We look at key focus areas, from improved customer understanding to streamlining the back office.
Key takeaways
Companies should consider the scalability and flexibility of their AI solutions.
Successful AI implementation will require cross-functional collaboration.
Amid the hype around generative artificial intelligence, technology companies may find themselves grappling with how best to integrate AI into existing systems and offerings. Considering the enormous potential of this technology, it may feel daunting to determine which use cases to prioritize. Especially for technology companies, zeroing in on key focus areas can help businesses figure out where to direct their efforts.
With advances in technology and pressure to innovate, middle market companies want to move fast with artificial intelligence solutions to develop a competitive advantage. However, while many organizations have already seen a positive impact from AI solutions, the strategy is not without challenges and risks.
Recent research from RSM reveals that the majority of middle market businesses report utilizing AI. According to findings in the RSM Middle Market AI Survey 2024: U.S. and Canada, 78% of executives at midsized companies use AI formally or informally, while 77% have adopted generative AI solutions. Use cases for generative AI are broad, led by quality control and customer service.
Plenty of technology companies have already been enabling AI in their tools and applications for some time. For many businesses in the tech sector, the most important thing to do is take an inventory of where they are already incorporating AI into their tools and reassessing those integrations.
Along with ensuring AI applications comply with regulations and prioritize ethical standards to maintain user trust, technology companies should assess where AI can most enhance efficiency, innovation or customer satisfaction. This can help make the path to high-value use cases clearer.
Here are some potential areas of focus for AI implementation:
1. Understanding customers better
AI tools can analyze vast amounts of user data to forecast customer churn and help technnology businesses identify pain points where they may be able to improve customer retention. That, in turn, can help sales teams improve lead-scoring methods and better determine how to personalize outreach to existing or potential users.
AI can also improve customer support by providing intelligent chatbots and virtual assistants that handle routine inquiries, allowing human agents to focus on more complex issues.
2. Enhancing internal workflows
Another critical use case for technology companies is the integration of AI into internal workflows. Tools like GitHub Copilot, which assists developers by suggesting code snippets and automating repetitive tasks, can boost productivity. Microsoft Fabric is another example—this tool allows users to interact with data through natural language commands. This can help democratize data analysis within a company and make that analysis more accessible to nontechnical employees.
AI tools can also improve knowledge management processes more broadly, streamlining information retrieval and making search functions more intuitive.
3. Improving data governance
Companies can use AI tools to make their data governance and data architecture systems clearer and more organized via data tagging and streamlined access protocols. This is an especially crucial use case for companies that have employees who gather and interact with customers’ personally identifiable information.
Making sure your organization already has data architecture that is easy for these AI systems to understand is critically important. This will enable a tool to identify where sensitive information is stored and also identify which people and which parts of the business are using that data.
4. Streamlining back-office applications
Especially for leaner or early-stage technology companies, AI may be an attractive solution to streamline a host of back-office applications. This includes automating accounting tasks, detecting and preventing fraudulent activities, and providing predictive analytics for better planning. On the sales and marketing side, AI can personalize customer interactions and help optimize marketing campaigns. IT teams can also harness AI tools to detect network anomalies and infrastructure issues.
5. Financial management and planning
Generative AI tools can produce more precise contract renewal forecasts, efficiently identify how to optimize existing contracts and help the finance function with broader revenue forecasting. These use cases can be particularly key for technology companies that use subscription-based business models and need to constantly track recurring revenue metrics.
The bottom line
For any company, successful AI implementation requires cross-functional collaboration. Marketing teams, IT departments and business units must work together to identify opportunities for AI integration and to develop cohesive strategies. Engaging stakeholders from different areas ensures that the AI solutions are aligned with overall business objectives and that they address the specific requirements of each function.
When evaluating potential AI use cases, companies should consider the scalability and flexibility of their AI solutions. As technologies evolve, the chosen solutions should be able to adapt and grow with the company. Scalability ensures that the AI implementations remain relevant and effective as the organization’s needs change.
RSM contributors
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