Crunching Time to Market with Agile AI Solutions
AI is now a dominant driver for many industry-wide use cases. However, despite its ubiquitousness, AI implementations face quite a few stubborn challenges, such as time-to-market and scaling. These challenges can be addressed by shortening the overall development cycle of an AI solution through the various stages, such as identifying the problem area, acquiring data, preparing the model, running it, testing it and finally developing it on the scale, taking up to 4-5 months to show business impact. In a progressively competitive and fast-evolving data-driven world, it is becoming more and more necessary to be agile in adopting AI-driven solutions. Organizations are trying to get around this problem by reusing highly customized in-house developed solutions across problem areas. But building customized solutions comes with challenges in decision-making and individual efforts spent on each problem. In addition, it requires iterations, validations, and coordination across multiple technology and business teams. Implementing product-based solutions also has associated logistical challenges such as licensing, IP rights and other paper pushing. Pre-built AI as a faster, more reliable, and cost-effective solution Using pre-built specialized targeted solutions that can be quickly customized to different datasets is known to reduce the turnaround time and be cheaper and more dependable. Furthermore, since the solution is pre-built, it also solves the logistical challenges for the end user while also making it easier for them to use the core solution across multiple problem statements. Pre-built solutions also offer speed and agility through quicker model building and scaling using MLOps, creating a loop of continuous testing and improvement of AI models. With pre-built AI solutions, modularity is also an added advantage. Since the solutions or parts of solutions are originally designed to fit in with each other, it is easier to add new capabilities and features over an existing one in a comparatively shorter time. Also, traditionally, to build a new solution, the technical team had to consider existing infrastructure, tech stack and other nitty-gritty. But recent advances in open-source technologies enable service providers to make largely technology-agnostic solutions, making pre-built solutions an easier plug-and-play alternative. That being said, it is not a cookie-cutter approach. Problem identification and diagnosis still need business acumen and perspective, which requires competent functional expertise. There also needs to be dedicated efforts spent on exploratory data analysis (EDA). Moreover, the standardized nature of these solutions necessitates the templatization of input and output data. Even then, the pros far outweigh the cons, leading to major data solution providers drifting towards pre-built AI solutions. Deploying pre-built AI solutions Companies are adopting pre-built AI solutions across use cases such as marketing, promotion optimization, pricing, and budget decisions. Forecasting is also one area of interest for data solution companies which could be on the supply side (the price of commodities) or the demand side (sales, volume). For instance, in the traditional approach to demand forecasting, a firm, either via an external vendor or in-house forecasting department, had to understand the historical demand data, prepare it, and choose algorithms/models which work best (some examples are regression, time series, ARIMA, etc.), and then finally validate the outcome. This is even before tackling the scaling phase and deploying, which comes with its own sets of problems. However, since the core approach to modelling a demand forecast is quite similar, it is one of the best use cases to apply the modular approach of a pre-built AI solution. Once the historical demand input data is standardized to be fed into the model, only the validation and testing need human intervention. Implementing MLOps also ensures that the model is self-correcting with the availability of new data as opposed to the traditional approach, which requires turning occasionally. As analytics and AI permeate more and more into the business world, firms will need faster results to see the impact. The advent of pre-built AI solutions makes it possible for businesses to work with agile models and reduce the time to show business impact by 60% compared to the traditional way of approaching AI/ML solutions.
The advent of pre-built AI solutions makes it possible for businesses to work with agile models and reduce the time to show business impact by 60% compared to the traditional way of approaching AI/ML solutions.
The advent of pre-built AI solutions makes it possible for businesses to work with agile models and reduce the time to show business impact by 60% compared to the traditional way of approaching AI/ML solutions.
["Deloitte"]
["Go", "TPU", "Deloitte", "programming_languages:R", "AI", "data-driven", "ML", "MLOps", "analytics", "GAN", "R"]
["AI", "ML", "analytics", "MLOps", "TPU", "R", "Go", "GAN", "data-driven", "programming_languages:R"]
Prashanth Kaddi is a Partner with Deloitte India, in the Consulting business working on analytics and cognitive. He has 20 plus years of experience in technology, analytics, product management and strategic marketing. He has worked with Fortune500 companies across Asia Pacific, India, Europe, US and Latin America solutioning and delivering data-to-information solutions. As a result, enterprises have transformed business models, increased top line and bottom line, deepened customer engagement, and enabled better risk management through AI/ML solutions. Prashanth is also a part of the global leadership team of the Deloitte AI Institute, driving AI as a strategic growth engine.