Choose your AI Projects Effectively

Artificial Intelligence (AI) is fundamentally changing everything around us, reshaping entire industries and redefining our daily interactions with technology. It is altering not just what you do, but the very methods and workflows through which you’ll do it in the future. For decades, machines have been replacing human brawn through mechanical automation and industrial robotics. Now, sophisticated AI projects are beginning to replicate the functions of the human brain, tackling complex cognitive tasks, pattern recognition, and creative problem-solving. Technology has progressed from being a simple, obedient tool used for basic calculations to becoming an essential assistant, a strategic navigator, and a collaborative co-pilot that works alongside us in real-time. As the capabilities of AI rapidly evolve through breakthroughs in machine learning and generative models, so does the internal demand for AI projects to transform critical business processes and secure a competitive edge. Yet, when faced with limited technical expertise, legacy infrastructure, compressed timelines, and finite funding, a significant challenge emerges. How do you effectively collate all this diverse AI project demand, evaluate your options fairly based on potential impact, and prioritize and deploy your most valuable AI projects successfully to ensure long-term growth and a high return on investment?

AI Project Transformation Opportunities

What can AI projects deliver? As it turns out, quite a lot.

Machines will never really be intelligent from a human, sentient, perspective. However, they have been trained on a LOT of data, and they have learned a lot. As the training data availability, techniques, and processing power have geometrically increased, the effectiveness of deep learning to derive the appropriate weights to apply to multi-dimensional inputs to produce meaningful outputs has dramatically improved.

The consumer-accessible evidence of these Large Language Models was brought to the fore with the public release of ChatGPT by OpenAI. This is now embedded in Microsoft Co-Pilot and broadly accessible via Edge and is embedded in Microsoft’s operating system and products. Similar Generative AI solutions include Google Gemini, Llama by Meta, and Claude by Anthropic.

The goals of all these models are description, prediction and prescription. What is it? What’s going to happen next? What to do about it?

Provided the input can be digitized, this type of feedback can be invaluable to the success of AI projects.

Example AI Use-Cases

In our daily lives we probably interact first with an AI system before any human. Every time we unlock our phones just by having them recognize us, we’re benefiting from AI computer vision and classification. Whilst access cards and PIN codes can be lost or shared, our biometrics are uniquely ours. Not surprisingly, therefore, use of AI capability for identification and security purposes is now highly prevalent. Whether by our face, our fingerprints, or voice we’re increasingly being seamlessly authenticated in all walks of life. Automated human identification is transforming point of sale, time and attendance tracking, and access control.

As humans, our natural, most intuitive method of communication is not typing on a keyboard or tapping on a screen. It’s talking. Today, massive advances in Natural Language Processing (NLP)—including highly accurate speech recognition and incredibly life-like speech synthesis—are opening up entirely new, frictionless ways of interacting with technology. What would it mean for your business if your digital sales agents could proactively call out, handle inquiries, and capture complex orders over the phone without your customers ever needing to access your website? How much more productive—and safer—could field and maintenance staff be if they didn’t have to pause their physical work to type reports, but could instead simply describe what they’re doing as they do it? Furthermore, how much valuable time could be saved in professional consultations if the tedious need to manually recall, summarize, and transcribe conversations was entirely eliminated by intelligent, real-time audio capture?

Much human interaction with computers has been to obtain an analysis of past events. We’ve relied on IT to keep track of our expenditure, sales performance, connections and fitness statistics. Now, thanks to this large volume of accumulated history, and the application of machine leaning neural networks, the focus has shifted from descriptive analytics to predictive analytics.

Rather than relying on ever-optimistic human forecasts, AI systems have seen these patterns before and can provide a much less biased and more realistic prediction of future outcomes. What is the most likely forecast of your capital expenditure? Which of your product lines is most likely to be a hit this Christmas? Which customers are most likely to churn? And how could this predictive analysis help you avoid waste and maximize returns?

As AI models mature, they can handle ever more complex tasks. Already AI-powered software applications are automating routine tasks such as invoice processing, bank reconciliation, and material requirements planning. With chain-of-thought models enabling deeper reasoning and more accurate responses, AI is promising to handle much more sophisticated tasks. AI agents will start to manage customer profiling and collections, supplier risk assessments, staff selection, and product design. It is unlikely that current human involvement will be eliminated, as we will still be required to supervise critical actions, but our teams are likely to become much leaner and more productive.

Even in the physical world, we’re seeing a transition from manual to robotic operation. Coupled with computer vision, and reinforcement learning, AI-powered equipment is becoming rapidly more capable and cost-effective. Self-driving cars still crash, but it’s only a question of time before they’re considerably safer than human drivers.

In a similar fashion, robotic manufacturing continues to advance. AI is immune to boredom, illness, or lapses in precision. Incorporating automated systems into production will surely enhance quality, safety, output, and versatility. Manufacturing roles must shift from performing tasks to the instruction and oversight of these machines.

Knowledge graphs help connect and relate organizational data with real-world facts to infer and produce valuable insights beyond the capacity of human recollection. These new insights can help uncover new product and market threats and opportunities.

As an entire organization transitions to an AI-powered future, the amount of digital data will only increase. Through the feedback loop of reinforcement learning, early adopters of AI will benefit from a virtuous cycle of continuous value realization.

Unfortunately, or perhaps fortunately for us humans, there are some things that AI projects don’t do well.