In today's tech-savvy world, artificial intelligence (AI) is at the front of everyone’s mind. With AI processes affecting every aspect of our world and buzzwords like ‘machine learning’, ‘neural networks’, and ‘deep learning’ becoming commonplace. AI's presence in our everyday lives is becoming increasingly undeniable and process adoption in organizations is on the rise.
However, the move to AI process adoption has led to a notable shift in the current landscape. Instead of merely being curious about the hypothetical possibilities of AI, customers now demand practical, cost-effective AI solutions that deliver tangible returns in shorter time frames. Gone are the days of speculative investments, it’s all about getting things done and showing real benefits fast.
Changing focus
All speed and no substance, is AI worth the hype?
So, is AI living up to the hype? According to a recent McKinsey report, one-third of all respondents stated that their organizations regularly utilize generative AI in at least one function. In other words, 60 percent of organizations with reported AI adoption are using gen AI and that 40 percent of those reporting AI adoption at their organizations say their companies expect to invest more in AI overall thanks to generative AI (McKinsey, 2023). Studies such as this show that the shift toward practical AI solutions is clear.
AI usage is becoming particularly prevalent in cyber security and fraud management spaces, with over half of business owners in a recent Forbes survey stating they use artificial intelligence to assist in these functions. In addition, in the customer survey space, nearly two-thirds (64%) of business owners in the same survey stated that they believe AI will improve customer relationships (Forbes, 2023). This data point shows that people are impatient, they want AI that works to the capabilities they need, and they want it now. This is leading to the diversification of AI offerings, think AI-driven customer service platforms that improve user experience or predictive maintenance systems in manufacturing that cut down on downtime. These are the kind of tangible benefits that people are excited about.
An example of ChatGPT 4 in action
So, what’s the concern with AI process automation?
Of course, with all this excitement, there are some big concerns too. Here are the main ones:
- Data Safety: Keeping sensitive info secure from breaches and misuse
- Responsible AI Use: Making sure AI is fair and unbiased
- AI Hallucination: When AI systems generate incorrect or misleading information
These issues are serious. Take AI hallucination, for example—when AI systems generate incorrect information, it can be a huge problem for end users. According to a study by Statista, 66% of respondents stated that they were very or somewhat worried about AI hallucinations.
Data safety was another key concern here with 62% of respondents stating they were very or somewhat concerned by data privacy. Respondents were most fearful of AI generated scams with 71% of respondents stating that they were very or somewhat worried about the prospect (Statista, 2023). These findings highlight the need for increased policies surrounding responsible AI use, this is where Retrieval-Augmented Generation (RAG) offers a solution, helping users cut down on mistakes by using verified data to guide AI, ensuring that the output is accurate.
AI: what is the concern?
AI trial and error: what is the solution?
So, what’s the fix? The future of AI is all about balancing cutting edge technology with solid safety measures. AI co-pilots, AI process, and process mining are some of the key players here:
- AI Co-pilots: As their name suggests AI co-pilots are designed to assist humans in their role, not to replace them. They are on the rise and have the capabilities to assist with everything from coding to customer service, boosting productivity and accuracy.
- AI Process: This involves automating complex workflows and decision-making processes. AI process technologies streamline operations, reduce errors, and reduce time wastage by handling repetitive tasks, allowing humans to focus on more strategic activities.
- Process Mining: Helps businesses analyze and optimize their processes by digging into existing data. This process mining leads to the creation of smarter, more efficient operations. Process mining tools are able to track and visualize how processes work in real-time, highlighting bottlenecks and opportunities for organizational improvement.
By using these technologies, companies can make sure their AI applications are not only practical but also secure and trustworthy, ensuring that organizations aren’t left behind.
Trial and error
The future of business process automation
As we move forward in the world of AI, the focus on practical applications and data security is becoming increasingly important. The shift from hype to real results shows a mature understanding of what AI can and can’t do within an organization. We’re entering an era where AI’s power is harnessed responsibly to deliver real, measurable benefits. The AI journey continues, marked by innovation, safety, and practical impact.
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