Developing a competitive artificial intelligence business strategy has quickly become an essential leadership strategy as AI has grown into an indispensable business tool.
Businesses from all different industries are incorporating new enterprise AI use cases in their workflows to improve products and disrupt their respective industries. To keep up with the competition, business leaders need to develop an AI business strategy that addresses their unique business model while helping them keep pace with industry-wide digital transformations.
In this guide, we’ll walk you through eight key steps of crafting a top AI business strategy. We’ll also cover some of the greatest benefits and biggest challenges that come with adopting AI as part of your business’s operational framework.
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Artificial intelligence tools and strategies can be infused throughout your business operations, but chances are, there are a few key areas where AI will make the highest-value impact in your organization.
To determine where these gaps are, start by looking at your legacy tools and applications, as well as any performance data or support tickets that indicate recurring problems with those systems. Additionally, assess the size and quality of different departments and teams, paying close attention to any resources they’re missing that would make their work more efficient.
Finally, look at things from the investors’ or customers’ perspective and ask this question: What current performance gaps are impacting their experience or the bottom line?
Asking these questions, completing a deep audit of your current resources and processes, and documenting your most crucial gaps is an important first step toward determining which AI solutions, partners, and investments are most strategic for your business.
To develop a plan to improve performance, it’s critical to create metrics. The chart above illustrates the various metrics that can be used to estimate the ROI of an AI investment. Source: Gartner
Once you’ve decided which areas of your business could most benefit from AI tooling and strategy, it’s time to look at the AI technology landscape and what it has to offer.
Depending on your internal skill sets and budget, you may choose to invest in:
There’s also a wide range of prebuilt AI tools that won’t require you to adjust any deep learning algorithms or training data. Instead, you’ll simply work with the vendor or their platform to adjust the AI to your specific needs. You’ll also need to determine if you want to invest in more generic AI solutions — such as chatbots, copilots, and AI automation tools — or if you’re interested in a more specialized solution that is designed for your industry or a enterprise specific use case.
All of these approaches are valid but may not be the best route for your business. To make the best possible investment, spend some time researching leading AI vendors, big and small, assessing their individual products, longterm roadmaps and goals, and the additional resources they provide to support their customers.
It’s also valuable to look at customer reviews on third-party review sites, ratings from technology research firms, and the investors that are currently backing some of these technologies. Through each of these portions of your research, return to this question: Does what I’ve learned about this vendor, product, or service align with our business’s goals and way of doing things?
You’ll likely have a basic idea of your AI adoption goals and desired outcomes at this point, but you’ll stand the best chance of reaching those goals if you spell them out. There are several different ways to do this, but SMART goals provide a straightforward and highly objective way to measure how well you’re staying on track.
SMART goals are:
Your team may have one or several SMART goals like this one, depending on how many AI projects you’re working on at once. A helpful way to organize and visualize all of these goals as part of a bigger picture is through a planning roadmap. And while these goals may look a little different, it’s worthwhile to spell out any AI ethics or compliance goals too, so your team won’t forget about them in the middle of implementation.
AI vendors come in all shapes and sizes. There are massive enterprises that were some of the earliest pioneers of AI technology, there are small AI startups that focus on a specific product or use case, and there’s companies that do a little bit of everything for AI products and services.
While it isn’t realistic to research every AI company out there, it’s smart to look at a variety of players to see who would be a strategic partner for your business. In many cases, the biggest name isn’t the most aligned or experienced with what you want to do.
The best way to ensure your AI investments work across all departments and functions is to bring key leaders, managers, and stakeholders from each group into the decision-making process. Consider having them complete a demo or trial period, share their perspective on what is and isn’t working with their current tech stack, and gain more operational data to secure a well-informed partnership or a well-rounded purchase.
Realistically, you’ve already completed several steps in creating your AI implementation plan, especially if you were thoughtful while writing out your AI adoption goals. Now, it’s time to figure out the exact details for executing a successful AI rollout.
You’ll want to first prep all key aspects of your internal operations — tools, data, and teams — for AI adoption. With your existing applications and data, this step may involve cleaning up or reformatting data so it works with new tools. With your teams, you may need to take some time to share your tooling and timeline decisions with them so they know where they fit into the AI implementation schedule.
Several examples of AI action or implementation plans can be found online, but ultimately, you know what makes the most sense for your team. Source feedback from your employees, talk with your vendors about what’s possible, and don’t be afraid to adjust your plan if needed along the way.
AI business strategies, no matter how strategic, will fall apart if your teams are unaware of or uninvested in your hoped-for outcomes. That’s why it’s important to train all employees on how AI will impact their role and how they can best use it for success. This can be a particularly sensitive step in an AI business strategy, as some employees may fear they are being replaced by AI.
To address and mitigate these fears, make sure that your change management program offers retraining and professional development resources that can help these employees feel confident if they need to up-skill. Fortunately, many AI vendors provide customers with extensive knowledge bases, learning resources, and even training academies and certifications that can help. These resources are available at all times, so when new employees come aboard or existing employees need a refresher, go back to these resources to keep things running smoothly on all fronts.
During and after AI implementation, your business should regularly track AI performance through the metrics that matter most to you. For example, if you’ve adopted an AI healthcare assistant or agent, your metrics may look something like this:
However, if you’ve invested in an AI data analytics platform to support your marketing and sales teams, your metrics may be more like this:
As you can see, these metric sets are quite different from each other. But they’re similar in that they each focus on both quantitative and qualitative measures of success. Regardless of what type of AI tool you use, be sure to select a wide variety of useful metrics and measure often; these measurements will help you determine if an AI app needs updating or a team needs retraining for better outcomes.
What AI looks like today is not what it will look like tomorrow. And what your business looks like today is not necessarily what it will look like tomorrow or a month from now. Additionally, the AI tooling and regulatory landscape is changing at a rapid and constant rate, so it’s important to keep your AI implementation and adoption plans iterative and agile.
If you adopt and test AI solutions on an iterative basis while also keeping up with how AI and industry-specific regulations are evolving — as well as how your customers and the general public’s views on AI ethics and usage change over time — you’ll be prepared to shift your approach quickly and keep your company aligned with the best possible outcomes.
For a deeper understanding of AI compliance issues, read our guide: AI Policy and Governance: What You Need to Know
The potential benefits of AI grow significantly when AI is accompanied by an effective business strategy. These are just a handful of the benefits that come with comprehensive AI business strategy planning:
AI business strategy planning is a difficult process, especially if you don’t get the right people and solutions in place from the outset. These are some of the most common mistakes and challenges that businesses face when working on AI business strategy plans:
Businesses need an AI strategy because investing in AI technologies can be an expensive, risky, and time-consuming practice. Going in with clear objectives and a strategic framework for AI investment will help your team identify the best solutions and get them operational with minimal hassle. This strategy will also help you determine if AI companies, technologies, and methodologies align with your organization’s culture, long-term goals, and ethical and legal expectations.
An example of an AI strategy is the structured framework, objectives, and measured steps that go into an AI implementation project like deploying a customer-facing AI chatbot on your e-commerce site.
To do this effectively, your organization should follow an AI strategy to get the right stakeholders involved, establish a clear overarching goal, set up objectives and deadlines, determine steps and initiatives to move in that direction, and define metrics to measure the overall success of this strategic implementation.
In the case of the example listed here, this will involve actions like:
You should implement AI into your business through an iterative and ongoing strategic process. As business priorities, budgets, stakeholders, and the AI landscape change over time, it will be important to watch for these shifts and make changes to your AI tooling strategy accordingly. Taking measurable, distinct steps in your AI adoption journey will make it easier to pivot.
AI business strategies should be custom-fitted to your organization, though the steps covered above provide a useful framework for getting started. Ultimately, you know what your business’s weaknesses are and what areas can most benefit from AI adoption. If you don’t know where these weaknesses are now, it’s time to start the internal discovery process and speak directly with internal stakeholders so you can identify where AI support is most needed.
When you begin to develop your AI business strategy, start by reflecting on what’s happening in your particular organization, industry, talent pool, and tool stack. All of these variables should influence the AI partnerships and tools you select, especially as many AI vendors are beginning to specialize in highly specific niches and use cases. If your workforce has limited AI experience or technical knowledge, it may be wise to research and partner with an AI-as-a-service or AI consulting company that has experience with your industry and the goals you are trying to accomplish.
To learn where the next generation of AI companies are headed, see our extensive overview: Top 75 Generative AI Startups
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