How to build an successful Ai software for multiple businesses

 

Building AI software for business can be a challenging but rewarding task. There are many steps involved in creating AI software, such as:

Clarifying your AI business objectives: 

Clarifying your AI business objectives is the first and most important step in building an AI software. It helps you to define the scope, purpose, and direction of your AI project.

To clarify your AI business objectives, you need to answer some questions, such as:

1. What is the problem that you want to solve with AI?
2. Who are your target users or customers and what are their pain points or needs?
3. What are the benefits or value that your AI solution will provide to your users or customers?
4. How will you measure the success or impact of your AI solution?
5. What are the challenges or risks that you may face in developing and deploying your AI solution?
6. What are the assumptions or hypotheses that you have about your AI solution?
7. How to evaluate available data?

You need to define the problem you want to solve with AI, the value proposition of your solution, and the metrics to measure its success.

• Evaluating available data: You need to collect and prepare the data that is relevant to your problem and that can be used to train your AI model.
• Choosing the right tech stack: You need to select the tools and frameworks that suit your AI project, such as programming languages, libraries, platforms, etc.
• Investing in IT infrastructure: You need to ensure that you have the hardware and software resources that can support your AI development and deployment, such as servers, cloud services, GPUs, etc.
• Building an AI team: You need to hire or collaborate with experts who have the skills and experience in AI development, such as data engineers, data scientists, machine learning engineers, etc12.
• Big data analytics: You need to analyze and visualize the data that you have collected and processed, and extract insights and patterns that can help you build your AI model.
• Deploying ML models into AI software development: You need to design and implement your AI model using machine learning algorithms and techniques, such as supervised learning, unsupervised learning, deep learning, etc.
• Testing of software and ML models: You need to test and validate your AI software and ML models using various methods, such as unit testing, integration testing, performance testing, etc

How to Build AI Software

While every AI project is different, these are the typical phases in building an AI software product. It is recommended to start small with a prototype to test your ideas and earn more budget that would support more ambitious goals.

1. Clarify Your AI Business Objectives

Don’t just chase the shiny new trend. Analyze how emerging AI technologies can truly bring value to your business. Do they solve any of your pain points that cannot be solved otherwise? What are their genuine contributions?

A lot of businesses fail in their AI undertakings because they let FOMO (fear of missing out) squander away their resources on costly but poorly fitted and futile AI projects. When it seems everyone else is doing it, think critically about why you want your own AI software. The above section may help.

2. Evaluate Available Data

Once you have nailed down the justifiable business objectives, consider your available data. Is your data siloed or inconsistent? The current state of your data will affect your choice of the tech stack and the skills you will need. Your business objectives and the available data significantly impact the scope of your project, and thus, the amount of investment. Therefore, ensure the first two steps are properly done - they justify the “why” of your project.

3. Choose The Right Tech Stack

The right tech stack should satisfy your project requirements. This includes programming languages, development tools, testing tools, cloud services, and big data solutions. You can only narrow down your options when you are clear about your project requirements. Your AI team (see below) should be able to advise on these AI solutions.

4. Invest in IT Infrastructure

Training machine learning models eat up a lot of computational resources. You may need to invest in extra servers, storage, and network to ensure the AI operation does not disrupt your current operation. If your business already has in place updated technologies such as cloud computing and data analytics infrastructure, then scaling them up to make space for AI capabilities is a reasonable expectation. Meanwhile, those with legacy systems will need to modernize before they can get started with AI projects.

Moreover, having an AI-ready infrastructure makes it easier to attract and build an AI team.

5. Build an AI Team

Three types of roles comprise a comprehensive AI team: business, data analytics, and software engineering.

The business and engineering people may be familiar with working together on previous non-AI projects, but the data specialists are likely new to the team. Consequently, the AI team needs to figure out how best to work together, by answering questions such as:

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• How should AI projects be managed?

Consider this within the context of your company. If your company favors the Waterfall project management methodology, will it be sustainable for AI projects with high risks? To manage the risks of AI project failure, how big is the expected learning curve for your organization?

How experienced are team members in working with AI?

AI is still a new technology requiring a major shift in mindset; adding to this is a global AI talent shortage. It is a big ask to expect that your company can compete against Big Tech for an entire team of AI experts. It is normal to have team members who will be working on an enterprise-level AI project for the first time. Make sure you do have some experienced AI team members working alongside others who are willing to start their own AI journey.

Concepts of Big Data Analytics:

Big data in the real world

Big data analytics helps companies and governments make sense of data and make better, informed decisions.

• Entertainment: Providing a personalized recommendation of movies and music according to a customer’s individual preferences has been transformative for the entertainment industry (think Spotify and Netflix).

• Education: Big data helps schools and educational technology companies alike develop new curriculums while improving existing plans based on needs and demands.

• Health care: Monitoring patients’ medical histories helps doctors detect and prevent diseases.

• Government: Big data can be used to collect data from CCTV and traffic cameras, satellites, body cameras and sensors, emails, calls, and more, to help manage the public sector.

• Marketing: Customer information and preferences can be used to create targeted advertising campaigns with a high return on investment (ROI). 

• Banking: Data analytics can help track and monitor illegal money laundering.

Types of big data analytics-

There are four main types of big data analytics that support and inform different business decisions.

1. Descriptive analytics

Descriptive analytics refers to data that can be easily read and interpreted. This data helps create reports and visualize information that can detail company profits and sales. 

Example: During the pandemic, a leading pharmaceuticals company conducted data analysis on its offices and research labs. Descriptive analytics helped them identify unutilized spaces and departments that were consolidated, saving the company millions of dollars.

2. Diagnostics analytics

Diagnostics analytics helps companies understand why a problem occurred. Big data technologies and tools allow users to mine and recover data that helps dissect an issue and prevent it from happening in the future.

Example: A clothing company’s sales have decreased even though customers continue to add items to their shopping carts. Diagnostics analytics helped to understand that the payment page was not working properly for a few weeks.

3. Predictive analytics

Predictive analytics looks at past and present data to make predictions. With artificial intelligence (AI), machine learning, and data mining, users can analyze the data to predict market trends.

Example: In the manufacturing sector, companies can use algorithms based on historical data to predict if or when a piece of equipment will malfunction or break down.

4. Prescriptive analytics

Prescriptive analytics provides a solution to a problem, relying on AI and machine learning to gather data and use it for risk management. 

Example: Within the energy sector, utility companies, gas producers, and pipeline owners identify factors that affect the price of oil and gas in order to hedge risks.

Deploy ML Models into AI Software Development:

The software engineers deploy ML models to build AI software during this phase. Since the risks in AI projects are high, adopting Agile into the software development cycle is better to manage risks at every phase. Specifically, the AI team should follow MLOps.

MLOps provides the framework for a software team to incorporate ML models into development. MLOps is an extension of DevOps, a practice that improves software quality with continuous feedback between operations and development.

Essentially, MLOps facilitates the collaboration between the AI software team and operations, enabling a comprehensive workflow that traverses software, hardware, and IT support. Its ability to break down silos between departments is a quality that will benefit AI software development.

Testing of Software and ML Models:

Just as software improves in quality through rigorous testing, ML models also benefit from continuous testing. This is because ML models are continuously updating itself with new data without human interference. Without frequent monitoring, datasets will degrade, and biases may be introduced into the datasets.

Key Roles In an AI Team:

As mentioned above, an AI team or department consists of three key roles: business, data, and engineering. Data literacy is the leading quality that any AI team member needs, no matter their position. It is easy to get confused about new technologies. Training programs should improve the team’s competencies in working with big data, so that they share the same understanding and can communicate data as meaningful information to external stakeholders.

In terms of soft skills, the most important ones are communication and teamwork, because AI software projects demand team members to work with highly collaborative frameworks to be successful.

Business Roles

These are the project managers, product managers, product owners, business analysts. They are responsible for:

• Keep the AI team focused on the project requirements
• Ensure the AI project satisfies the business objectives and stay within time and budget constraints.
• Work with stakeholders such as legal, accounting, marketing, and investors, while preventing the AI team from getting isolated from the rest of the company.
• Represent the interests of the end-users whose feedback and education will be crucial to the business success of the AI project.

Data Specialists

As mentioned above, depending on the state of your data, your AI software will need a number of data specialists: data engineers, data scientists, data analysts, and machine learning engineers. This list is not exhaustive; other roles may include database architect and data architect. They should have a mix of technical skills in maths, statistics, programming tools, cloud computing, database management, and other data-related skills.

AI Software Engineers

You may be familiar with these software engineering roles: front-end engineers, back-end engineers, testers, QA engineers, UI/UX designers, and so on. More importantly, software engineers for AI projects should have experience with or are willing to adopt DevOps and MLOps.

Conclusion:

If you want to build AI software for sentiment analysis, your problem statement could be:

• Many businesses struggle to understand the emotions and opinions of their customers from online reviews, feedback, surveys, etc.
• This leads to missed opportunities for improving customer satisfaction, loyalty, and retention.

Your value proposition could be:

• An AI software that can automatically analyze the sentiment of any text and provide a score and a label (positive, negative, neutral) for each sentence or paragraph.
• This will help businesses to gain insights into their customers’ feelings and preferences, and improve their products or services accordingly.

Your SMART goal could be:

• To develop and deploy an AI software for sentiment analysis within 6 months that can achieve at least 90% accuracy on a benchmark dataset and generate actionable reports for businesses.