While data science can be crucial to maintaining profitability, many executives face serious challenges in implementing AI projects. Shortages of data science talent, lack of strategy that incorporates AI, and time to build and deploy an AI project are all factors that cause obstacles in deploying AI.
Kumesh Aroomoogan, co-founder and CEO of Accern, here offers his insights as to how companies can move past these obstacles using no-code AI.
From technology to healthcare, manufacturing to retail, and airlines to finance, artificial intelligence has been one of the fastest emerging technologies adopted by companies in nearly every industry. Since 2018, the market for AI has grown at a steady rate, with the IDC predicting that AI will reach $97.9 billion by 2023. Thanks to AI-centric companies like Adobe, Oracle, and Salesforce, who have each introduced AI at the foundational level in managing customer relationships, the benefits of AI and automation are no longer foreign to businesses.
AI is necessary for businesses to stay competitive in today’s tech and data-driven world. Recent studies show that the demand for AI grew exponentially, especially within the financial services industry. Looking to the future, AI will lead the stage in innovation and growth within the financial services industry. Along with AI’s innovation and ROI, other driving factors for AI adoption include a skills gap and shortage of subject matter experts.
Data science is crucial to maintaining profitability, but many enterprises face challenges such as a shortage of data science experts and software engineers, lack of strategy that incorporates AI, and time to build and deploy an AI project. With the disconnect between the rising need for AI and the lack of resources to execute, no-code AI is critical for bridging this gap. Organisations should be able to tap into the benefits of data, which are growing by the day, without being pressured to fill data science roles that often surpass the number of qualified data scientist candidates.
In addition to the lack of talent, manually building out an AI model requires a significant amount of time and technical expertise. Studies show that building a single AI model takes an IT team 12 to 18 months on average. Furthermore, 80 percent of data scientists’ time is spent on finding, cleaning, and reorganising huge amounts of data, while only 20 percent is spent on actual data analysis.
There is a solution to the problem though: to empower non-technical teams with the ability to deploy pre-trained AI models and build AI models from scratch without having to write a single line of code.
A no-code environment allows the end-user to implement AI without even being aware they are building out a highly technical process. Through simple point-and-click commands, an easy-to-understand user interface, and pre-trained models that can be customised to a user’s specific needs, businesses can reap the benefits of AI and ML without the time delays or manpower requirements that manually building out an AI model would normally take. Financial enterprises can then focus on achieving their objectives without worrying about the highly technical processes that go into achieving those results or the large development team needed to make them happen.
Data Is Only Getting More Complex – Accessing It Will Require A More Simple Approach
Over 2.5 quintillion bytes of data are created every day globally and studies show that the amount of data produced will surpass 463 exabytes of data each day by 2025. Data is the driving engine for financial analysis where a broad range of financial decisions are made including investment, lending, and underwriting decisions. With manual data research and extraction, human emotions, bias, and error can prevent financial teams from extracting the right information from the noise and obtaining the most value from the information out there.
On a no-code AI platform, pre-trained AI models are already trained on high-quality, premium data that has been standardised from financial analysts. Since the back-end of a pre-trained AI model is already built out, the end-user can reap the benefits of accessing premium financial data sources and deploying AI models without having to worry about the technicalities. Additionally, users can customise AI models by retraining the pre-trained models using standardised data to solve a specific problem.
No-code AI is a low-cost solution for financial teams to obtain insights quickly by separating the relevant information from the noise and building out complex models to generate investment insights. Given all the benefits that such platforms provide, ranging from lower development costs to faster time-to-value, there are also several misconceptions around the no-code movement. One such misconception is that these platforms are not a fit for more advanced data science teams. However, the ability to quickly test multiple hypotheses is at the core of any AI project. No-code AI platforms, at the very least, solve that problem by providing a clickthrough interface that allows advanced data science teams to configure and test multiple models in a fail-fast workflow.
Financial firms who are looking to navigate away from highly manual processes, are looking for more efficient ways to optimise financial data, and are facing difficulties hiring the right data scientists are all good candidates for no-code AI. On the other hand, companies that have already built out their process internally and have large teams of advanced technical experts who are used to coding may feel that no-code AI is a drastic change in the process. Organisations looking to only reconfigure and tweak their code may feel that throwing out an internal structure and adopting a completely new framework may not be the most efficient or cost-effective solution.
Depending on where an organisation is in its AI journey, no-code AI platforms offer benefits specific to the end-user. Therefore, it would be in the best interest for financial teams that are evaluating no-code platforms to identify the use-cases, stakeholders, problem areas, and criteria for success according to the end-user and his or her objectives. This action-oriented framework will help accelerate the innovative roadmap for data science teams as they identify areas where they can leverage these platforms as-is versus where they have to do additional work.
The enterprises that can reap the benefits of both tech and no-code worlds will gain the competitive advantage.