AI for businesses of any size
Right now, we are entering an era where most businesses have access to technology that, once upon a time, could only have been found in science fiction.
Consumer-facing generative artificial intelligence (AI) — deep learning models that can produce new, high-quality content — has been democratized, and it’s going to have a major impact on how nearly every company functions. McKinsey estimates that generative AI could add at least $2.6 trillion to $4.4 trillion to the global economy each year.
At the same time, it’s never been easier to develop machine learning models — programs that can find patterns or make decisions using new data. ML can deliver answers that would have taken hundreds of staff hours.
These still-developing technologies have massive amounts of potential, but if you don’t have experience in this area, how do you get started? And how can you ensure that your investment of time and money delivers the results you need?
While it takes effort to build an AI and ML practice, it may be easier than you imagine. In this guide, we’ll lay out some of the basic principles for success.
The High-Level Benefits of Artificial Intelligence and Machine Learning
Discover the advantages of leveraging this technology in your business.
Enhanced Efficiency
AI doesn’t need coffee breaks, so it can output work product — whether that’s a segmented customer list, a thousand lines of code, a new marketing plan or the first draft of a client letter — much faster than a human being could. Even when the AI’s work isn’t quite as good as a professional’s, it can give human workers a head start on their jobs, cutting the total time required significantly.
Cost Reduction and New Revenue
Increased efficiency leads, in turn, to cost savings. Enhancing a single worker’s productivity could mean that you can stop hiring out work that would have gone to a contractor or consultant. AI and ML can also open up new opportunities for revenue generation by turning your stored data into consumer products that people will pay for. For example, maybe your company has produced decades of text-heavy scientific reports. Those could be converted into a paid data source.
Increased Accuracy
Assuming it is using clean and accurate data, a well-built application can deliver results that tend to be much more accurate than a person alone might deliver. This is especially true of predictive applications powered by machine learning. An ML model can consider many more variables, resulting in far greater predictive power.
Practical Innovation
Thanks to the explosion of new AI tools and frameworks, it’s never been easier to apply this powerful technology to the business problems of today. In the process, you could achieve results that once seemed unimaginable.
Putting AI and ML to Work in Your Business
These technologies can streamline processes, save time and uncover powerful insights.
Artificial intelligence and machine learning are already being used to solve the kinds of problems faced by businesses across the country — and are delivering significant value in the process. Here are a few real-world examples from several fields.
Customer Service
Chatbots: A huge number of companies have installed chatbots on their websites and apps to respond to basic questions at all hours of the day, seven days a week. Some B2B companies use their site’s chatbots to serve up product content targeted to each user’s unique needs and ask specific questions to qualify users as potential leads. Techniques with generative AI tools allow us to create chatbots that have the voice of your company.
Virtual assistants: Tools like Siri and Alexa go a step beyond basic chatbots. Not only can they answer questions, they can schedule meetings, set up calendar reminders, place e-commerce orders and much, much more. Even better, these engagements can be customized as the tools learn from interactions with users. Your company might consider a similar assistant to help your customers check on the status of their order, update their preferences, or purchase a new product.
Tone analysis & categorization: There are AI tools that will quickly analyze incoming customer emails, calls, or texts to classify incoming messages, understand the sentiment and highlight client problems that need to be resolved immediately. These can be configured to match your internally defined categories and sentiment preferences.
Sales and Marketing
Predictive analytics: An ML model can harness a company’s past data to make forecasts about future sales trends, predict where the business should expand next and decide where and how to spend marketing budgets in order to generate the greatest return. AI can also help you understand which variables are affecting sales, so you can then find ways to control those variables and improve your results.
Personalized marketing: AI can also provide greater insight into your customers, both individually — for example, by providing product recommendations based on their purchasing history and web browsing — and in groups. A model could help you identify which customers are at higher risk of churning and which ones might be an ideal audience for an upsell to a new product. An ML-powered lead-scoring project could be a great way to get started in this area.
Buyer intent: You can create an AI-powered tool to analyze recordings of sales calls and identify when a prospect says something that indicates higher interest — comments that might otherwise be overlooked.
Supply Chain & Logistics
Demand forecasting: Using past data, including order and sales history, you can predict demand for specific SKUs. Depending on how detailed your records are, you could even develop forecasts by ZIP codes. Many large retailers incorporate weather data in their forecasting to develop their product lineup.
Route optimization: Save time and fuel — and deliver orders faster — by using ML and mathematical optimization techniques to organize the most efficient delivery routes for your fleet.
Inventory management: Never get caught shorthanded again. AI can be used to monitor your levels of stock and, by predicting when it will be used up, placing reorders before you run out. You could create an AI tool that takes pictures of each item in your inventory to identify those with defects — a safeguard against sending broken items to customers.
Human Resources
Resume screening: AI has the ability to quickly analyze large numbers of resumes and use selected criteria to identify the most promising candidates for open positions, so they can be prioritized for followup interviews.
Employee onboarding: It’s possible to build a chatbot like tool that walk new hires through paperwork, training and other onboarding activities, freeing HR team members to focus on other work.
Predictive attrition: The same way that AI can be used to predict customer churn, it can also identify when an employee could be at risk of leaving the company, so managers can potentially intervene.
Financial Services
Automated accounting: AI & ML can streamline several parts of the accounting process – for example, suggesting how to classify expenses or identifying outliers in payroll or invoicing.
Risk management: You can use models to study market trends and forecast potential dangers, so you can take a defensive posture in your holdings.
Fraud detection: If your credit card company has ever flagged a purchase on your card, then you’ve had first-hand experience with fraud detection. Financial companies and eCommerce sites use sophisticated models and user data to identify transactions that are likely to be fraudulent.
5 Steps Toward a Successful AI Implementation
Set your project up for success with these proven strategies.
Every AI and ML project is unique, but the successful ones tend to follow the same standard guidelines. To ensure that your project achieves its goals — and avoids the most common points of failure — apply these rules to your work.
Gather the Right Data
Everything starts with data, but it must be clean and relevant. If your data is formatted incorrectly, if there are duplicates, if values are missing, that makes it impossible for your AI or ML application to function correctly. It’s also important to train your AI on the relevant data for making decisions. Including data that doesn’t apply to current conditions — like datasets from decades ago – can lead to incorrect conclusions.
On the other hand, we can use AI to resolve dirty data issues, modern software tools make data extraction easier than ever before, and data transformation tools are finally quite robust. It is always important to consider the data you might need later on but a lack of foresight in this area is often not an insurmountable obstacle.
Choose the Right Tools
Which may be easier said than done. There are multiple solutions competing for your attention and your budget. All the major cloud providers, for example, offer a suite of tools. You also have Hugging Face, OpenAI and a plethora of other AI providers, and more tools and AI frameworks enter the market each day.
Your selection criteria should reflect your business and the use case in question. If you don’t have the expertise on staff to set that criteria, it could be worthwhile to hire a contractor to help choose the best tools for the situation.
Build or Hire a Team
You’ll need people to build and maintain your models, collect and clean the data and perform other tasks to make your AI projects come to life. As part of that, you’ll have to make a decision: Do you want to hire full-time employees, or do you want to contract with an outside agency or expert? Building an internal team could be a good choice if you want to integrate AI into your operations — full-timers will have the time and (eventually) the institutional knowledge to make that happen.
Hiring, onboarding and retaining all that talent, though, takes time and money. Working with a contractor can help you develop AI more quickly. You could also choose a “best of both worlds” approach: Hire a few full-time employees, and use a contractor to supplement and train them.
Integrate Before You Overhaul
It might be tempting to start fresh and build a brand-new application as part of your investment in AI. But it could be smarter to look for ways that AI can be used to enhance your current systems and processes. A seasoned AI/ML practitioner will be able to identify opportunities for automation in your current processes.
Monitor and Adjust
Any planning for an AI project should include arrangements for regular maintenance. After all, most AI-powered applications aren’t ever truly “finished.” They still require regular review and attention to make sure that they still have fresh, accurate data and that the application is still producing accurate, actionable insights or decisions.
Doing the Right Thing with AI and ML
The ethics of leveraging artificial intelligence and machine learning
The potential of AI and ML seems limitless. Ironically, that’s why it is so important for businesses to develop and adhere to basic ethical guidelines around how they employ this technology. A large and growing segment of the population has serious concerns about how AI and ML might be used against them. Forward-thinking businesses know that following a code is one of the best ways to protect their customers and retain their trust.
Best practices for using AI and ML ethically are still developing, and the exact application will vary from company to company. But there are a few basic principles that most businesses should apply to their operations.
Empower Staff — Don’t Erase Them
Be thoughtful about how you introduce AI- and ML-powered solutions to your organization. Many employees fear their jobs will be at risk of elimination or replacement by this new technology. You can mitigate that fear by pointing out how these tools can create new opportunities and help staffers grow in their roles by taking over monotonous tasks.
Be Transparent
Users should know how and when AI is making decisions, especially when those decisions impact a person’s ability to secure a bank loan, apply for a job, buy insurance or any of the millions of ways that AI could have a real, lasting impact on a person’s life. Knowing that AI is playing a role in a process — and could very well make a mistake — gives people the knowledge they need to advocate for themselves and, if necessary, push back against unfair decisions.
Guard Against Bias
Ensure that your AI system is fair and doesn’t perpetuate existing biases. No business would ever program an application to be biased, but there have been a number of cases where AI has made decisions that were, in practice, unquestionably racist. To prevent this, more organizations are placing more scrutiny on the data they use to train their models, and they’re reviewing the outcomes to prevent biased outcomes.
Respect users’ data privacy
You may have access to amazing amounts of data about your customer base, including the information that you collect, as well as any external datasets you use to enhance your models. As a result, your AI project may accidentally surface sensitive information about individual users that should remain private, like an impending pregnancy or an appointment with a divorce attorney. Smarter businesses show respect for data privacy by thinking carefully about what kind of data they collect, guarding it zealously once it’s gathered, and being considerate about how they use AI-generated insights in their marketing and messaging.
The Future Is Now
Artificial intelligence isn’t going away. We’ve just begun to see how it can improve both the most routine and the most critical aspects of business. Smart organizations should start thinking about how they can incorporate this technology into both their strategy development and their operations.
While it might seem intimidating, it’s possible for any business — including your business — to enjoy the benefits of AI. The key is to implement the right tools and strategy with a consistent, continuous focus. Don’t let a lack of understanding or perceived cost stop you from investigating where these tools might benefit your business.
If your team needs help getting started, Stacked Analytics can assist — at any level of support that you need. Let’s talk about creating an engagement that’s sized to your organization’s needs, goals and budget.
