How to Implement AI Enterprise Solutions

Ai solution for your enterprise

The following step-by-step guide discusses how to implement AI enterprise solutions effectively.

Understand the Enterprise Needs and Objectives:

The first step while implementing an enterprise AI solution is to understand the organizational needs. When deciding on an AI solution, make sure it aligns with your business goals. 

As a project manager or a CTO, you will set metrics to measure the effectiveness of the proposed AI solution and make modifications if required.

Build a Strong Team of AI Professionals:

It is essential that you have AI professionals on board. You will need a team of experts including, business analysts, software architects, software engineers, and quality assurance staff who are well-versed in AI technologies.

For instance, your software engineers should have extensive knowledge of the required domains of AI such as machine learning, computer vision, natural language processing, data analytics, etc. 

Your software architects should be able to devise an effective AI development solution for your business needs. They will help you in adopting the right development approach and techniques like the implementation of a suitable neural network, selection of learning algorithm, validation methods, etc.

For this purpose, your team should be experienced in the related AI technologies. For example, if you plan to build and train a neural network for understanding your customer sentiments, your engineers should know about the related NLP domain of semantic analysis.

Moreover, they should be proficient in supervised and unsupervised machine learning approaches, prominent machine learning algorithms for your specific business problem, etc.

Even if you plan to use third-party services for implementing an AI solution for your enterprise, a deep knowledge of AI solutions and how they work in the back end will help you and your team in making the correct decisions and adopting the right AI implementation approach.

Moreover, your developers should be experienced in using common AI development programming languages such as Python and Java and their ML-related libraries and packages.

They should be familiar with software IDEs and tools such as Jupyter Notebook, Sublime, Eclipse, Tensorboard, etc. to build scalable AI solutions efficiently.

Read more on best AI tools available today on our blog here.

Get Familiar with Available AI Solutions

Today there are many AI services available on the cloud. Prominent examples include Amazon AI and ML webs services, Microsoft Azure AI, etc. You can opt for a cloud infrastructure that suits your business needs and is cost-effective. 

It is important that your team performs an elaborate analysis of all the available options by going through their websites and consulting with their customer support team. 

Adopting a cloud infrastructure for AI solution will free your development team from a lot of back-end tasks such as networking, operating system management, storage management, etc. Your team will be able to focus completely on AI solution development and maintenance.  

Identify Where your Enterprise needs an AI Solution

The global market for AI solutions will reach an economic worth of 16 trillion US dollars by 2030. Although the opportunities for business growth are huge by adopting AI solutions, it is necessary to apply an AI solution to a problem that actually needs an AI solution.

To spend your resources effectively on the implementation of AI solutions, your technical and business development team should carefully figure out the business modules that can be accelerated using an AI solution. 

A reasonable approach is to improve current business processes with AI solutions in incremental stages. This will help your enterprise to carefully monitor the outcome and adjust AI use cases according to the business needs.

The general manager of the Global AI+ Accelerator program, Luke Tang, explains this point in these words,

 “When we’re working with a company, we start with an overview of its key tech programs and problems. We want to be able to show it how natural language processing, image recognition, ML, etc. fit into those products, usually with a workshop of some sort with the management of the company. The specifics always vary by industry. For example, if the company does video surveillance, it can capture a lot of value by adding ML to that process.”

Perform Data Integrations

Data is an important part of implementing AI enterprise solutions. Most ML models give optimal results when exposed to huge amounts of relevant data. For this reason, you, as a CTO, are responsible to make this data available for AI developers through data scientists and other relevant professionals.

This stage can include multiple tasks like collecting and integrating data from multiple sources or legacy systems, performing data wrangling and cleaning, formatting data, creating a data management framework, etc.

Data integration and preparation will also give you an idea of the required processing power and storage capacity. As AI models perform better on huge datasets, they, in turn, need more processing power and memory.

If you are opting for a cloud solution to implement an AI enterprise solution, your needs for computing power and storage can be fulfilled instantly. You won’t be requiring on-site high-processing computers and servers. All this hardware will be handled on a cloud by the service providers.

Start with a Pilot Project and Scale with Balance

At this point, you have successfully identified the opportunities in your enterprise for using AI solutions effectively. You have a team of AI professionals who are equipped with the required knowledge of the AI domain and familiar with the available options for AI solutions.

Moreover, you and your team have worked on data collection, preprocessing, and management. Now, your business is ready to go through a pilot project on a small scale.

Your AI engineers and architects can implement an AI solution using the suitable AI technique and deploy it for a specific business process. 

The next step is to monitor the performance. Are your set metrics indicating in the right direction? 

If the data insights help your business executives in making better decisions that ultimately improve your business growth, it means your AI solution is working and it can be expanded to other business modules or departments.

As you scale your AI enterprise solution, it is important to constantly monitor its performance and mature the overall lifecycle of AI development. Some ways to mature the lifecycle of Artificial Intelligence development include:

  • Creating a data platform that helps your employees to collect, store, and analyze data in a streamlined manner. Reporting and analytical insights form a huge part of business process automation in enterprises.
  • Create business processes, technologies, management systems, etc. focused on your business use cases such as customer support, sellers management, etc. Our blog here discusses business process automation in detail.

You can read more on AI development lifecycle on our blog here.

Some Challenges in Implementing AI Enterprise Solutions

Following are some of the challenges you should consider when implementing an AI enterprise solution.

Data Security: It is a major challenge for businesses today. AI models working on large customer data can pose a risk to data security.

Industry Compliance: Sophisticated AI solutions need more effort in ensuring that all legal compliance requirements are being met.

Turn Around Time: Complex AI technologies require expert skills. Implementation of AI solutions and training of staff for required expertise may require more time and effort than expected.

Planning to Create an Enterprise AI Solution?

AI solutions are providing great benefits to enterprises in the form of efficient customer service, improved business processes, increased productivity, informed decision making, etc. There has never been a better time to invest in an AI enterprise solution.

At the same time, it is important to understand the correct procedure of making such an advanced technological investment.

Aligning your business goals, figuring out the right business use case that can utilize AI solutions effectively, forming a qualified AI advisory and technical team, and choosing the right technology is of utmost importance.

There is no doubt that steps related to choosing the third-party AI services, implementing end-to-end AI solutions, and then managing them for a good performance require a competent team of software engineers.

If you are looking for such technical AI resources, DevTeam.Space can help you. We have field-expert software developers and data science professionals who are experienced in developing market-competitive AI solutions using best practices for all kinds of industries. 

Some of these AI products built by developers at DevTeam.Space include Hit Factor, Recognition Of Air Signatures For Airsign, High-Speed Vehicle Identification System, etc.

You can get in touch with our project managers via this quick form. Simply enter your initial requirements for AI enterprise solutions, and one of our experts will get back to you to answer any more questions in detail. They will also link you with expert machine learning developers to implement AI enterprise solutions efficiently.

Frequently Asked on Questions on How to Implement AI in Enterprise

What developer expertise is required to introduce AI in enterprise business?

For effectively architecting AI in enterprise systems, your software engineers should be experts in prominent AI domains like machine learning, deep learning, cognitive learning, etc., apart from having excellent skills in handling and preprocessing big data.

What are some open-source platforms to implement AI in enterprises?

Some opensource AI frameworks include,
Google’s TensorFlow,
Amazon Sage Maker Neo,
Scikit-Learn,
Microsoft Cognitive Toolkit,
Theano,
Keras, etc.

What are some challenges in adopting AI in enterprise businesses?

Limited knowledge and expertise in AI technology, complexity in big data and data silos, lack of AI development tools, etc. pose limitations to adopting AI in enterprise processes.

Tell us about your challenge & get a free strategy session

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