5 ways small and midsized businesses should be preparing for an AI future today
Worried that your small or midsized business (SMB) isn’t ready to take advantage of all the AI capabilities that are heading your way? If so, you’re not alone. Business and IT leaders at companies of all sizes, across all industries, feel unprepared – due in large part to how quickly AI capabilities are advancing.
Unsure where to start? The path to a successful AI strategy starts with your approach toUnsure where to start? The path to a successful AI strategy starts with your approach to data and analytics. It can help to start small, building on existing strengths to experiment with AI tools, exercising existing muscles and developing new ones along the way. It helps to have trusted partners on board for your AI journey – those who understand your business, your strengths, and where you fall on the data & analytics maturity curve.
The right partner can accelerate your AI journey by finding practical ways to connect your organization’s capabilities to new and emerging technologies. They can also help refine new or existing algorithms and processes, positioning your organization to take advantage of the wave of AI capabilities on the way.
SAS draws on our deep, hands-on experience with SMBs to help you prepare for what’s next. We’ve created this guide to identify a few smart, strategic moves you can make today that will lay the groundwork for your AI success.
- Begin making the business case for AI now
Leaders like you are already thinking about AI, even if it’s unclear where or how to deploy it. You see its promise and potential – but you’re also highly focused on driving business value.
Two-thirds of SMBs report that AI has already had a very big or modest impact on their businesses to date, and just over 75% expect this level of impact in the next two years, according to the SMB Group.1 AI-driven automation is their initial goal: reduce manual tasks, analyze large datasets to detect patterns for decisions, and enhance end user experience.
You need an AI business case that articulates how to drive business outcomes, not just science experiments. Prioritize with a narrow focus to identify what will deliver the most high value impact to your business. Describe the risk of doing nothing and the potential of business disruption from new entrants or competitors. More importantly, document your ideas and use cases for how AI could be the catalyst to transform your business. Kick automation to the next gear for more flexibility, speed, scale, and personalization. SAS has written the AI Business Case Guide – a practical, detailed guide that can serve as your road map, specifically designed for the unique challenges and opportunities of AI.
- Identify exactly what you need AI to deliver
Many executives encourage their teams to concentrate their efforts on answering a single “north star” question – one that has the single greatest impact on everyday business decision making and which ultimately spawns many more questions. SAS customer 1-800-Flowers. com, the online flower and gift retailer, defines their north star metric as customer frequency: the number of times a customer buys from the company annually. Company leaders know that the cost of acquiring a first-time customer far outweighs the cost of subsequent purchases, so they maintain an intense focus on repeat business that reverberates throughout the organization. Customer frequency isn’t the only metric the company tracks, but it is the most important one, affecting virtually all business decisions the leaders make.
Start with absolute clarity on what exactly you’re seeking to accomplish with AI. What is the business problem that you need to solve to drive significant business impact? What tools are needed to measure your success toward those goals? What is the plan for communicating progress with key stakeholders, since celebrating successes, even small ones, is vital?
To increase their odds of success, SMBs are starting small and building AI capabilities and capacity as they go. They are using AI to streamline and automate analytics tasks: gaining faster access to information in order to make informed, data-driven decisions.
- Update your talent strategy
You’ve worked hard to recruit, retain, and grow great talent. But today, your people may not be ready for AI. Now is the time to update your talent strategy and execute change management initiatives.
Create a short list of curious, capable, pragmatic team members who could be prepared to lead the way on AI. From there, AI-oriented change management tactics can help, including providing extra support, training, and coaching. Many of the people on your team today already have a solid baseline of skills required to be successful in AI – by actively augmenting those skills with training focused on their technical and analytical capabilities, it’s possible to quickly ramp up your organization’s AI capacity.
Be honest about your talent gaps. There are a growing number of partners who can be pulled in for both short- or long-term projects to help your AI strategy get off to its very best start – and to continue delivering at a high level. Some, like software resellers, are purely transactional. Others provide more in-depth capabilities such as staff augmentation, data management and analysis, hosting, and much more. And yes, SAS offers many of these services as well.
- Get your data house in order
Data readiness is probably the least glamorous, most overlooked, and most important element of any AI strategy. Like traditional analytics, success with AI is data dependent. At many SMBs, data is often improperly handled, or managed on an as-needed basis: There’s plenty of it, but only a limited segment of the data gets used to inform business decision making. If that sounds familiar, it’s time to advance your data strategy to ease the transition into AI capabilities. Start by focusing on two aspects of data: access and quality.
Your data needs to be meaningful – not perfect – for you to act on it with confidence. Since your data is constantly accumulating, determining best practices for storage and access can be challenging. Partners who offer data pipeline and data strategy services can help make sure your data meets the standards for AI usage, working to ensure it’s available in the right formats when needed.
- Start kicking the AI tires now
Given how quickly AI technology is advancing and changing, make sure to keep a pulse on the capabilities (and limits) of AI technologies to determine where you want to start. There is no need to go “all in” on AI on day one. You can be successful starting small and growing into a more analytically mature business.
For example, many SMBs are heavily reliant on spreadsheets for decision making. One SAS customer had reached their limit with the 3.5 day, 34 step process required to generate a win-loss report. With automation, the business implemented an “always on self-service portal’ with interactive, web-based dashboards. The business impact: insights drove product direction, optimized sales staffing, and launched new campaigns.
Evaluating your existing data and analytics technology is a great way to assess your readiness for AI. Where do you already have strengths that might bolster your AI strategy? Consider that the majority of work required to enable AI will focus on data – gathering it, prepping it, analyzing it, creating visualizations, and understanding relationships, trends, and outliers in the data. These are all areas where you can begin assessing your organization’s AI readiness today.
