The future of work: How businesses can harness GenAI to solve real-world problems

Despite Generative AI (GenAI’s) uncanny ability to transform industries, enable companies to automate workflows, enhance customer interactions, and accelerate innovation, many businesses face a common challenge: How do we move from exploration to execution?

The key lies in selecting and deploying AI tools with purpose and precision.

The Stanford AI Playground is a fantastic example. In their endeavours to create a convenient, centralized and easily accessible environment for staff and students, the University IT launched AI Playground — a platform where members could test and compare various AI tools. This hands-on innovation also ties in with the University’s effort to address the evolving role of AI across education, research and administration.

In this article, let’s take a look at how organizations can select the right AI tools that tie in with their business goals and focus areas.

Five steps to an ROI-driven GenAI strategy

Here are five practical steps to ensure your organization gets real value from its GenAI investment.

1. Focus on the business problem first.

Very often, companies start their AI journey by choosing a tool first and then trying to fit it to a business challenge. This approach leads to wasted time and resources, when the smarter approach would be to start with a clearly defined problem.

Are you trying to reduce customer service response times?

Automate repetitive tasks?

Personalize your marketing content at scale?

By defining your business challenge first, you'll narrow down your AI tool options to those that address your specific needs.

For instance, a company focused on improving customer engagement would prioritize tools with natural language processing capabilities. Meanwhile, a business looking to streamline internal data analysis would seek tools optimized for data extraction and reporting.

Key takeaway

Start with the problem, not the tool. This ensures your AI initiative has a clear purpose and measurable outcome.

2. Choose the right mix of tools.

There are two main categories of AI tools available today: general-purpose tools and specialized tools. General-purpose tools can handle a broad range of tasks, such as generating text, automating emails, or creating presentations. These are flexible and easy to scale across multiple departments. On the other hand, specialized tools are designed for niche applications, such as legal document review, customer support automation, or medical diagnostics. These provide higher accuracy in their specific domains but often require more effort to integrate.

Both types of tools have their own places in a well-rounded AI strategy. General-purpose tools can deliver quick wins, while specialized tools provide higher accuracy and reliability for specific business needs.

Key takeaway

A hybrid approach — leveraging both general-purpose and specialized tools — delivers the best results.

3. Prioritize data integration and security.

Even the most advanced AI tool won’t deliver value if it doesn’t integrate smoothly into your existing systems.

When evaluating AI tools, ask yourself:

• Does it integrate with our CRM, ERP, or internal databases?
• Will it disrupt existing workflows?
• Does it have built-in security features to protect sensitive company data?

Data security is especially critical in industries like healthcare, finance, and legal services, where tools must comply with privacy regulations and handle personally identifiable information (PII) with care.

Key takeaway:

Integration and security are non-negotiable. The right AI tool should work seamlessly within your tech stack while ensuring data protection.

4. Set clear KPIs to measure success.

A common mistake that organizations make is deploying AI tools without clear performance metrics. This leads to confusion about whether the tool is delivering value.

Before rolling out an AI tool, define your key performance indicators (KPIs). These might include, but are not restricted to customer satisfaction (CSAT), process cycle time (PCT), cost savings and revenue growth.

Now, link these KPIs to specific business goals. For example, if you’re automating customer service, measure whether response times have decreased or customer satisfaction has improved.

Key takeaway

What gets measured gets managed. Clear KPIs ensure your AI initiative delivers measurable results.

5. Don’t forget the human element.

Even the best AI tools won’t succeed without employee buy-in. Employees often fear that AI will replace their roles.