Balancing Speed and Quality in AI Development: 4 Best Practices for Workplace Integration

Artificial Intelligence (AI) is a hot topic in the tech industry. Tech giants have already adopted AI, with companies like Microsoft, Google, and Salesforce integrating it as an intelligence layer across their whole tech stack. Also, many other businesses around the globe are starting to embrace it.

They are hiring more data scientists and increasing dedicated budgets in an effort to adopt AI, helping them stay ahead of the curve. From automating processes, reducing errors, and improving customer experience and sales, there’s so much that AI can do for your tech company to help you achieve top-line growth while maintaining bottom-line expenses.

In fact, a PwC report reveals that global GDP will increase by 14% by 2023 due to AI. However, integrating AI projects effectively into an organization can be challenging, especially for CEOs who need to balance speed with quality in AI development. So how does your tech company integrate AI while ensuring high-quality outputs? Read on for our four best practices. 

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Context: AI in the Workplace & Thought Starters 

AI development involves building software systems that simulate human intelligence processes, including thinking, learning, making decisions, and anticipating and solving problems. Businesses can apply AI anywhere from sales, marketing, customer support, manufacturing, operations, human resource, and more.

For example, Google Maps uses AI to help you reach your desired destination. Amazon uses it to recommend products for shoppers, and social media companies like Facebook and streaming services like Netflix use AI to suggest content to users, increasing customer retention.

More recently, Amazon implemented an AI system at a Washington convenience store using cameras to track how customers interacted with items and lower checkout line times. Twitter uses it to detect potential terrorism and hate speech instances within user content. 

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AI can offer your business a wide range of benefits, giving it a competitive advantage. It:
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Tech businesses that scale AI across the company integrating it into core company processes, workflows, and consumer journeys, realize the most value out of the technology.

However, scaling AI from the proof-of-concept stage to the enterprise level can be challenging for many businesses. It involves complex algorithms and a significant investment of time and resources. Many companies struggle with balancing the need for speed with the need for quality in AI development, leading to errors, mistakes, and costly failures. This can pose significant risks to businesses, which must ensure they deliver high-quality products and services to remain competitive in today's market.

For example, a financial company lost 20,000 USD  in 10 minutes after one of its machine learning models misbehaved. The company had no clarity into the main issue and couldn't tell which of the models was malfunctioning, forcing them to pull back the plug. They rolled back all models to prior iterations, severely deteriorating performance and losing them weeks of effort. 

So, tech leaders must be actively involved in the AI development process, pushing for the shifting mindset and cultural change needed to scale AI and develop a strategic vision for the company to create, deploy and manage AI apps quickly and efficiently. 

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Looking to hire top nearshore AI software developers? Learn more

Best Practice #1: Set Clear Goals And Expectations

The first step is to define the objective of the AI project and desired outcomes. To identify areas that would benefit from an AI project, you can look at areas holding back business growth. For example, your company may be struggling with hiring. Your company can maybe add a new software program to automate hiring, which will, in turn, grow your company.  You can brainstorm with your human resource team to learn what they think would best help you reach these goals.

Next, communicate the project’s expectations to your team and everyone involved in the project. What do you hope to achieve from automating your hiring process, and for how long?

Also, define the project’s success criteria and track progress against the criteria. Success criteria examples would be to reduce hiring time by half and lower the HR team’s workload within six months.

Finally, handling many projects simultaneously can be disastrous since it’s hard to keep track of what’s effective and what’s not. Start off small and slow and handle one project at a time, taking time to track whether it’s positively impacting your business; then, you can apply your learning to a second project. 

Best Practice #2: Establish A Quality Assurance Process 

AI software development requires developers to use training data to teach models to process and make conclusions in a manner that satisfies the hyperparameter configuration. So, an AI model is reliable, accurate, and unbiased only if the data it was trained on also has all of these stages. 

For the training data to fit the model, the data itself must be tested for quality, validity, reliability, and completeness. This involves identifying and eliminating any kind of human bias. That’s why implementing a rigorous software testing and validation process is a must-have.

Also, ensure that the quality assurance process is transparent, easy to understand for all stakeholders, and includes appropriate documentation and review processes.

Finally, many companies are not ready to have their AI models run independently and wholly without human intervention. So, conducting audits and reviews regularly is critical to avoid flaws that could cause harm while identifying any areas for improvement. 

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Best Practice #3: Create A Cross-Functional Team

Assemble a cross-functional team of high performers with different expertise and backgrounds to ensure reliable and stable deployment of AI applications.

AI scaling needs a broad skill set range, including engineers (specializing in machine and deep learning, cloud, and DevOps), data scientists, data engineers, cybersecurity and IT specialists, project managers, and more.

More importantly, tech staff must be connected with business professionals who would decide on specific use cases based on business needs. To ensure that the team rapidly detects and fixes issues and embeds learnings easily across the application life:
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• Encourage collaboration and communication between team members and ensure that all stakeholders are involved in the process.
• Ensure the team has the resources and support necessary to achieve the project goals.
• Make sure your team continuously learns about AI implementation and success cases.

Best Practice #4: Leverage Agile Development Methodologies

To achieve success, the AI model’s design, build, test, and deployment must be done correctly and in that order. As such, QA engineers use agile methodologies throughout the software development life cycle and test all growth in small and iterative increments, ensuring sure that the product always responds to the relevant goals. Also, foster a positive communication environment, so your team can confidently provide feedback and solutions. 

Why TECLA is the Best Partner to Fulfill Your AI Development Needs

AI is taking center stage in the tech industry. So, it is essential that your tech business partners with an organization that understands your needs and the growing AI landscape. TECLA will match you with the right nearshore AI software developer that will skyrocket your company’s goals. Here’s why you should work with us: 

• TECLA has over 40,000 pre-vetted tech professionals across 18 LATAM countries.
• Hiring becomes seamless and hassle-free because we handle legal, payments, contracts, HR, and talent retention. 
• Start receiving potential candidate profiles in record time- within 48 hours.
• Get high-impact results with short ramp-up times.
• Access senior talent on demand, scaling up and down as required without long-term commitments. 
• Enjoy zero-risk hiring with our 14-day trial period and a 90-day guarantee.

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FAQs on Balancing Speed and Quality in AI Development