By now there is virtually no one in the business world who hasn’t heard of generative AI. In recent years, particularly since the release of ChatGPT in late 2022, there has been a lot of AI hype floating around the globe. But how much of this hype is mere fantasy, or a practical reality?
This article digs deeper into AI in a bid to separate fact from fiction.
It also highlights some of the common challenges in realising the promise of AI’s power, practical steps for delivering AI initiatives, and measuring the ROI of implemented AI projects.
AI hype vs AI reality
With Hollywood’s fascination with AI in feature films, portraying futuristic worlds run by robots and highly intelligent machines, it’s little wonder there is so much hype surrounding current AI innovations. The key is to try and separate AI’s practical applications in today’s society from the fear and conjecture of what AI is actually capable of and what it means for the future.
AI hype
AI hype is about exaggerated perceptions and expectations surrounding the influence, power and current capabilities of AI. The media has perpetuated much of this hype. Marketing tactics and convoluted claims by technology companies are also responsible. Some of this hype can also be blamed on the depiction of intelligent machines in science fiction movies and literature.
The media has been heavily focused on futuristic and somewhat dystopian applications of AI technology, inferring that AI will surpass human intellect and people will eventually be redundant.
Furthermore, marketing companies use AI buzzwords to inflate the potential of AI for marketing purposes and to elevate their AI-powered products. Some AI buzzwords frequently found in marketing spiels include “machine learning”, “deep learning” and “artificial intelligence”. This adds an air of sophistication, even if there are limits to AI’s capabilities.
The general public has developed an intense fascination with AI, further perpetuating the hype. It seems everyone has an opinion on what AI is, and what it’s capable of doing. Some of the hype and conjecture is positive while much has negative connotations. There are even articles about the imminent arrival of sentient robots. In general, the narrative surrounding AI is largely sensationalism.
AI reality
The concept behind AI technology is for machines or computers to be able to perform tasks that require human intelligence. Examples are recognising patterns of data, problem solving and learning. In the areas of machine learning and deep learning, AI technology has made significant advances.
When it comes to recognising data patterns, machine learning is about training the algorithms to make predictions and decisions according to that information, extracting meaningful insights from layers of complex data.
- In the medical field, AI can now analyse images, as well as diagnose and predict patient outcomes.
- The world of finance uses AI algorithms to detect fraud and perform risk assessments.
- Many writing assistant platforms online now utilise AI to not only provide outlines for articles and stories, but to write complete works.
- AI can now compose music and create beautiful artwork.
- Many website chatbots are now AI-powered, and contact centres around Australia are also taking advantage of this technology.
These are just some of the practical applications currently available with AI technology. These are real world examples and not hype or fantasy.
Common challenges in realising AI’s promise
With AI technology rapidly advancing and confusion between the hype surrounding AI and the practical reality of its application, there are a few challenges facing businesses looking to integrate AI into their processes.
For starters, it’s hard to determine which innovations are actually useful, and which tools and programs are simply hyped up in order to get sales. The AI hype can cloud peoples’ judgement when it comes to deciding which AI technology to adopt.
Another potential challenge is balancing the implementation of AI and the prospect of job displacement. As certain AI algorithms can perform a variety of human tasks, there is the risk that some employees will be made redundant if the technology is integrated into the business.
Practical steps for delivering value from AI initiatives
Due diligence and evaluation must be pursued
Before making firm decisions about implementing AI into your business, you first need to evaluate the value of the AI technology you’re seeking to introduce. Will it perform the functions you need it to, and will somebody lose their job as a result? It’s best to take a cautious approach and do your research. Ask questions and seek demonstrations before arriving at a decision. This is essential for determining the feasibility and benefits of various AI solutions.
What about ethical considerations?
Ethics also need to come into the equation. This involves things like data collection, data sharing, model training and ongoing monitoring. Transparency, accountability and fairness are essential in all AI decision-making processes.
Consider your organisation’s real needs
To get the most out of AI technology in your business, you first need to consider your organisation’s needs to determine the areas where AI will be of most value.
- Where will AI make the most impact and be the most cost-effective?
- Do you need AI for data anomaly detection or quality checks?
- Does AI need to analyse consumer data to create targeted marketing campaigns?
Predictive analytics and forecasting are also common applications of AI technology, along with helping business owners and executives make better, more informed decisions.
Take a multi-disciplinary approach
Collaborate with industry experts, policy makers and stakeholders to establish guidelines for the implementation and use of AI technology in your business. Develop regulatory frameworks for the responsible use of AI.
Measuring the ROI of AI projects
The success of AI applications within a business can only be gauged through regular monitoring and evaluation. Constant refinement will ensure you get the most out of any AI application, and will help to achieve a positive ROI on AI projects. AI needs to offer measurable benefits to be profitable.
Start with the overarching company goals, and work backward to align systems and data. Determine priority outcomes and ensure they have traceability.
Successful AI integration also depends on setting performance metric benchmarks. These benchmarks act as a road map, enabling you to assess whether an AI system is meeting intended goals and expectations, or if it’s failing to achieve its objectives.
As AI learns the more you use it, continuous testing, feedback gathering and tweaking to improve your systems, is essential.
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