eWEEK content and product recommendations are editorially independent. We may make money when you click on links to our partners. Learn More.
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.
TABLE OF CONTENTS
1. Identify Current Performance and Technology Gaps
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
2. Research the AI Technology and Services Landscape
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:
- A free AI tool, an open-source solution, or a fine-tuned existing AI model
- A managed large language model
- Or even build your own generative model (though this is a particularly resource-intensive and expensive approach)
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?
3. Set SMART Goals for AI Adoption
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:
- Specific: The goal should very clearly state what you want to accomplish and on what timeline. It’s important to include numerical measures of progress and deadlines so everyone understands what the goal is. For example, “increase online conversion rate for sales of personalized AI product recommendations by 10% in the next eight months” is a highly specific SMART goal.
- Measurable: Again, numbers are important for these types of goals, because you’ll only know if you’re on track if there’s a way to measure your progress toward milestones. In the example we’ve listed above, the numbers are all there, and the team can easily track which conversions are happening due to AI-powered recommendations.
- Achievable: AI adoption will fall apart if you don’t set realistic goals for your team to act on. In the case listed here, this goal should be achievable if enough team members are aware of and committed to resourcing it.
- Relevant: Just because AI can do a certain task doesn’t mean that it’s the most efficient way to use it in your business. Be sure to set goals that are relevant to your business operations and longterm revenue goals, or else you’ll end up wasting time on a less-valuable project. This SMART goal is only valuable for businesses that are hoping to increase their product revenue in this way.
- Time-bound: SMART goals should include a realistic timeline for completion. In the details or subtasks that roll up into this goal, consider including monthly or weekly check-ins so progress and bottlenecks can all be assessed.
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.
4. Partner With Strategic AI Vendors
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.
5. Develop and Follow an AI Implementation Plan
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.
6. Create Cross-Functional AI Training and Change Management Programs
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.
7. Track AI Performance Metrics
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:
- Patient satisfaction with AI agent interactions
- AI agent’s response timeliness
- AI agent’s response accuracy
- AI agent’s adherence to HIPAA and privacy standards
- Number of patient interactions with AI agents over time
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:
- Quality of predictive insights
- Quantity of predictive insights
- Relevance and actionability of AI recommendations
- User satisfaction with AI and data explainability
- Performance speed and accuracy with larger datasets
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.
8. Adjust AI Solutions and Plans Periodically
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
7 Benefits of AI Business Strategy Planning
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:
- More strategic AI adoption and usage: An AI business strategy plan provides clear details about who should use what AI tools, when and why they should use them, and how they should use them most effectively. This comprehensive plan helps your whole team adopt the right solutions and ensure they actually get used in a way that consistently benefits the business.
- AI risk management and disaster recovery planning: An AI strategy doesn’t just cover what tools you’ll be using but also delineates what structures and safeguards will need to be established for success. The preplanning that comes with AI business strategy leads many business leaders to prepare a more effective risk management and disaster recovery plan, which may even extend beyond your AI tools to better protect the rest of your business applications and operations.
- Responsible and ethical approach to AI adoption: AI business strategies help you to think about all of the ways AI can impact your business, both good and bad. Proactive planning and ideating for AI is the best way to ensure you make responsible and ethical decisions that consider the needs of your employees and customers alike.
- Cross-functional and cross-disciplinary AI adoption: Without an AI business strategy, individuals or individual departments may adopt AI tools without much thought given to who else could benefit from these technologies. An overarching AI strategy helps the entire business identify useful tools and how they can be used effectively in different roles and divisions of the company.
- Competitive edge: Businesses that flesh out their AI strategies will have a clear picture of what they want to accomplish and what steps they need to take to get there. While other competitors may simply start using AI and run into performance issues or bottlenecks due to poor planning, businesses with an AI strategy will avoid many of these pitfalls and pass their competitors quickly.
- Automation and productivity support: An AI strategy assists leaders in identifying where current performance gaps or challenges are hindering productivity in the business. From there, they can select the AI software that is most likely to solve these issues, automate complex workflows, and otherwise improve the day-to-day operations of the business.
- Enhanced customer experience and customer insights: Businesses typically start their AI journey with internal solutions to help their employees’ productivity, but with an AI business strategy, they may more quickly identify how AI can improve customer experiences.
7 Common Challenges of 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:
- Making smart cross-functional AI investments: Technology investment decisions are often made from the top down. But with AI technology that is designed to be embedded and incorporated into multiple parts of business operations, this approach may mean you invest in a tool that is not a good fit for certain employees’ day-to-day responsibilities. To avoid this, you should create a cross-functional decision-making team for AI business strategy planning.
- Preparing internal data and operations for AI: Many organizations spend all of their time researching and selecting the right AI tool for their business but never consider all of the work that should go into preparing their data, workflows, and other business assets for AI. Businesses that fail to prepare their data for AI may end up with poorly-trained or ineffective AIs, or worse, an AI that has been exposed to PII or PHI in a noncompliant manner.
- Setting and sticking to reasonable AI goals: AI is an exciting business prospect, and many business leaders are tempted to implement AI solutions across their operations all at once. However, this method can lead to implementation errors and limited utility, as you’re giving your teams minimal time to edit and fact-check AI additions. It’s a good idea to set iterative AI implementation goals, giving your team a chance to learn how these solutions work and optimize them before moving on to the next big thing.
- Considering AI from important ethical and privacy angles: Whether you’re in a highly regulated industry or not, working with AI can be risky because of how some of these models train with and otherwise expose private data and business processes. Businesses should steer clear of AI companies that are unable or unwilling to explain their data collection and training processes; they should also work closely with these vendors to determine what additional AI privacy security safeguards need to be added to protect their data when using AI tools.
- Identifying and enforcing change management best practices: Businesses often make the mistake of subscribing to an AI tool and then letting it collect dust while employees continue to manage their workflows as usual. To make sure AI investments are threaded through your operations effectively, you must first determine what AI should support and why and how this support should be offered. From there, it’s important to train and retrain all impacted workers on how to work with AI in their roles.
- Addressing new security challenges: Because of how AI technology works with data and complex training algorithms, these models can expose your data and operational infrastructure to new security risks. Many businesses fail to prepare for these new attack surfaces, which can lead to severe breaches and data theft or loss.
- Staying up-to-date on changes in AI technology and regulations: The AI technology landscape — and the regulations that are trailing just behind — seems to change on a minute-by-minute basis. If your organization doesn’t keep up with these changes, you may find yourself in a position where you’re using a tool or working with a company that is operating unethically or is noncompliant with regulations that impact your organization. It is your responsibility to stay abreast of regulatory changes and confirm that your AI vendors and tools are in alignment.
Frequently Asked Questions (FAQs)
Why Do You Need an AI Strategy?
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.
What Is an Example of an AI Strategy?
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:
- Involving marketing, sales, IT, and product team stakeholders in decision-making
- Setting a goal for how you want the AI chatbot to interact with customers and pass off conversations to human agents
- Developing the steps and investments your organization needs to reach this goal
- Measuring the AI solution’s success with metrics like response accuracy, response speediness, and customer satisfaction
How Do You Implement AI into Your Business?
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.
Bottom Line: Developing an AI Business Strategy That Works for You
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