic concepts. The TechCode Accelerator provides various startups with a varied range of resources through partnerships with organizations such as Standford University and corporations in the AI space. Organizations such as Udacity suggests easy ways to get started with AI and increase your knowledge in Machine Learning (ML) and predictive analytics.

Scrutinize the problems that you want AI to solve

  • Plan how you can add AI capabilities to all your existing products and services. Your company should be structurally AI-oriented to solve business problems. For instance, if your company wants to do video surveillance, it can secure a lot of information just by adding an AI-based ML to that process.

Give importance to the base value

  • It is important to assess the financial value and business potential of the various AI implementations identified. You should prioritize on the dimensions of potentiality and feasibility based on near-term visibility for the company. For this, you need ownership and recognition from top-level executives. You should stress the importance of linking your efforts directly to your business value.

Inner capability gap acknowledgement

  • You should know that what your company is capable of and what not from the perspective of a technological business before launching a full-blown AI implementation. Addressing your internal capability gap refers to the identification of the need to acquire and any processes that need to be attended internally before it gets going.

Establishing a pilot project by expertizing

  • Business is ready only when it starts to integrate itself. This is where the need for expertizing AI consultants is required. After a pilot project is completed, you should be able to decide whether the value proposition makes sense for your business. The people who know about your business, as well as AI, should be merged on your pilot project team.

Data integration through a task force

  • Your business data should be clean and subtle before incorporating ML into it. It is an important step to get high-quality data by forming a cross-task force and integrating different datasets to sort out all inconsistencies. AI system scanned data have all the right dimensions required for ML.

Apply AI to a small sample of data 

  • You should begin to apply AI to a small data sample to make it a simple one. Start simple use of AI incrementally to prove value, expand and collect feedback. You should make selection on what the AI will be reading. Without throwing all the data while answering a certain problem, you should focus the AI on it and solve it specifically.

Plan your AI by including storage

  • You should consider the storage required to implement an AI solution after wrapping all your data sample. To reach research results, an improved algorithm is necessary. This requires huge data volume to build up more accurate models. Thus, the requirement of a fast and optimized storage system should be considered in the AI system design. 

Make AI a part of the daily schedule

  • With automation and additional insight provided by AI, you should make a tool to make AI a part of your daily routine. Companies should tech transparent to resolve issues in a workflow. That is where the consideration of AI on a regular basis is important. Visualizing how AI arguments play their role is highly necessary.

Forming balance

  • While making an AI system, a combination of meeting the needs of new technology along with the research project study is important. Before starting to design a considerable AI system, you should first build the system with balance. Designed AI around specific aspects that encircle around the viewpoints through which a team can achieve its research goals. To achieve this balance, your company should have sufficient bandwidth for storage, networking and the graphics processing unit (GPU).

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