Growth and profitability opportunities for small and medium-size enterprises (SMEs) using AI far exceed the challenges. According to a recent Constant Contact survey, 91% of SME respondents using AI said it has made their business more successful and 28% said they expect it to save them $5,000 or more in the coming year. The business environment is technologically growth-oriented, as the performances of the AI giants have shown via the recent stock market moves and valuation increases. SMEs play a vital role in the global supply chain and need to take advantage of AI to achieve return on investment (ROI) and their business goals.


Programmers and developers use various tools like machine learning models, natural language processing, computer vision, and neural networks to create AI algorithms. Today, generative AI systems respond to user queries by generating content. AI impacts SMEs’ productivity and efficiency by enabling automation and enhancing the workforce’s speed and decision-making processes. SMEs’ most common uses of AI are in data analytics tools, automating tasks and creating content using generative AI applications such as ChatGPT and Bard.


The aspects of SMEs’ business processes that can benefit from AI are growing as the technology continues to evolve. Additionally, SMEs that have adopted AI can respond more quickly than competitors to take advantage of strategic opportunities and avoid or mitigate potential risks.


Educating Employees


Before AI adoption, SMEs should educate employees on its capabilities, limitations, accuracy, and potential biases in workflow integration. Defining the role of AI tools within team tasks is essential. In manufacturing, when AI-powered robots are installed, employees should understand how to collaborate with them effectively, focusing on tasks that play to human strengths while leveraging robotic process automation for routine jobs.


Employees using AI tools must familiarize themselves with data security and privacy concerns. In healthcare, where AI aids in diagnosing medical conditions, employees must be aware of privacy regulations and the importance of safeguarding patient data during the AI-enabled diagnostic process.


Having a contingency plan is necessary in case AI tools don’t meet expectations. For example, a logistics company adopts AI for route optimization; if the AI’s recommendations consistently lead to delays, employees should be prepared to revert to conventional routing strategies as needed.


Also, implementing AI tools necessitates ongoing training and skills development. Effective communication within the organization is essential when incorporating AI tools, e.g., when a financial services company introduces AI-driven portfolio management, employees need to communicate transparently with clients about the uses of AI in investment decisions and the continued presence of human expertise.


AI used in HR functions must comply with employment laws; other statutory and regulatory requirements; and diversity, equity, and inclusion issues. Communicate clearly during the implementation that this will help employees optimize routine tasks and allow them to focus on nonrepetitive tasks.


Communication with Customers


Websites can be built with AI chatbots and virtual assistants to communicate with customers via email, social media posts, press releases and media announcements, product data research, pricing requests for vendor selection, automated resolutions of requests or complaints, and order updates. SMEs can use such tools to personalize their marketing and customer service with customized content creation.


A recent example of the use of AI is a huge safety measure in the swimming pool at the Ann Arbor, Mich., YMCA. AI technology integrated with the YMCA’s existing closed-circuit TV cameras to alert the lifeguards on their smartwatches when a distress signal is observed.


Suppliers and Data


AI is often applied to supply chain management. When executives use AI to research and communicate with suppliers, they should be transparent about data collection and usage, obtain explicit consent and explain benefits, collect only necessary data, ensure accuracy, provide easy access to tools, and implement proper controls.


Also, management should implement strong data security and privacy protection measures, use encryption for data protection, control access to authorized personnel, prepare for cyber incidents, and train employees tasked with supply chain management. Another factor to consider in using AI is applicable regulations related to suppliers, so a company should stay compliant with industry regulations, seek legal advice, maintain records, and sustain customer and supplier rights and privacy policies.


Management Processes


Managers can employ AI in various activities, e.g., automated routine processing in accounting. Managers implement AI-driven cybersecurity solutions to monitor data and network activities in a digital era with a huge variety of cyber threats. Through AI, they can detect unusual activity in information systems, issue early warnings of breaches, and signal new opportunities.


Also, managers can use predictive modeling for planning. Companies can improve labor and support efficiencies using predictive maintenance (PM), prepare for weathering uncertainties, and build a resilient business model. A polyvinyl chloride company in southern Ohio faced the prohibitive cost of a factory line shutdown and start-up for reactive machine breakdowns. Managers installed machine learning-based control systems to alert and coordinate their PM program.


Environmental, Social, and Governance


The use of AI has implications for companies’ environmental, social, and governance activities. For example, AI can optimize energy consumption in various sectors, such as manufacturing, transportation, and buildings. Smart systems can adjust energy usage based on real-time data, leading to reduced waste and costs. Conversely, AI model training and operations can be computationally intensive, consuming significant energy, but advancements in energy-efficient hardware and algorithms are mitigating this concern.


AI-powered sensors and data analytics can enhance resource management in agriculture and water distribution, leading to reduced waste, improved yields, and sustainable practices. However, the production of AI hardware can contribute to electronic waste if not managed properly. AI can analyze large data sets to predict and manage natural disasters, helping small businesses’ early warning systems and response coordination.


An example of social impact is when AI aids in medical diagnoses, drug discovery, and personalized medicine. It can improve patient outcomes, especially in remote areas with limited medical resources. That said, concerns arise regarding privacy, data security, and potential biases in AI algorithms used for healthcare decisions.


Weigh the Pros and Cons


AI can automate repetitive tasks, increasing efficiency and allowing humans to focus on creative tasks and strategic planning. The downside is that job displacement can occur in specific sectors, requiring reskilling efforts to avoid potential societal disruption.


SMEs must evaluate familiar challenges. Most notably, AI prompts questions related to data quality, biases, the ROI of any significant investments, a shortage of financial and human resources, integration with existing systems, ease of scalability, security and privacy concerns, ethical and legal issues, social acceptance, and organizational culture as resistance to change or fear of job elimination. Despite risks and challenges, AI is here to stay. To be part of the technological revolution sparked by global integration of AI tools, it’s paramount that SMEs conduct cost-benefit analyses and strive to take advantage of the strategic use cases of AI tools to spur growth and outmaneuver their competition.

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