Generative AI offers enormous potential, but complexity, cost, and uncertainties currently hold back AI adoption. Strategic changes to funding, talent, and mindset can help smaller companies begin benefiting from this powerful technology in targeted ways that minimize risks.
1. Complexity of Generative AI Models
The complex machine learning techniques behind generative AI models like GPT-4, DALL•E 2, and Stable Diffusion are difficult for non-technical small business owners to grasp. Terms like “large language models” and “diffusion models” have little meaning for entrepreneurs unsure how tools that create images, text, and other content could solve problems within their industry.
This lack of understanding breeds uncertainty, leading many small firms to dismiss generative AI altogether without properly evaluating its potential benefits. However, with education specifically around real use cases tailored to their needs, entrepreneurs can demystify generative AI technologies and see how they may tackle certain issues within their businesses. Ultimately, practical demonstrations and targeted examples may do more to convince smaller companies than lofty technological claims – if generative AI can be shown to help solve real, specific problems they face every day.Small businesses and entrepreneurs need use cases, not jargon. Click To Tweet
2. High Upfront Costs Hinder AI Adoption
The expense of implementing generative AI deters many small businesses. AI models require powerful GPUs or cloud computing infrastructure that most small firms cannot afford, with commercial solutions costing thousands per month. In addition to software licenses and AI APIs, smaller companies must invest in data collection, hardware, staff training, and more to properly utilize generative AI.
While these upfront costs intimidate most small businesses, options exist to make generative AI more accessible. Some AI vendors offer payment plans spread over time rather than large lump sums. Meanwhile, several free or low-cost generative AI alternatives are emerging that can meet the needs of many smaller companies.
3. Concerns About Reliability and Accuracy of AI Content
Small businesses depend on high-quality, error-free results that will not damage their brand reputation or customer trust. However, uncertainty surrounds how consistent, accurate, and unbiased outputs from generative AI models are. Data issues, instability in neural networks, and other factors can reduce the reliability of AI-generated content.
These concerns worry smaller firms that do not have the resources to weather mistakes caused by unreliable generative AI systems. To minimize risks, small companies should start small with generative AI initiatives, meticulously test outputs, and partner with AI vendors that provide transparency into their models, data, and human-in-the-loop capabilities.
4. Difficulties in Acquiring AI Talent Slow AI Adoption
The skills shortage makes it challenging for small businesses to hire and retain AI specialists to implement generative AI tools properly. There aren’t enough trained generative AI professionals to meet demand, and larger companies often outcompete smaller firms for top talent.
However, there are creative solutions small businesses can employ to overcome this hurdle. They may train existing staff, outsource certain generative AI tasks, partner with freelancers or graduate students, or even crowdsource AI development. While imperfect, these tactics can help smaller companies leverage generative AI technologies without entirely depending on bringing full-time AI experts on staff – at least as an initial stepping stone.
5. Potential Security Risks and Data Issues
Handling and securing the massive datasets required to train powerful generative AI models presents potential risks to customer data and intellectual property that worry small businesses. These firms typically prefer tools they can fully understand and control, which is difficult with complex “black box” AI systems. Data breaches or inappropriate use of customer information enabled by generative AI could also damage trust and harm smaller companies operating on tight margins.
While no system is completely secure, small businesses can mitigate risks by anonymizing sensitive training data, limiting access to AI models, meticulously vetting potential AI vendors, and avoiding sharing customer data whenever possible. Transparency around AI data management is also key to reassuring customers and building trust.While no systems are completely secure, small businesses can mitigate generative AI's data risks through careful policies and procedures. Click To Tweet
6. Return on Investment for Small Businesses
The potential return on investment for generative AI remains unclear for many small businesses. There is skepticism that the expense of adopting these systems will provide worthwhile benefits that outweigh the costs. This uncertainty arises from a lack of education and real-life examples of successful generative AI implementations tailored specifically to the needs and resources of smaller companies.
Adopting a testing-first approach can help determine whether generative AI truly provides value for a small business. Initial pilots and proofs-of-concept focused on addressing specific problems or workflows can demonstrate tangible benefits that justify further investment. Over time, identifying and scaling up only the most impactful uses of generative AI within a small firm’s unique context can optimize its return on investment.
The reasons why small business struggle with AI adoption are complex. However, smaller companies can start benefiting from generative AI – but in ways scaled to their needs and resources. The keys are starting small, testing tools thoroughly, combining alternative funding with training current staff, and focusing on practical benefits rather than hype to determine if generative AI genuinely helps solve problems within your small business.