AI Implementation Strategies: Building, Customizing, or Integrating?

“Building an AI solution from the ground up is the only way to secure a competitive advantage”, some business owners argue, and they couldn't be more wrong.

Unlike just a few years ago, there is now a plethora of off-the-shelf AI options designed for specific domains. What's more, this selection continues to grow as the global adoption rate of AI continues to rise, currently being at 35%. But while the idea of building a custom AI solution is appealing, it also can be resource-intensive and, in many cases, is optional rather than necessary.

At Flyaps, we focus mainly on building and implementing AI solutions. Thanks to our track record of successful cases, we’ve got enough firsthand experience to help you learn everything there’s to know about AI implementation strategies. Further on, we will unveil three main AI implementation strategies: building, customizing and integrating through gen AI tools. We will explore pros and cons of each approach and discuss some real-life examples of such solutions. Sounds interesting? Then, keep reading.

Building custom AI solutions

When talking about AI implementation, building refers to the process of developing the basic AI solution using the expertise of an experienced software development team.

Now, imagine a telecom giant. It has a sprawling network spanning remote, rugged terrains where conventional solutions fall short. Their connectivity requirements are as unique as the landscapes they serve. To address this challenge, they have no choice but to embark on the journey of building a specialized AI system.

The company must assemble a team of top-tier AI and data experts, who will collaborate with their industry specialists. Together, they craft an AI solution that fine-tunes itself continuously, ensuring that signals are transmitted flawlessly across the most challenging terrains, thereby meeting the specific demands of their extensive network.

With that being said, let's delve further into the advantages and disadvantages of custom AI implementation.

Pros of building custom AI tools

1. Control: Building custom AI solutions provides organizations with maximum control over the development process, allowing them to create a solution that fully meets their needs and preferences.

2. Tailored to unique problems: Custom-built AI solutions are ideal for addressing particularly unique or complex problems that may not be solved by off-the-shelf solutions.

3. Intellectual property rights: Developing your own AI solution ensures that the technology and its benefits remain exclusive to your organization.

Cons of building custom AI tools

1. Resource-intensive: Developing custom AI solutions can be resource demanding, requiring skilled developers, top-end equipment, and substantial investment, which not all companies have readily available.

2. Time-consuming: The process of building a custom AI solution, from research and data preparation to coding and testing, can take a lot of time and may not yield meaningful results quickly.

3. Not always relevant: In some cases, businesses can achieve their AI-related goals by leveraging existing technologies, partnerships, or ready-made solutions, making the custom build option unnecessary.

So, let’s sum up what businesses usually benefit from this AI implementation strategy.

Companies that are best suited for this approach

Companies with unique or highly specialized AI needs that cannot be met by off-the-shelf solutions (for example, telecom giants) are highly suited to building custom AI solutions.

This AI implementation strategy is also great for businesses that plan to offer cutting-edge business intelligence products that require usage of AI.

On the other hand, smaller companies, or those with limited resources and tight deadlines, may find it more practical to explore options such as buying AI solutions or partnering with others to access the expertise and data they need. The choice between building, buying, or partnering should be based on the organization's specific needs and resources.

Case in point: building an ML-based resume parsing app for HR technology company

Relocate.me, a platform that connects IT professionals with job opportunities around the world, encountered a common problem – candidates were often rejected without an interview due to subpar resumes. As the platform's user base grew, manually improving resumes became increasingly time-consuming. The team recognized the need for an automated solution to efficiently improve technical resumes.

Relocate.me designed CV Compiler, an AI-powered tool that analyzes and improves tech resumes based on CV guidelines and a technical glossary. To develop a machine learning algorithm capable of analyzing resumes, the company turned to Flyaps for help.

The tool works in the following manner: CV Compiler effectively parses resumes, evaluates their content, and provides customized recommendations for improvement. Users receive unique advice on how to improve their resumes and increase their visibility to recruiters at top companies.

To achieve this remarkable result, Flyaps began by addressing the PDF analysis challenge. The primary format for resumes is PDF, which accounts for 80% of the resumes analyzed by CV Compiler. Analyzing PDFs presents difficulties due to encoding and formatting variations. Our team overcame this bottleneck with specialized libraries and used machine learning techniques such as NLP for accurate text extraction.

Flyaps also integrated a machine learning model with the CV Compiler to improve the accuracy of section identification. This integration achieved an impressive response time of 1.87 seconds, with potential for further improvement.

The development of the API for CV Compiler was a significant milestone, enabling the introduction of an API as a service business model. The product was optimized for CV Compiler customers, including Indeed, a global online job search platform. CV Compiler's flexible API adapts to specific business needs, allowing the extraction of custom features such as resume summaries.

As a result, CV Compiler serves as a B2B and B2C solution, attracting boot camps and over 50,000 IT professionals. Boot camps use CV Compiler to provide students with a powerful tool that significantly improves their employment prospects.

As you can see from the above, some initial internal custom solutions have the potential to become an additional revenue stream, as they can be used by other companies facing similar challenges.

Customizing off-the-shelf AI solutions

Think of Apple and its iconic iPhone. While Apple is the creative force behind the iPhone's design and its global marketing, it doesn't physically produce every component on its own. Instead, Apple collaborates with manufacturers around the world, each specializing in a particular part. For instance, there are factories responsible for crafting camera components, those dedicated to producing high-quality displays that grace the iPhone, and so on.

A similar approach goes for AI implementation, especially in the context of machine learning. The approach is pretty straightforward: you use a pre-built, off-the-shelf AI product, and customize it according to your business needs. Such an approach is often referred to as AI as a Service (AIaaS) and typically includes a simple user interface accessible through a website or a downloadable software product.

For instance, such tools can be used for extracting data required for forecasting. One famous example is Microsoft Power BI – an AI solution for custom data visualization. Within Power BI, users can leverage AI Insights, a set of pre-trained machine learning models that can enhance data preparation tasks. The tool is equipped with Power BI visuals SDK — an open-source, command-line tool based on NodeJS and available on GitHub то ensure customization.

AI-based browser extensions represent another category of solutions that can seamlessly integrate into a company's daily operations. Consider the case of Grammarly, a widely recognized AI-powered tool.

Grammarly, initially developed as a writing assistant for individuals, quickly gained popularity in the business world. In a corporate environment, businesses can tailor Grammarly to their unique industry terminologies, writing styles, and specific communication requirements. This customization ensures that the tool not only checks grammar and spelling but also aligns with the company's branding, tone, and communication standards.

Pros of customizing off-the-shelf AI solutions

1. Cost-efficiency: Customizing existing AI solutions is more cost-effective compared to building one from scratch. It saves you time and resources that would otherwise get spent on developing a solution from scratch.

2. Proven reliability: Established AI solutions have a proven track record, ensuring a high level of reliability and trustworthiness.

3. Proven functionality: Off-the-shelf AI solutions have already been developed and tested, meaning you can rest assured they function as intended

Cons of customizing off-the-shelf AI solutions

1. Limited customization: Customization is often limited by the features and capabilities of the solution of your choice.

2. Integration challenges: Integrating off-the-shelf AI solutions with existing systems and processes within an organization may pose challenges, particularly when dealing with legacy systems.

3. Dependency: Companies may need more control over the core functionality and development roadmap than when building their own AI product. Businesses can also become dependent on their provider, which could pose a risk if the provider experiences issues.

4. Licensing: While off-the-shelf solutions can be cost-effective initially, licensing fees and additional customization costs can accumulate over time, potentially making it less cost-effective in the long run.

Companies that are best suited for this approach

For businesses with constrained budgets and resources, customizing existing AI solutions is a pragmatic choice. It allows them to tap into AI benefits without the significant upfront costs and resource investments associated with building AI from scratch.

Companies that require specialized AI components or access to particular datasets often find this AI implementation strategy to be a convenient starting point. These solutions can be tailored to incorporate the required components, saving the company its time and budget.

In essence, the versatility and cost-effectiveness of customizing off-the-shelf AI solutions make this approach particularly well-suited to organizations that need a rapid and scalable solution for incorporating AI into their operations.

Case in point: an AI-based plugin for IT recruitment that allows companies to streamline hiring processes

GlossaryTech, an HR technology startup, approached Flyaps with a specific need: to develop an AI-based Chrome extension that streamlines the evaluation of candidates' IT expertise for IT recruiters. The client wanted to create a tool that would significantly simplify the resume screening process by automatically analyzing and scoring candidate resumes.

To solve this problem, Flyaps had to develop a solution that could understand human language data and accurately recognize technical terms, including multi-word expressions such as "Ruby on Rails" or "React Native". Our team chose to implement a natural language processing algorithm using the NLTK Python library and create a custom tokenizer to achieve fast and accurate text analysis.

The main challenge was to develop an algorithm that could extract and accurately interpret essential data from unstructured documents. This was particularly complex when dealing with technical terms and jargon. To overcome these challenges, Flyaps developed an NLP algorithm that referenced a technical glossary to understand the context and meaning of the language.

In addition, Flyaps developed a custom tokenizer optimized for identifying specific entities or phrases in the ICT domain, including multi-word expressions. Building this tokenizer allowed for greater control over text preprocessing, resulting in a more consistent and higher-quality analysis tool.

As a result, the extension has gained popularity with over 25,000 users, including major industry players such as Tesla, Microsoft, and Amazon.

Integrating generative AI solutions

The third approach to AI implementation is to use existing generative AI tools, such as ChatGPT or GitHub Copilot, an AI-powered code assistant, to seamlessly integrate AI capabilities into an organization's workflow.

Consider a software development company that specializes in building mobile apps for various domains. To improve their app development process, they decided to integrate GitHub Copilot to help their developers write code more efficiently and with fewer errors. The tool provides real-time code suggestions, automatically fixes common coding errors, and even offers solutions to complex programming challenges.

While this AI tool may not be a perfect fit for every unique aspect of the company's workflow, it has significantly improved the company's competitiveness. By integrating AI capabilities into the tools their employees already use, they improved overall productivity and code quality without the need for extensive AI development efforts.

Pros of relying on gen AI tools

1. Reduced development time: By streamlining the coding process and offering solutions for complex programming challenges, AI tools can speed up the development cycle, allowing companies to deliver products faster.

2. Accessibility: AI tools like GitHub Copilot are readily available and accessible, making it easier for organizations to incorporate AI capabilities into their workflows without requiring specialized expertise.

3. Cost-effective: Using existing generative AI tools is often more cost-effective than building custom AI solutions from scratch, as it eliminates the need for extensive development efforts and associated expenses.

Cons of relying on gen AI tools

1. Privacy and data security concerns: Integrating external AI tools may involve sharing sensitive company data, raising concerns about data security and privacy.

2. Dependency on third-party tools: Relying on external AI tools means the organization becomes dependent on the availability and updates of those tools, which could lead to disruptions in workflow if the tools are modified or discontinued.

Companies that are best suited for AI implementation via gen AI tools

Organizations that specialize in software development, like the mobile app development company mentioned in the example, can benefit from generative AI tools like GitHub Copilot to streamline their coding process and increase productivity.

Startups with limited resources can also use generative AI tools to accelerate product development process and reduce costs, allowing them to compete more effectively.

Small and medium-sized businesses (SMBs) that often face time and budget constraints can also use generative AI tools to streamline their workflows.

Case in point: how Panasonic Connect partnered with Microsoft and used the Azure OpenAI Service to create ConnectAI

Panasonic Connect, a division of the Panasonic Group, has partnered with Microsoft to leverage the power of the Azure OpenAI Service to bring ConnectAI to its 12,500 employees in Japan.

The decision to integrate AI into its daily operations stemmed from the belief that AI will become an indispensable tool for all business professionals. The primary goal was to empower employees to focus on creative and complex tasks, while delegating repetitive and time-consuming work to AI.

The foundation of ConnectAI, the Azure OpenAI Service, provided advanced language models such as OpenAI GPT-4, GPT-3, Codex, DALL-E, and Whisper. With these capabilities, ConnectAI was able to assist employees with a wide range of tasks, from drafting emails to coding, significantly increasing overall productivity.

ConnectAI was carefully designed to be easy to use and require minimal training. Users accessed the assistant through a simple URL and were able to ask questions in a way that they were used to rather than using traditional search terms.

In addition, Azure OpenAI Service introduced additional features that included responsible AI safeguards and content filtering to improve the overall experience.

After its rollout in February 2023, ConnectAI experienced rapid growth in user queries. The number of questions increased from 55,380 in the first month overall to 5,000 questions per day. The user base spanned various departments, from IT to legal and accounting, boosting the company’s overall performance.

How Flyaps can support your AI integration

As an AI-focused software development company, Flyaps offers comprehensive support for businesses in integrating cutting-edge AI technologies into their operations. Here’s what we can offer you.

• Personalized AI implementation strategy and consulting: Our team can assess your business needs, objectives, and existing systems to create a tailored AI strategy. This includes defining which AI solutions are most suitable and how they can be integrated to meet your specific business goals.
  
• Custom AI development: Flyaps can design and develop custom AI solutions that align with your business requirements. This might involve creating AI algorithms, machine learning models, or AI-driven applications.
  
• Data management: Effective AI integration requires high-quality data. We can assist you in data collection, cleaning, and storage to ensure the availability of accurate data for AI applications.
  
• Integration with existing systems: We can integrate AI solutions seamlessly into your current software and technology infrastructure. This might involve developing APIs or connectors to link AI components with existing applications.
  

Unsure about the most suitable AI implementation strategy for your business? Don't hesitate to reach out, and we'll gladly assist you in finding the right path!