Artificial intelligence is fueling one of the largest and fastest spending sprees in the history of technology. The plethora of AI solutions now available, however, leaves businesses with a critical decision: choosing between smaller, more practical solutions that address specific needs or investing in larger, flashier systems that promise to revolutionize their entire operation. While the allure of groundbreaking AI technologies can be tempting, the reality is that AI’s current value lies in more tactical use cases such as data analysis and automation for enhancing the customer experience and employee support. Ultimately, an approach that builds around realistic parameters – rather than potential – will deliver the greatest impact.
Here’s why businesses, and their IT teams tasked with making these decisions, should prioritize substance over style when adopting AI.
Faster Deployments and Results
One of the most compelling reasons to opt for practical AI is its faster implementation timeline. Large-scale AI projects are often resource-heavy and take months, if not years, to fully deploy across an organization. These implementations can also require significant time dedicated to data gathering, preparation, training AI models, and integration across a range of existing systems. While flashy AI initiatives may offer grand promises of futuristic capabilities, they often require more time than businesses have to create meaningful impact.
On the other hand, smaller, more targeted AI solutions are designed to address specific opportunities or pain points within the business. Whether it’s automating and improving a single application like customer service chatbots, streamlining routine manual work like network monitoring and device management for IT teams, or better forecasting demand, these AI tools tend to integrate quickly and easily into existing operations. Businesses can see tangible benefits within weeks, rather than months or years, reducing employee workloads and helping decision-makers demonstrate value early in the adoption cycle.
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Smaller Costs and Improved Return on Investment
Businesses aren’t just looking to invest in AI for the sake of AI – they need solutions that provide strong returns. While large-scale AI systems can potentially offer a massive impact, they also come with the risk of over-investing in unproven technology. The initial costs of setting up large and complex AI solutions, combined with the uncertainty of long-term success, can result in a poor ROI, especially if the deployment encounters delays or fails to meet expectations.
Practical AI solutions, however, are much more predictable in their costs and achievable outcomes. Because they are designed to address a specific business problem or improve a certain process, their results are more measurable. Businesses can quickly assess whether the solution is working and whether it justifies the expense. This leads to a more consistent and reliable ROI, as the focus remains on solving high-impact, immediate business challenges.
Better Alignment with Business Goals
Flashy AI solutions often present themselves as the answer transforming entire business models or industries. However, these solutions can sometimes be disconnected from the real-world, day-to-day needs of a business. A common pitfall is adopting AI technology for its potential to innovate rather than its immediate applicability. As a result, businesses may end up with complex AI systems that fail to directly address their most pressing problems or align with their key goals.
Smaller AI solutions are usually designed with a clear, targeted outcome in mind. Businesses that focus on use-case-driven AI tools tend to have better alignment with their overall strategy. Whether it’s improving the customer experience, reducing operational inefficiencies, or enhancing product development, these AI solutions serve as tools to achieve specific business objectives. The clarity of purpose ensures that AI deployments directly contribute to measurable outcomes that drive the business forward.
Easier to Build Organizational Trust in AI
While the adoption of AI is quickly becoming a necessity for businesses, that doesn’t mean that employees are always enthusiastic about the new technology. Uncertainty around AI’s complexity, disruption to well-established processes, or potential job losses can create some resistance to the technologies. Large AI projects, with their long timelines and uncertain outcomes, can exacerbate these concerns as they are often seen as risky endeavors.
Practical AI solutions, by contrast, provide clear, easily communicated value to employees and stakeholders. When AI tools are deployed to solve specific, identifiable problems – such as automating repetitive tasks or providing better insights through data analysis – employees can immediately see the benefits. This helps to build trust in the technology, as with tangible and positive quick-turn results. Over time, this trust can also pave the way for larger AI initiatives as teams across the business become more comfortable with AI-driven change.
As the old adage goes, bigger is not always better. For most companies, practical AI solutions that focus on specific, manageable goals will provide faster results, cost-effective implementations, and better long-term outcomes. By opting for targeted AI tools over flashier, large-scale systems, businesses can ensure they are leveraging AI in a way that aligns with their strategy and delivers the most value.
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