From POC to Production Overcoming Challenges in Scaling AI Solutions

Overview & Market Insights  

Deploying AI solutions can affect your business. Thus, AI should be implemented to simplify your work not to work against you. To ensure viable development of AI solutions, imbibing the POC (Proof-of-Concept) is the initial AI adaptation plan that ensures project success

Initially, AI-powered solutions are built and leveraged to speed up the process and gain accuracy through automation. However, this is just the beginning, AI offers a vast scope in terms of scaling AI solutions.

Certainly, aligning with the scope of AI involves challenges that need to be overcome. So, in this blog, we will be moving ahead with the steps involved while scaling AI solutions and potential challenges to overcome ensuring project success integrated with the right ideation and inclusion of AI algorithms.  

Potential Steps for Successful AI Scaling

Collaborate with data science partner  

Start by partnering with data science experts. Understanding your AI scaling needs and defined business objectives, they can design custom data models helping you drive innovation and increase efficiency across your processes.  

Identify the right data sets  

To train the data models, identifying and applying the right data sets is essential to ensure the seamless and accurate performance of data models. These data sets can include employee records, customer data, transaction history, research reports, etc.  

Engage with stakeholders  

Allow your AI partner to engage with your stakeholders across departments including operations, finance, customer service, and others. This engagement will help your AI partner to develop AI models aligned with your unique business needs.  

Optimizing data sources  

To train and validate AI models, it is important to have relevant, structured, and up-to-date data. Collaborating with a trustworthy team of data scientists can help by developing secure and scalable data structures to streamline integrating and updating associated data sources.  

Consider a project solving your pain area  

Prepare your AI implementation roadmap defining your business objectives, overcoming specific business challenges, and aligning to deliver specific business results. Start with choosing a project that is more likely to enable a winning situation and ensure the highest success rate. Such projects can include customer support/service, payroll management, human resource management, etc.  

Adhere to governance & compliance  

Make sure that your AI partner is ensuring the inclusion of built-in governance features. To ensure seamless compliance and industry regulations, it is crucial to include AI algorithms accordingly which is again your AI Partner’s role.  

Leverage the right collaboration tool  

Use of right tools to ensure seamless collaboration among the data science team, development team, and other stakeholders can communicate and ensure streamline progress of the project. This can help businesses to stay updated with project flow, progress, and feasibility.  

Monitor the performance of AI models  

Tracking and monitoring the AI models is imperative for successful AI scaling. End-to-end tracking for custom AI models can be performed considering key metrics including speed, associated costs, value to users, etc.

With this real-time tracking and monitoring, businesses can quickly come up with potential issues that can be conveyed to the data science team to be resolved in a timely manner.  

Challenges to Overcome While Scaling AI Solutions  

Scaling AI solutions in your organization can be challenging, thus, it requires strategic planning, resource allocation, and other careful considerations. Here are potential AI deployment challenges and ways to overcome them ensuring seamless scaling and adaptation of AI solutions:

Data security  

The performance of AI solutions totally depends on data that can be uploaded from various formats including text, images, PDFs, videos, etc. Thus, ensuring data security, data mining, and effective database management are the core necessities to ensure the seamless performance of AI models and get data-driven results. 

This requires significant investment in scalable data storage solutions such as cloud-based data storage and data security solutions to secure data across the channels. So, yes, investing in scalable solutions is the major challenge when it comes to scaling AI solutions. Thus, it is recommended to keep a budget for data security and scaling data storage.   

Team collaboration  

To support the iterative process, ensuring team collaboration is another challenge to overcome while scaling AI solutions in your organization. This team collaboration can involve your internal stakeholders from different departments, data science team, design & development team, and other business experts.

So, it is important to streamline the collaboration between internal and external stakeholders. This can help and ensure that your AI-solution is addressing your business challenges and aligning with your business goals.  

Choosing right tools  

Making use of the right tools can lead to successful project execution. Such tools can include – development tools to be used by AI Engineers and IT/database team, and KPI monitoring tools to be used by business users to track and measure the AI outputs.

To overcome this challenge, most of the concerns will be taken care of by your AI Partner. However, they can also help you align with the tools to ensure seamless tracking and monitoring of AI outputs.  

Finding the right AI partner  

Certainly, finding the right partner can always be a substantial challenge for businesses from adapting proof-of-concept to scaling AI solutions. So, it is inevitable that your AI solution partner should comprise a team of data scientists to develop custom machine learning models and train the same to deliver the defined AI outcomes.  

Determining the project scope  

While extending from POC to production, it is essential to consider and prioritize your project scope. Instead of going in one go, determine your manageable project scope. This will help avoid potential disruptions.

This can ensure step-by-step success while developing the expertise of handling the involved challenges to be leveraged in further project scopes.  

Project time frame  

While moving from the proof-of-concept stage to scaling the AI solutions requires significant time, effort, and investment. Depending on your scaling AI project complexity, it may vary as it includes efforts in terms of data preparation, integration, and tracking/monitoring of the AI outputs. However, taking recourse of automation software and open-source tools/libraries can help data scientists/AI engineers to speed up the process.  

Planning to Scale Your AI Solution? Scale with Applore Technologies as Your AI Partner!  

When businesses decide to go beyond the POC and plan to scale the AI solutions across their organization is a paramount challenge that requires a strategic approach and careful consideration. However, partnering with the right scaling AI solution provider can help businesses ensure a seamless AI implementation roadmap.

Partnering with Applore Technologies, you can effectively align with your business goals. Whether you want an AI-based mobile app solution or want to deploy scaling AI solutions, we can help.

Facilitated with a team of AI Engineers, Data Scientists, UI designers, development, and quality assurance experts, we can help you scale your AI solution addressing your core business challenges, fulfilling gaps, and streamlining your day-to-day process flow.

To understand how Applore Technologies can help you align with your business goals through our Scaling AI solutions and overcoming AI deployment challenges, connect with us at +917838711340