The truth is AI myths hold businesses back. For years, organizations have wrestled with misconceptions about artificial intelligence. Some believe they need cutting-edge technology to get started. Others think AI requires massive investments or a dedicated team of experts. Worse yet, many consider AI to be a passing trend, unsure of its long-term value. So, what’s the real story?
In this blog post, we’ll break down these myths and set the record straight. Learn how AI isn’t a luxury for the few but a powerful tool for businesses of all sizes, why you don’t have to overhaul everything to start AI adoption, and how IBM i can play a pivotal role in your AI journey. Plus, we’ll share real-world data to show how these misconceptions stand in the way of true business transformation.
Myth 1: AI Only Works with Cutting-Edge Technology
Contrary to popular belief, AI is not exclusive to the latest tech stacks. Businesses can implement AI even involving legacy systems. These older systems store valuable historical data, which can be analyzed through machine learning to uncover patterns and predict trends.
Natural Language Processing (NLP) tools also enhance legacy systems with features like sentiment analysis, automated customer support, and voice commands. AI can also act as a bridge between older systems and modern cloud-based platforms.
Tools for data integration and API development allow organizations to modernize their systems incrementally. The key is to identify where AI can add value without revamping your entire infrastructure.
Myth 2: You Need a Dedicated AI Team to Get Started
Another misconception is that AI requires an extensive team of experts or deep pockets to implement. The reality? This notion that AI demands large teams or massive budgets prevents many organizations from taking the first step. Small, focused initiatives can bring immediate value.
Our LinkedIn poll reveals that 50% of respondents focus on a mix of tactical and strategic AI projects, which demonstrates that organizations can adopt AI without committing to major transformations.
Businesses can achieve quick wins through integration of AI into tactical projects. For instance, AI-powered tools like chatbots, recommendation engines, or document processing systems can deliver measurable results without a massive initial investment. These quick wins help build momentum and confidence in AI adoption.
Myth 3: AI Is Just a Trend
AI is not a passing fad. Its foundations date back to the mid-20th century, and its recent advancements are proof of its longevity. Since the launch of ChatGPT in 2022, businesses have seen an explosion of generative AI applications, making the technology more accessible and impactful.
Our data reveals that 33% of respondents are in the exploration phase of AI adoption, while 23% are experimenting. These numbers highlight a common hesitancy to move forward.
However, waiting for AI to “stabilize” or become “complete” is a costly mistake. Competitors who embrace AI now position themselves as industry leaders and leverage AI to boost efficiency and demonstrate innovation.
Businesses that start small, experiment, and build their AI maturity incrementally often see the greatest success. AI is a long-term enabler, not a passing trend, and those who adopt it thoughtfully position themselves for the future.
Myth 4: AI Will Cause Widespread Job Loss
A frequent concern is that AI replaces human workers, but the reality is more nuanced. AI often automates repetitive tasks and allows employees to focus on strategic and creative work.
For example, AI tools can analyze large datasets, generate insights, and handle routine customer interactions to free up teams to tackle higher-value challenges.
However, trust in AI remains a barrier. According to 47% of our poll respondents, trust is the biggest challenge in using AI for business decisions.
Transparency in AI processes and clear communication about its benefits are essential to foster employee confidence and adoption.
Myth 5: AI Is Too Expensive
While high-profile AI projects can require significant investment, most businesses can start with cost-effective solutions. Many AI tools, like chatbots or predictive analytics platforms, offer scalable options that fit modest budgets. Open-source frameworks and cloud-based AI services further reduce costs that makes AI accessible to businesses of all sizes.
One of the best ways to minimize costs is by clearly identifying the right use cases. Businesses that fail to define their objectives and align their AI efforts with specific needs often invest in AI initiatives without clear expected returns.
Our poll data shows that 37% of respondents struggle with AI use case identification, highlighting this very challenge. When companies take the time to understand their business problems and how AI can solve them, they can efficiently focus on AI solutions to bring business value.
That’s where Fresche’s AI-Celerate helps, a program designed to help businesses quickly identify AI use cases and prioritize them based on impact.
With AI-Celerate, organizations gain clarity on which AI applications will provide the most significant return on investment. This allows businesses to implement AI in small, manageable projects that demonstrate clear value early on, without large upfront investments.
Myth 6: AI and Machine Learning Are the Same Thing
AI and Machine Learning (ML) are closely related but distinct technologies. While AI refers to the broader concept of machines simulating human intelligence, ML is a specific subset of AI that involves the development of algorithms that allow systems to learn from data and improve over time.
Many businesses confuse the two, leading to misunderstandings about what AI can achieve. AI encompasses a wide range of capabilities, from rule-based systems to natural language processing (NLP), while ML is focused on pattern recognition and predictive analytics. Understanding this distinction is crucial for businesses that want to effectively leverage AI to meet their needs.
The benefits of AI and ML are often intertwined, and businesses can use both to achieve different objectives. For example, AI systems can support decision-making with analysis of large datasets, while ML algorithms help improve these systems by predicting trends and optimizing operations.
Myth 7: AI Solutions Always Require Cloud Migration
There is a common misconception that businesses must migrate everything to the cloud to leverage AI. While cloud tools offer flexibility, they are not the only option for AI implementation.
Many AI solutions now work seamlessly on-premises or in hybrid environments and enable businesses to use their existing infrastructure without complete cloud migration.
For IBM i users, this means you don’t need to move all your workloads to the cloud to take advantage of AI. IBM i offers the flexibility to place workloads where they work best—whether on-premises or in the cloud.
This hybrid cloud approach helps you strike the right balance between cloud-first ambitions and practical needs, ensuring you can leverage AI without compromising on performance, cost, or security.
Our recent poll shows a balance of priorities in the hybrid cloud conversation, almost like a perfectly divided pie: one-third of respondents are focused on scalability and performance, another third on data security and compliance, and the final slice on cost optimization. These concerns are valid and reflect the balance organizations must achieve when adopting a hybrid cloud strategy.
With hybrid cloud, you can take advantage of the AI tools that work best in your environment without having to go all-in on cloud migration. The key is to assess your business needs, understand where your data resides, and choose the best deployment option for your AI projects.
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