The story goes like this: In late 19th century Kansas City, an undertaker by the name of Almon Strowger grew suspicious of his telephone switchboard operator. This operator just happened to be the wife of his local competitor, and he suspected that she was diverting his calls to her husband’s business.
Most people would lodge a complaint. Not Strowger. Instead, he put his outrage (and engineering skills) to work and developed the first automated telephone switching system, known thereafter as the Strowger switch. His intent was to remove the human component of telephony entirely. And it worked. Sorta.
His innovation did kick off the global automation of telephone exchanges. But many companies jumped in before the technology had been perfected, and paid a price. Some customers, used to hearing friendly human voices and easily annoyed by faulty transfers, complained or took their business elsewhere. The technology frequently broke down entirely. The Lorimer system, a competitor to Strowger, went bankrupt due to its poor reliability. Of course, these systems were eventually perfected, but it took time. And there were multiple failures and losses along the way.
Today, we’re looking at a similar evolution in AI. This technology has huge potential and is already creating business efficiencies. However, companies diving in headfirst, without the proper preparations and guardrails, are taking a huge risk. Businesses must implement AI with clarity, purpose, and foresight to truly harness its potential.
“Everyone is doing it” is not a good reason
While enterprise leaders may fear they’re behind their competitors, in reality they’re probably not. Sure, the competition may claim to be “full speed ahead” with AI, but the data tell a different story. According to a wide-reaching survey conducted by our team at Lucidworks last year, only about 25% of enterprises reported successful outcomes, despite 99% having invested in AI initiatives.
AI must have rock-solid data foundations
AI is only as good as the infrastructure and information behind it. AI cannot fix broken processes or compensate for poor-quality data. Applications like customer support or enterprise search, for example, rely on structured and unstructured datasets that must be well-prepared and relevant. Without a clear data foundation, AI implementations will falter. Chaos answers quickly when irrelevant insights call.
While AI platforms excel at certain tasks, they often need organization-specific context to generate meaningful results. Successful AI strategies integrate the technology into existing workflows and ensure that outputs align with business needs, like directly addressing end-user challenges or internal operational gaps.
To overcome these challenges and make sure AI is working for them, there are some measures that organizations can take:
1. Define a Clear Use Case By Asking the Right Questions
Start by identifying the business problems AI can solve. Whether it’s streamlining supply chains or delivering personalized marketing experiences, every AI initiative should have clear metrics for success. Consider tangible impacts like cost reduction, revenue increases, or customer satisfaction improvements. In the planning stages, it’s helpful to ask the following questions:
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Will it improve how quickly and effectively we respond to customers or clients?
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Can it help buyers or end users interpret complex technical documents?
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Will it support more informed decision-making during the purchase process?
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What specific workflow or customer experience gap are we trying to address?
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How will we measure success — and what does meaningful impact look like?
2. Prioritize Data Quality
Data is at the heart of AI. Businesses should focus on sourcing high-quality, relevant data and preparing it for AI consumption. Misaligned or incomplete data risks undermining the entire implementation, much like faulty materials in construction result in compromised structures.
3. Balance Control and Automation
AI solutions from major vendors are primarily developed one-size-fits-all. In most cases, it’s up to enterprises to then tailor them to their unique needs. This could mean modifying algorithms to focus on industry-specific nuances or setting boundaries within AI tools to adhere to company policies or principles.
4. Invest in Robust Governance
To ensure AI tools operate both ethically and effectively, businesses need frameworks for governance. This includes security controls to avoid breaches, guardrails that define AI’s scope, and clear policies for monitoring, auditing, and improving performance over time. With so much scrutiny around AI ethics and privacy, businesses that overlook this step risk not just financial damage but reputational harm.
5. Build a Long-Term AI Ecosystem
A successful AI strategy isn’t just about deploying a single application or tool; it’s about creating an ecosystem of interconnected AI tools that continuously learn and evolve. With the rate of innovation in AI, platforms that enable scalability and future adaptation are crucial.
Closing Thoughts
AI has incredible potential to transform business strategies and operations. But much like the adoption of the Strowger switch, successful implementation relies on thoughtful planning and purpose-driven design. Start with clear goals and a commitment to do AI responsibly. After all, in the AI revolution, it’s not just about adopting the tools; it’s about adopting the right strategies.
Just as the automation of telephone exchanges a century ago couldn’t happen overnight (not well, at least), the rollout of enterprise AI cannot be rushed. Companies must align their AI ambitions with clearly defined – and achievable – business goals. Rushing into adoption risks costly mistakes, inefficiencies, and public trust issues.
