The era of GenAI is just beginning, and early pilots are compelling to watch. Since their inception, ChatGPT, Copilot, and Midjourney have captured the current public attention with their ability to revolutionize knowledge work across various industries and business functions within the economy, motivating people to explore its capabilities. In areas like sales, marketing, customer operations, and software development, its potential to reshape roles and enhance performance is evident, potentially unlocking immense value amounting to trillions of dollars across sectors ranging from banking to life sciences. Consequently, a wider range of stakeholders, especially SMBs and midmarket firms, confront generative AI’s implications for business and society.
Unveiling the GenAI Landscape for SMBs
According to data from a recent Techaisle survey, for 53% of small businesses, GenAI has become a top priority, up from 41% in April 2023. Overall, 60% of SMBs and 84% of mid-market enterprises have already integrated or intend to integrate generative AI into their operations within the upcoming six months.
This blog will focus on offering a distinct perspective that provides crucial considerations for SMBs delving into GenAI adoption and the challenges that might lay on their path, and finally, emphasize how adopting a strategic approach is vital to capitalize on its extensive capabilities fully.
Decoding GenAI for SMBs
Modern generative AI signals a massive leap in the evolution of AI. It can transform the fundamental nature of work owing to its ability to understand natural language and excel in tasks like data reorganization and classification. SMBs are particularly keen to capitalize on this aspect to automate repetitive tasks, reducing manual dependency and instead using that time to focus on activities like product development.
According to a recent Forbes report, around 16% of SMBs (including companies employing 1-499 employees) today have less than 20 employees, which makes efficiency a top priority. With GenAI, SMBs can achieve more with fewer resources.
For instance, GenAI is finding applications in fueling personalized chatbots for enhanced customer support, leading to improved customer satisfaction and retention. Furthermore, it’s reshaping email communication dynamics by empowering businesses to create more impactful and tailored messages, leveraging the capabilities of generative AI-driven language models. Additionally, SMBs can analyze customer data for better sentiment analysis, helping retain and attract new customers and quickly scale capabilities.
Navigating the GenAI Landscape: Strategic Considerations
Modernization is crucial for 95% of mid-market enterprises, with 60% actively investing in this pursuit. Nevertheless, almost half of these companies encounter hurdles when selecting the appropriate “AI-first” strategy and recognizing existing data and application interdependencies needed to train their large language models (LLMs).
Create Business-specific Datasets to Train the LLM
Moreover, SMBs require assistance collecting diverse data, leading to training datasets that might lack scope and representational diversity since they are not equipped with extensive resources. This limitation could result in biased model-generated content due to the constrained dataset, impacting the ability to ensure fairness and inclusivity in outputs. To counter this, SMBs must create and train their GenAI models using comprehensive and impartial datasets.
Address the Skills Gap for GenAI Experts & Competitive Demand
SMBs and midmarket enterprises encounter another hurdle: recruiting and retaining AI expertise. These businesses may need to cultivate in-house proficiency to fully comprehend and harness AI solutions’ potential. However, the competitive demand for AI experts in the job market, coupled with the influence of larger corporations, can make assembling a skilled team proficient in deploying and managing GenAI technologies challenging.
Ensure Effective Data Collection and Unification Approach
To optimize generative AI, SMBs need to develop a strong data collection plan. Consolidate diverse data sources, ensuring a structured and accessible organization. This will streamline integration and establish a robust AI model foundation.
Create a Robust Cybersecurity Strategy
Additionally, there are cybersecurity challenges to address. Generative AI reduces the barriers for hackers without deep security expertise, allowing them to carry out effective attacks. Those new to this field may target SMBs, viewing them as less defended than larger enterprises. Therefore, businesses interested in adopting generative AI should thoroughly evaluate the technological aspects, weigh their resources, and strategically analyze their capabilities to make a well-informed choice.
Check Integration with Existing Systems
Assess the alignment of generative AI models with existing systems. The effectiveness of AI performance relies on its smooth integration with the existing infrastructure. Opting for a “rip and replace” approach that hinders data integration might pose a notable disadvantage.
Real-world Gen AI Use Cases
Signaling a step change in how work is done and understanding the technology’s potential could help companies go a long way in successfully adapting and implementing it. Beyond its potential value in specific use cases, generative AI can reshape organizational dynamics by revolutionizing internal knowledge management systems.
Leveraging its remarkable natural-language processing capabilities, generative AI can empower employees to access stored internal knowledge through human-like queries and ongoing dialogue. This quick access to pertinent information can significantly enhance decision-making and strategy development. It has been estimated that knowledge workers spend about a fifth of their workweek seeking information, making generative AI assistance highly impactful. With the ability to efficiently “read” vast corporate libraries and collaborate with humans for refined research, generative AI offers a scalable solution that surpasses traditional methods.
Enhance Customer Operations
Generative AI can transform customer operations by boosting customer experience and agent efficiency through digital self-service and skill augmentation. It’s gaining ground in customer service for automating natural language interactions. A study on a company with 5,000 agents showed that generative AI increased issue resolution by 14% per hour, reduced issue handling time by 9%, and reduced agent attrition and manager requests by 25%.
(Erik Brynjolfsson, Danielle Li, and Lindsey R. Raymond, Generative AI at work, National Bureau of Economic Research working paper number 31161, April 2023)
Moreover, generative AI can elevate quality assurance and coaching by extracting insights from customer interactions, identifying areas for improvement, and providing agent guidance.
Transform Marketing and Sales
The swift adoption of generative AI is evident in marketing and sales, where personalized text-based communication plays a pivotal role. It creates tailored messages based on customer interests, reducing content creation time. Its ability to customize content at scale enhances customer engagement, conversion, and retention. Additionally, it aids in multilingual campaigns with specific visuals and messaging.
Generative AI tackles marketing challenges by interpreting varied data sources like text and images. It optimizes data for customer profiling and channel recommendations, extracting insights from unstructured data like social media.
Both B2B and B2C companies can utilize generative AI for sales transformation. It prioritizes leads with consumer profiles and suggests engagement strategies while also generating product information and conversation scripts for effective follow-ups.
Elevate the Banking Experience
Banks recognize generative AI’s potential across their frontlines and software operations. Pioneers are utilizing ChatGPT and domain-specific tools, especially for software and knowledge applications. Moreover, GenAI tools could significantly elevate customer satisfaction, enhance decision-making and employee experience, and mitigate risks by improving fraud and risk monitoring.
Three use cases underscore its industry value.
- Enhanced employee performance by augmenting skills with a virtual expert
- Accelerating code development to minimize technical debt and boost software delivery
- Rapid generation of customized content
Revolutionize Product R&D
Generative AI’s potential in R&D is often overshadowed by its impact in other business areas. Pretrained foundation models, whether through fine-tuning or not, extend beyond single-task optimization, accelerating time to market and diversifying generative design applications, enhancing productivity by swiftly producing design options and improving the designs themselves. For instance, generative AI can optimize material usage and manufacturing design, leading to cost reductions, enhanced product quality, and quicker testing through scenario drafting and testing candidate profiling.
Exploring GenAI tools
As SMBs embrace the technology, enterprises can offer more potent AI tools, like CRM and campaign management integrations, to address a broader spectrum of SMB challenges. Even generative AI tools like GitHub’s Copilot, now featuring ChatGPT abilities, can significantly enhance software engineering speed and efficiency, enabling cost-effective custom feature development.
Moreover, software engineers use GenAI to create, refine, and validate software code sets. An essential application is quality assurance, where generative AI models manage tasks like bug fixes, test generation, and diverse documentation needs. Notable instances encompass Code Snippets AI, ChatGPT, Google Bard, and Tabnine.
Generative AI is also employed in coding various apps and creating product documentation. Additionally, foundational models and APIs drive the creation of new generative AI models and products, such as customer service chatbots based on OpenAI models. Examples include MOSTLY AI, Stability AI, AI21 Labs, and GPT-4.
GPT-3 powered tools such as Fireflies AI notetaker allows the creation of personalized messages customized for specific roles in marketing, customer service, sales, and other areas. GenAI tools, such as Twain, Salesforce Einstein GPT, and HubSpot ChatSpot, can also generate outbound marketing campaigns that boost upselling and cross-selling strategies.
Embracing GenAI with Clarity and Strategy
Technology has transformed business practices, with text-based AI at the forefront. While initial generative AI investment focuses on text applications like chatbots and translation, about 20% of potential value lies in multimodal capabilities beyond text.
GenAI holds immense potential but comes with challenges. Pinpointing AI’s process automation role is vital for small and medium-sized businesses (SMBs) seeking integration. There’s no one-size-fits-all; tailored solutions are critical based on industry specifics. Despite resource constraints, SMBs can reap GenAI benefits, addressing limitations if harnessed wisely. As with any technology, proficiency and proper utilization remain pivotal for successful implementation.