Tossing AI tools around and hoping for the best? That usually leads to a mix of results. To truly harness AI, it pays to map out opportunities and address risks upfront.
Even big companies are navigating the AI landscape with uncertainty. Dael Williamson from Databricks points out that simply moving data between systems can compromise its integrity. “You need checks and balances,” he says. And every company has data silos that can complicate things.
If your data isn’t reliable, the results will be off, and the return on investment might not materialize. Plus, selecting the wrong AI language model can lead to pitfalls. “Training models is key, but inference is where the real value lies,” Williamson notes. AI holds great potential, but it comes with challenges.
Security is another concern. Richard Cassidy from Rubrik warns that focusing on how to implement AI is crucial; otherwise, you risk security lapses. For instance, AI can create distractions, making it harder for users to spot real threats.
Cassidy also highlights a common issue: flawed processes. “AI can’t fix broken systems; it amplifies chaos,” he says. Many organizations jump on the AI bandwagon without asking what it should look like in practice. CISOs might invest heavily in security, but introducing AI can compromise those controls.
The Office of National Statistics indicates that 39% of firms struggle to identify practical AI applications, while 21% cite costs as a barrier. A lack of expertise also hinders progress. “Start with the problem, not the hype,” Cassidy advises. Identify specific challenges, like customer service bottlenecks, and build solutions incrementally.
To minimize risks, establish clear guidelines for pilot projects. Try automating simple tasks like report summarization or invoice generation, then assess the results. Did costs drop? Did value increase? Use those insights to inform future decisions.
Jumping into AI too quickly can backfire, especially if you mix sensitive data with generic models. Tony Lock from Freeform Dynamics notes that once data is fed into a model, you can’t retrieve it. “That’s why RAG exists—cleaning data before it’s used,” he explains.
What happens if a model gets pulled from the market? There could be unforeseen legal consequences, especially with existing lawsuits related to AI’s use of intellectual property. Companies might face penalties or unexpected costs as regulations evolve.
Lock emphasizes that organizations should ensure their data is clean and secure before diving into AI. Often, it’s not ready. Companies need to thoroughly inventory, audit, and standardize their data before integrating AI, which can ultimately help in reducing costs and improving workflows.
Robbie Jerrom from Red Hat advises organizations to take their time. “Understand your need, then narrow the use case,” he says. Calculate the costs associated with AI enablement, as even simple tasks can stack up over time.
Experiment with smaller projects to get familiar with AI’s potential. Perhaps have AI review contracts for inconsistencies; you might uncover issues that hadn’t been noticed. AI can streamline your processes, but it’s vital to verify results and adjust as necessary.
Training employees is also essential. Many will need guidance on using AI effectively. “Getting this wrong can lead to problems,” warns Jerrom, highlighting AI’s pervasive presence in the workplace.
Sue Daley from TechUK underscores the importance of understanding AI’s role in enhancing efficiency. Determine specific goals before exploring options. Different business needs might require varied approaches, like small language models tailored to specific challenges.
Incorporating AI intelligently starts in a controlled environment. Assess compliance and ethical considerations while bringing cross-functional teams into the fold. Education is crucial—engage everyone from leadership to frontline workers as you navigate changes.
Lock notes that it’s vital to make sure AI genuinely aids employees. When implemented correctly, AI can elevate productivity and worker satisfaction. Finally, organizations should recognize that not all AI is created equal; many might already have experience with simpler AI systems.
