AI is significant for us all, not just because of its future potential, but because it’s already addressing the real-world challenges that businesses face today.
With AI, we have quickly developed a tendency to “horizon scan”, looking at hypothetical and long-term risks and opportunities way into the future. It’s easy to see why: generative AI has already had a profound impact, creating excitement and concern around new uses that stray into what was once considered science fiction.
We can, however, temper some of our worst fears by recognizing AI as very much an embedded technology, to be used as and where humans dictate, rather than an unchartered territory. AI tools have already been adopted across industries, bringing benefits to businesses and wider society. Organizations are demonstrating how AI can be used in a responsible, ethical, and effective way, showing that we know how to do “Good AI”, and that it’s now time to use “AI for Good”.
So how exactly do these real-world applications take shape, and what strategies can decision makers implement to make the most of AI?
Boosting daily productivity in the workplace
The impact of AI in streamlining current workplace ecosystems is undoubted. However, the challenge – and opportunity – lies in communicating this effectively to decision makers and key stakeholders across the organization, for more effective by-in.
The variety of AI tools now offer ways to optimize workflows across almost every conceivable industry. From HR onboarding and training to quickly develop new skills, to automating repetitive or tedious tasks allowing workers to focus on the most rewarding parts of our jobs. AI is fundamentally redesigning how we work.
For example, AI has overhauled the approach to cross-industry work processes in manufacturing, construction, and critical infrastructure. From streamlining data analysis and improving feedback mechanisms, to integrating AI laterally across other technologies such as drones and sensors, businesses can optimize for worker safety and efficiency.
The human touch will always be needed, however, to guide how AI can provide value. Operations managers can move workers from dangerous environments, to focus on safe, high value tasks that improve overall productivity and guide embedded tools. Due to the higher value output that AI enables, new pathways to reskill and upskill are unlocked, perpetuating productivity for the long-term.
Utilizing AI to improve sustainability practices
Through growth, digitalization has become a more embedded part of operational strategy, subsequently increasing the profile of sustainability goals. AI helps businesses monitor and report on pollution and emission levels, offering predictive insights into the pathways available avoid poor decisions and improve their footprint, such as through the mapping of potential carbon sinks.
AI analysis is the cornerstone for organizations to improve renewable energy operations and reduce output in a more effective way. When integrated into a wider network of in-the-field sensors and touchpoints, AI becomes an advanced central nervous system – providing proactive, reactive and mitigating feedback for decision makers.
Ultimately, comprehensive data analysis unlocks access to strategies that address sustainability challenges. For example, over 12% of CO2 emissions are as a result of deforestation and environmental fires. Therefore, implementing AI tools enables governments and non-government institutions to paint an enhanced picture of ecological landscapes and map forest degradation.
Supporting the vulnerable with AI-assisted healthcare
AI has been transformative to healthcare in particular. By weaving functionality into established processes, practitioners can improve the efficiency and diagnosis rate of illnesses, as we’ve seen with the University of Helsinki’s work into detecting brain hemorrhages. The AI solution works by assisting radiologists in interpreting CT scans to detect common non-traumatic brain bleads – simplifying improved care delivery.
By matching patterns across historic diagnoses, these solutions allow for deeper insights into complex medical issues. By working hand in hand with medical and social-care practitioners, AI supplements how critical medical data is interpreted and shared across departments. This vastly improves speed of diagnosis and effectiveness of care given.
These are important tools that have improved healthcare functions, through both protecting and uplifting vulnerable communities and supporting those that need it. The inherent benefit of the positive feedback loop AI provides allows caregivers to actively learn from experience in a more efficient way, making current initiatives resilient and long lasting.
Ensuring AI aligns with organizational needs
Businesses need to understand that AI technology will be here to stay. Strong AI strategies consider the purpose and objectives of considering AI, explaining the processes for businesses to prove value and absorb the rapid pace of change, considering the technology itself.
Implementation needs to ensure that solutions mesh effectively with IT infrastructure that’s already in place. Digitalization, digital transformation, and upgrading legacy systems, as overarching initiatives, require planning and understanding of how they will impact wider business functions.
That’s not to say it needs to be slow or cumbersome, however – one of the joys on AI is the ease with which it can put powerful new capabilities in the hands of teams. When due diligence is conducted effectively, AI integration can become the lynchpin to elevate business practices – boosting productivity, efficiency, and lowering costs. The opportunities for improvements cannot be understated, especially when looking at wider settings outside of just industrial or financial sectors.
Ultimately, overreaching when implementing AI, can create a situation where integrated tools muddy the water and dilute the effectiveness of their intended use. By looking at specific use cases, and implementing in a thoughtful and considered manner, organizations can make a tangible impact that avoids “horizon scanning”.
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