If recent history has taught us anything, it's that new technology comes with caveats. Innovations are rarely good to go hot off the press and need a bedding-in period. Early adopters and trailblazers will make mistakes, trial and error is inevitable, and a great deal of potential will be locked away until conditions hit the Goldilocks zone.
That's where we are with artificial intelligence and its many manifestations. According to a recent Forbes Advisor report, 56 percent of businesses are investing in AI to improve and perfect their business operations, but how many of those businesses can say with certainty that they have mastered AI?
In a 2024 Censuswide survey commissioned by ActiveOps, 91 percent of service operations leaders said they still struggle to get satisfactory insights to support their decision-making. What's more, a staggering 98 percent admit they have a steep hill to climb when it comes to adopting AI for gathering, analyzing, and reporting data. We're witnessing a classic case of technology evolving faster than businesses can keep up; There is no blueprint for perfect AI adoption, and the path to AI is still littered with cracks and pitfalls. Some larger businesses are like monster trucks with beefy tires, able to take a few hits along the way and still come out on top; smaller businesses, however, need to drive more carefully and avoid whatever obstacles they can along the way.
From data silos and outdated information to a lack of data literacy and insufficient leadership support, these obstacles can slow down or even derail AI adoption. Concerns about job displacement and the ethical implications of AI further complicate the road ahead, leaving some operations leaders feeling like they're carrying a torch through a dark cave. So let's shed some light on the path ahead. Here are the five main barriers to AI adoption in service operations.
Barrier 1: Data Silos and Inconsistent Metrics
No business is an island, and their departments can't afford to be islands either. Businesses that are yet to begin their AI journeys, and even those that already have, still play by legacy rules when it comes to their data. At the beginning of the current AI boom, back in 2022, a Forrester Research report revealed that employees were losing roughly 12 hours a week chasing siloed data. It was locked away in various departments, only accessible to people or applications with a perceived need. This might have been fine a decade ago, but for AI to work effectively it needs data consistency and data access. If data is isolated in various silos around the business, it naturally takes on a fragmented state that makes it nearly impossible to gain a full, holistic view of the business. What's more, those silos might be updated at different times with data manually exchanged between them, leading to data duplication and misclassification. This is anathema to AI. To truly benefit from AI, organizations must break down these silos and standardize their metrics, ensuring that data flows seamlessly across the enterprise and provides a solid foundation for AI-driven insights.
Barrier 2: Outdated Data and Impaired Decision-Making
AI thrives on timely, relevant data, but many businesses struggle with outdated information. According to our Censuswide survey, 94 percent of U.S. and Canadian businesses are still basing their decisions on data that is more than a week old, with an average 12.5 percent of businesses using data that is more than two months old. In service operations, decisions need to be made quickly and accurately, relying on the most current data available. However, when this data grows stale, it no longer reflects the real-time conditions of the business. This disconnect can lead to impaired decision-making, where AI-driven insights are based on information that is no longer valid. Trust in AI then plummets, leading to a lack of confidence and a state of decision paralysis in leaders. To fully harness the power of AI, organizations must prioritize real-time data processing and continuous updates, ensuring that their AI models are always working with the freshest and most accurate information available. For most businesses, that means hitting pause on their AI initiatives while they tidy up their data environments.
Barrier 3: Lack of Data Literacy and Skills Among Employees
Even the most sophisticated AI systems are only as effective as the people who use them. A significant barrier to AI adoption in service operations is the widespread lack of data literacy and the necessary skills among employees. While AI could transform decision-making and operational efficiency, it requires a workforce that can understand, interpret, and act on AI-generated insights. Unfortunately, many employees are unprepared or ill-equipped to engage with these complex technologies, leading to resistance, inefficiency, and underutilization of AI tools. Without proper training, even the best AI systems can become a source of frustration rather than a valuable asset. By equipping employees with the skills they need to confidently work with AI, through fostering a culture of continuous learning, businesses can ensure that their AI initiatives are fully supported and effectively integrated into daily operations, maximizing the potential benefits of AI.
Barrier 4: Insufficient Leadership Support and Buy-In
The success of AI initiatives in service operations hinges heavily on the support and buy-in from leadership. Our Censuswide survey revealed a common theme across multiple regions, where around half of service operations leaders felt their leadership teams weren't interested or confident enough in AI to pursue it. Without strong endorsement from the top, AI projects often struggle to gain the necessary resources, strategic alignment, and momentum needed to succeed. Leaders who are hesitant or skeptical about AI might deprioritize these initiatives, viewing them as experimental or non-essential. This lack of commitment can lead to underfunding, inadequate staffing, and ultimately, the stagnation of AI efforts. To break through this barrier, it's essential for leaders to be well-informed about the long-term value and strategic advantages of AI. By clearly communicating the benefits of AI and aligning it with the organization's broader goals, leaders can build the necessary buy-in and create an environment where AI initiatives are prioritized, properly resourced, and poised for success.
Barrier 5: Concerns About Job Displacement and Ethical Implications
As AI becomes more advanced and capable of automating tasks traditionally performed by humans, it's only natural that some employees are concerned about their jobs being put at risk. This concern can lead to resistance, reluctance to engage with AI, and even active opposition to AI initiatives. Then there are the ethical implications of AI related to privacy, bias, and the transparency of AI decision-making, further complicating adoption. All of these concerns are legitimate and won't disappear on their own. Companies, therefore, must engage in transparent communication and involve employees in the AI adoption process from the outset. In other words, give them a stake in AI and get their buy-in.
AI isn't just a game of technology; it's a game of trust, preparation, and cultural change. By confronting these barriers head on and fostering an environment of transparency and continuous learning, businesses can transform AI from a daunting hurdle into a powerful catalyst for innovation and growth.
Spencer O'Leary is CEO of North America at ActiveOps.