Council Post: Mastering the AI Maze: Insider Strategies for Tackling Implementation Challenges
As the emergence of Dall-E and Chat-GPT brings Artificial Intelligence to the limelight, it’s no surprise that companies are looking for creative ways to make use of this limitless power. AI isn’t simply a technology for specialists anymore – businesses can now fully leverage its advantages regardless of their technical skill level. 86% of business leaders see AI as an essential part of their daily operations, and they’re reaping the rewards in terms of increased efficiency and productivity. AI empowers decision makers with lightning-fast speed and accuracy, eliminating tedious manual data entry tasks along the way. With this level of intelligence on your side, unparalleled market opportunities will be within reach. As McKinsey’s global survey indicates, 50% of businesses have already integrated AI in at least one domain, with the adoption rate predicted to double in upcoming years. However only 11% of organisations leveraging AI have experienced notable ROI. Why is this so? As AI Advances, So Does the Role of Data Engineers and Scientists Picture this— just a few years ago, data engineers and scientists were merely responsible for maintaining databases and extracting useful insights from gathered business intelligence, but as technology advanced and AI solutions became a rising priority, a storm began brewing, leaving them completely blindsided. CTOs, data engineers, and scientists are facing the challenge of keeping up with the constantly changing technology landscape, which involves exploring and managing new algorithms, architectures, and solutions in both open-source and industry domains. Meanwhile, they are also grappling with the need to master best practices.The world of data has transformed into a fierce beast that is difficult to tame, and it is catching everyone off guard. Big universities and training camps have yet to catch up to the tech, leaving data engineers and scientists with no choice but to piece together knowledge from scratch and learn through trial and error. Data scientists need to access a specific table to build predictive AI models.But it doesn’t stop there. They will still face challenges such as figuring out the meaning of column names and dealing with missing values. These challenges can cause delays and confusion in the modelling process. The role of a data scientist has turned into an adrenaline-pumping race against time and the need to always be one step ahead in a field that is expanding towards uncharted territories. Data science is morphing into a new hybrid role, merging with technological advancement to shape the industry. In this article, I will explore three common challenges that enterprises face when implementing AI while providing practical solutions. 1- Challenge: Data Shortage Machine-learning models often require vast, diverse datasets to function optimally, but obtaining such data remains a challenge. Insufficient data can lead to overfitting and biassed models eventually leading to poor performance. Data shortages result from data privacy constraints, a lack of historical data for new trends, small sample sizes, and the costly, time-consuming nature of manual data labelling. Acquiring permission to use sensitive data is challenging, and historical data often proves inadequate due to evolving trends and processes. However, adequate training data is essential for creating unbiased and accurate ML models capable of addressing a wide range of scenarios. Solution: Synthetic Data Generation Synthetic data many times serves as a viable solution to help overcome this challenge. When generated artificially to resemble real datasets, synthetic data can reveal hidden patterns, interactions, and correlations between variables, offering a substantial foundation for ML models. Balancing datasets and improving performance, it can supplement marginal classes without jeopardising privacy. Advancements in synthetic data have significantly increased its value for machine learning models. Techniques like generative adversarial networks (GANs) and Wasserstein GANs (WGANs) foster the creation of more realistic data while maintaining compliance and data balance. We used synthetic data to develop an AI tool that could monitor the brand uniformity of posters outside automobile showrooms in India. By using CycleGAN, we generated data for both brand compliant and non-complaint cases, allowing us to successfully train and deploy the AI model. 2- Challenge: Talent Shortage 75% of decision makers prefer to help upskill current staff and 64% favor recruiting experts to bridge the AI talent gap. However, budget limitations and retention challenges are significant barriers to these solutions. Solution: Low Code and No-Code Platforms Enterprises are increasingly embracing low-code/no-code platforms to democratise app development and ease workloads. Faced with an estimated 85.2 million global software engineer deficit by 2030, businesses have found that low-code/no-code tools can increase their value by millions of dollars without hiring additional IT developers. For instance, our no-code platform streamlines the entire process of AI model training for pharmaceutical clients, including feature selection and deployment. What used to take a team of data scientists 2 months now just takes 1 week. Gartner has projected that the low-code technology market is set to reach $44.5 billion by 2026. This is due to a number of factors, such as an increasing demand for more rapid application delivery, persistent talent shortages, and the proliferation of hybrid workforces. Low-code platforms have become an essential element of successful hyper automation, with 50% of all new clients expected to come from business buyers outside the IT organisation by 2025. However, to achieve their full market potential, low-code/no-code platforms must continually innovate in areas like real-time iteration, DEVops workflows integration, scalability and API development. 3- Challenge: High expenditure AI investments have reached nearly $118 billion in 2022 and surpass $300 billion in the next few years. The cost of AI varies, with companies paying anywhere from $6,000 to over $300,000 for custom solutions. Few factors that influence the costs include the type of AI software (chatbots, analysis systems, or virtual assistants), whether the enterprise requires a pre-built or custom solution, additional features, and how the platform will be managed (in-house or outsourced). Project duration and complexity also impact the overall cost. Solution: Optimising Deployment Techniques In addressing the pressing issue of high costs associated with AI implementation for enterprises, it becomes crucial to optimise data processing methods applicable to multiple forms of data, including videos, text, and other pertinent information depending on size and nature. By adopting strategic and efficient deployment techniques, it is possible to achieve substantial cost reduction. Using specialised hardware for video processing and taking advantage of edge computing solutions can present a more economical option compared to solely relying on cloud-based solutions. Taking it a step further, a hybrid deployment can be implemented based on the specific use case and scenario, with a portion of the solution deployed on a dedicated server and the remaining on the cloud. This multifaceted approach not only simplifies data management processes but also significantly reduces the overall expenses involved in integrating AI in today’s business environment. Similarly, if you opt for an AI API, you’ll be charged an invoice for every digitization process. However, with open source or commercial models, you can deploy the model without the need for API-based payments, resulting in long-term cost savings. Developing and deploying customised models for businesses is ultimately a more cost-effective option than relying on AI APIs. Final Thoughts Organisations looking to take advantage of AI need an intentional and methodical approach that combines user interfaces, regulations, data infrastructure, data storage solutions and labelled datasets. Once these systems are in place, enterprise-focused machine learning algorithms can be trained with structured and unstructured datasets. Ultimately, successful adoption of AI results in operational efficiencies as well as critical insights that can foster growth and success for any organisation willing to embrace this technology. This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill out the form here.
Organisations looking to take advantage of AI need an intentional and methodical approach that combines user interfaces, regulations, data infrastructure, data storage solutions and labelled datasets.
Organisations looking to take advantage of AI need an intentional and methodical approach that combines user interfaces, regulations, data infrastructure, data storage solutions and labelled datasets.
["synthetic data generation"]
["data science", "machine learning", "artificial intelligence", "AI", "chatbots", "ML", "virtual assistants", "RAG", "Aim", "analytics", "synthetic data generation"]
["AI", "artificial intelligence", "machine learning", "ML", "data science", "analytics", "Aim", "RAG", "chatbots", "virtual assistants"]
Rahul Thota is the Founder &amp; CEO of Akaike Technologies. He is a data scientist at heart. He and his team of 50+ Data scientists are passionate about solving complex unstructured data problems that impact quality of human life. He has published multiple IEEE papers and holds a handful of international patents.