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Generative AI Solutions for MSMEs: Making Adoption, Implementation, and Impact Super Easy

It is easy to believe that AI—specifically, Generative AI—is a technology for the big boys. This belief is magnified when business leaders like Jamie Dimon, the CEO and Chairman of JPMorgan Chase, speak about Generative AI. In his annual letter to shareholders early this April, Dimon remarked that “the consequences” of using AI “will be extraordinary.” He backed his statement by saying JPMorgan Chase was employing 2,000 AI and machine learning experts and data scientists and spending $12 billion yearly on technology, including AI.

The numbers can seem overwhelming. However, it isn’t just the financial services sector placing heavy bets on Generative AI. Every industry, from healthcare to manufacturing and legal, is under its transformative spell. A survey of 4,700 CEOs released at Davos in January 2024 provides the reason: The survey results showed that 45 percent of corporate leaders were concerned their existing business model would not prove viable in a decade without significant “reinvention.” A senior banking executive put the concern in perspective: “We haven’t had to rethink what it means to be a bank like this since the start of the Internet.”

The reality is that if a change applies to large corporations sooner rather than later, it also applies to Micro, Small, and Medium Enterprises (MSMEs). The encouraging news is that MSMEs can leverage Generative AI reasonably and practically just as quickly as any other business.

MSMEs already have satisfactory proof of their capabilities to absorb technology. The sector has shown it can squeeze considerable benefits from digital technologies. A report released in September 2023 by the Broadband Commission Working Group on Connectivity for MSMEs found that 85 percent of MSMEs using digital technologies had increased sales, and 81 percent said digital technologies helped them cut costs.

Why MSMEs Across Industries Must Adopt Generative AI

Generative AI can be the next Big Opportunity for MSMEs in the financial services, healthcare, manufacturing, and legal sectors. But first, they must cross a unique but not unexpected hurdle. They must stop assuming that Generative AI is a complex technology, overkill for their operations, and of little material relevance to their business today.

Let’s simplify the idea of using Generative AI. A single chip like the Nvidia A100, which powers most large AI systems, costs $10,000. MSMEs do not have the budgets or bandwidth to develop their own Generative AI models and integrate them with their systems. In this, they are fortunate. Not having the financial wherewithal means they won’t be tempted to go down the path that requires them to deal with Diffusion Models, Variational Autoencoders, GPU Acceleration, Pandas, TensorFlow, PyTorch—the complex tech stacks that go into the creation of a generative AI system. They can leave that to large enterprises—such as JPMorgan Chase—who want to deliver massive product differentiation.

MSMEs are less dependent on product differentiation. Their competitive edge lies in entrepreneurial abilities, optimizing resources, cost control, developing new services, responsive customer-focused communication, and scaling business capabilities. “How can these abilities be amplified?” should be the #1 question when an MSME thinks of deploying Generative AI.

Generative AI Use Cases across Legal, Healthcare and Financial Services

Three examples—from the legal, health care, and financial services industries—help understand how MSMEs can leverage Generative AI for immediate impact without extraordinary investments or management overheads.

Legal: Vakilsearch, a legal and tax compliance company in India with startups and SMEs as its primary client base, integrated a variety of data into a scalable system and used pre-trained legal models to understand tax and legal forms. The system can extract document types, recognize legal entities/parties/aliases, identify legal relationships and clauses, normalize/augment data (via EDGAR information), and de-identify/mask sensitive information.

Using Generative AI, Vakilsearch now outperforms comparable document classification pipelines by 9 percent and extracts identity card information with 87 percent accuracy, even while it moves closer to optimizing its resources.

Financial services: AI is already being used in the financial services industry to screen applicants for credit lending through scoring, determining the maximum credit limit a customer may avail of, and pricing loans based on risk and business rules. However, when the business wants to respond to loan applicants after the screening is complete, the task is labor intensive.

This need not be the case. When a loan application is rejected, small data sets and conditional Generative Adversarial Networks (GANs) can be used to deliver personalized, applicant-friendly explanations such as, “Please consider reapplying for a loan of a different amount that may better align with your income.” The aim is to become more responsive to customers, helping them improve awareness, resulting in better subsequent loan applications.

Health care: Physicians spend 57 percent of their time meeting documentation requirements, reducing the available time for actual patient care. Generative AI can rebalance this by copiloting clinical documentation using natural language processing and combining it with information extracted from lab reports, radiology scans, medical records, transcripts of patient-clinician conversations, etc.The process can also identify speaker roles and context, extract medical terms, and instantly deliver ready-for-review transcripts and clinical summaries from examinations and telehealth conversations. AWS HealthScribe, for example, allows users to do this without having to invest in Machine Learning or train health-specific LLMs. These systems can be integrated with existing documentation tools and processes.

Towards Building an AI-driven Culture

The most inspiring (as opposed to awe-inspiring) Generative AI implementations have been achieved by MSMEs using available tools. Leveraging existing tools is a clear path for MSMEs to overcome the need for massive investments and expertise that place hurdles in Generative AI adoption.

The challenge is to navigate the new tools that keep appearing every day. This can be achieved with the help of a specialized technology partner that has demonstrated expertise in crafting solutions using foundation models like GPT-4, Llama 2, Claude, Stable Diffusion, J2-Ultra, Orca2, etc., that help create uniquely customized services designed for MSME.

Generative AI offers MSMEs an unprecedented opportunity to improve and streamline their existing workflows, boost creativity, build an AI-driven culture, and enhance competitiveness.

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