Artificial Intelligence

How to Use AI in Your Small Business: Applications, Myths, and Adoption Tips

In this article, we explore how AI can help small businesses to increase efficiency, enhance employee experience and make better strategic decisions.

When talking about AI, we usually imply that it will be used on a grandiose scale — saving lives with AI-assisted surgical tools, predicting historic events, driving autonomous cars, and what have you. In a corporate realm, business leaders are all over it and artificial intelligence consulting is in demand like never before. Not only does it help enhance a multitude of business processes but it also serves as a great tool to win customer loyalty and solidify competitive supremacy.

But what about smaller businesses or startups? Can they use this emerging, often confusing technology that even large corporations sometimes fail to implement correctly? Let’s explore the most potent use cases of AI for small business and how it can help increase efficiency, enhance employee experience, improve sales, and make better business decisions overall.

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AI Meets Small Business

AI & ML usage by U.S. small businesses

Powering Customer Relationships

Customer relationship management platforms are essential for businesses of any size. Tracking interactions with every customer via phone, email, or social media is a fundamental part of day-to-day service. Nowadays, customers expect businesses to know about them more than ever before. Providing personal recommendations based on previous interactions has become a standard of any B2C industry, and answering questions before customers ask them is what often separates thriving companies from the rest. As customers become more accustomed to all-permeating recommendations on media streaming platforms and online marketplaces, their expectations are rising, making businesses adjust their approach.

Some individuals tend to believe that the complete replacement of sales professionals by various AI-enabled tools is inevitable, which, however, is a very shortsighted point of view. We don’t expect AI algorithms to be able to make sense of thousands of complex emotional cues happening in real time. One of AI’s role here is to complement the human’s unique skillset by automating routine tasks.

Manual data entry is a notorious roadblock to a satisfying CRM adoption. It often leads to erroneous data, missed information, and wasted hours.

Main challenges of CRM use

On the contrary, AI-assisted tools can analyze both text and audio data from interactions with customers and automatically add this information to the CRM. This is exactly what Salesforce's Einstein Activity Capture feature does.

AI’s capabilities in the CRM realm are broad. The aforementioned Einstein can also identify the intent behind text messages by utilizing natural language processing (NLP). This alone can significantly improve efficiency and enable faster decision-making by prioritizing ‘hot leads’ and giving them the most attention first.

By sourcing customer information from various sources, including browsing data and past interactions, AI can offer valuable contextual suggestions to a sales rep who is about to pick up the incoming call from a customer. To go even further, AI can also predict which string of actions is likely to result in a closed deal. With ever-evolving AI algorithms, recommendations become more precise and the rationale behind those recommendations becomes more explainable, bypassing AI’s black-box problem.

Calls per se transmit very limited information about the caller. This, in turn, often forces even experienced sales reps to guess which direction of the conversation to take at any given time. The Cogito Intelligence Platform, for example, eliminates the guess factor by analyzing emotional states of calling customers. Cogito’s AI algorithm analyzes key indicators of an effective conversation, such as empathy, tone, and participation, and converts those insights into a color-coded meter. For an experienced communicator this is like a goldmine, as understanding the emotional state of a caller makes interactions with customers much less complex.

As customers become more accustomed to all-permeating recommendations on media streaming platforms and online marketplaces, their expectations are rising, making businesses adjust their approach.

Automating Customer Communication

At the same time, we have thousands of much less sophisticated customer inquiries. For example, many customers tend to double-check a restaurant’s open hours even if they are clearly stated on the official website or social media. With the latest advancements in chatbot development, such questions can be easily answered without allocating human resources.

This can significantly increase customer satisfaction as chatbots are available 24/7 and raise overall efficiency as service reps can dedicate their time to more strategically important tasks. Table reservation inquiries, hotel bookings, questions about product availability, or shipment information can be effectively processed by both textual and voice-enabled chatbots. Deep learning chatbots are even able to imitate real-life conversations by formulating answers to human queries based on upstream data collection.

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Assisting HR

With the word ‘human’ at its core, paradoxically, HR is largely about data. Conventional HR tasks such as employee performance assessment or job applicants’ skillset evaluation come down to thorough analysis first and only then to decision making. AI can again reduce time spent on finding best-suited candidates by automatically wading through hundreds of applicants and prioritizing them based on a certain set of parameters.

Especially relevant for SMBs, Google’s Hire applicant tracking system is set to reduce time-consuming tasks by highlighting the most important words in resumes, automatically scheduling interviews, and calling candidates without manually entering their phone numbers. Automatic scheduling help recruiters plan out multiple interviews and conduct them asynchronously using AI-enabled interview software.

From the hiring perspective, AI can also help writing better job descriptions. Better, in the case of recruitment, means ‘attracting exactly the type of candidate we are looking for’ or ‘widening the candidate pool by using broader job descriptions’. For example, Seattle-based Textio uses AI to help recruiters analyze text to detect which words tend to appeal to a particular gender in a specific context. Eliminating those words from the job description makes the candidate pool more diverse.

AI can also analyze employee experience within a company to predict which individuals are likely to leave, identify top performers, and adjust employee management strategies overall. For example, Worklytics uses AI to reveal employee engagement drivers and suggest to managers the best actions to take in a specific case. The software gathers data from various popular business tools including G Suite, Slack, and Office 365, and analyzes it to uncover hidden patterns that impact employee satisfaction.

Managing Cash Flow Intelligently

Thinking about starting your own small business? Consider that 20% of SMBs fail in their first year, and 50% fail in five years, according to the Bureau of Labor Statistics. While these figures don’t seem that aspiring, small businesses often make the same mistakes. The second most mentioned reason for failure among SMBs concerns cash flow. This area remains out of complete control for many business owners, resulting in detrimental mistakes like delaying a loan payment, failing to predict creditworthiness, and misunderstanding sales trends.

AI can be this rational, cold-hearted finance assistant capable of predicting cash flow risks and helping in their mitigation. QuickBooks, an app developed by Intuit, uses AI to provide cash flow recommendations based on 90-days predictions. Whether it’s a short-term loan that might save your business or a strategic employment recommendation, QuickBooks offers advice based on real data.

Gaining Competitive Advantage with AI

For a small business, a single competitive advantage can go a long way. Quite often, many rival businesses operate in the same niche with a very narrow target audience. Every customer can make a difference. With similar products or services, competitive advantage goes far beyond simple marketing strategies and price adjustments. The first step toward gaining a real competitive significance is doing a thorough competitor analysis.

Crayon, for example, uses AI to convert the most important data from companies’ websites into insightful reports. Machine learning coupled with natural language processing enables Crayon’s software to filter data and distill it into insights. These reports are further used to create effective marketing and sales strategies. Essentially, it eliminates the need for manual research, leaving managers with more time to spend on data-based strategizing and decision-making.

Debunking AI Myths

Small businesses rarely explore non-conventional solutions like AI. The confusion AI causes is understandable. Not only this technology requires deep research to fully understand it, but it also remains mostly unexplored in many niches. Unless an area’s key player uses the new technology and continues to succeed, others become more willing to adopt.

When it comes to the implementation of potentially revolutionary solutions that require sufficient training and flexibility, though, this ‘follow the leader’ situation is much less likely to happen for small businesses. However, the underuse of AI tools among SMBs is what makes this technology so potent for gaining competitive advantage. Now let’s explore some common myths that small businesses hold regarding this technology.

It's Too Expensive

While often true for the corporate world and governmental organizations, AI adoption for small businesses costs significantly less. The costs of AI implementation among enterprises are comparatively astronomical as their business models often require regulatory compliance, carefully tailored software, specifically trained AI algorithms, management of sensitive information, and a dedicated development team on board.

Small businesses don’t even have to invest in a development team as many ready-made AI solutions are already on the market. Even if a small business needs a custom approach, it will most likely use open-source machine learning frameworks, which imply a reasonable price point.

It’s for Big Business

SMBs often hesitate to tap into AI, fearing they don’t have enough data. Your local skate shop has trillion times less data than Amazon, no doubt. However, the majority of SMBs don’t realize how much information they really have and that they don’t need the same amount of data as Amazon.

Data often remains idle and disorganized, scattered across different sources. This is why you need to thoroughly prepare for AI adoption by investing in data organization first. Moreover, keep in mind that ready-made AI solutions typically use cloud data scouted from other businesses in similar niches.

It’s Too Complicated

Yes, to squeeze the most value out of AI applications, it’s a must to sufficiently learn its basics. However, the hardest part about AI is developing the algorithms behind it, which has nothing to do with effectively using those tools. Most AI-powered software available on the market has user-friendly and well-thought-out UI. In most cases, complicated AI processes are happening behind the scenes. Software vendors that offer AI-enabled tools also tend to provide training materials along with it, which enables smooth user onboarding.

The low level of AI adoption among SMBs is what makes this technology so potent for gaining competitive advantage.

Adoption Tips

With all the seemingly infinite advantages of AI, any initiatives in this regard should be thoroughly analyzed and carefully deployed. Small businesses are especially prone to making mistakes.

AI may seem like a kind of Swiss Army knife cutting through all the challenges small businesses face. However, consider this as another investment for your business to tackle one particular problem. The most effective AI solutions stem from actual business challenges. Many AI solutions are flashy, elegant, and fascinating to talk about, but they provide little value.

Figuring out the reason for AI implementation is the most important part of your adoption plan. In many cases, choosing to approach a particular problem without AI at all is the best decision. How exactly will the deployment of an AI solution provide ROI and how soon? The common trap that ambitious small businesses fall into is implementing AI to ‘find insights’. Insights, however, never directly increase your sales or market reach, but decisions based on those insights do.

Next, establish a proper data governance infrastructure. Hiring AI developers without a clear data plan is a sure way to waste money and resources. Moreover, data-based technologies will disrupt your niche sooner or later. Being prepared for this revolution with all the data being clean, structured, and consolidated in one place will get you a long-term competitive edge.

Unless there is a very specific use case, there is no need to rush AI implementation right now. Instead, focus on the research and begin preparing your organizational culture and data for the inevitable coming of mainstream AI.

Deploying AI solutions without a clear data plan is a sure way to waste money and resources.
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