Humans are often accused of being biassed. Ruled by emotions. Played by the people surrounding them but with different motives and agenda. So, when it comes to business, do machines, or programs and approaches run by Artificial Intelligence (Ai), have better judgement than humans? Or are they equally guilty of being biassed?
The simple answer is a resounding YES. AI-powered approaches can also be riddled with biases leading to issues such as loss of productivity and revenue, unequal treatment of employees, and possibly loss of business.
Left unchecked, AI is equally capable of committing biases. So how can we then harness the power of AI to spell gth in our business organisation. Here are some very interesting insights from Gartner, a $4.1 billion global leader firm on research and a member of the S&P 500.
Worldwide, Gartner says organisations are increasingly using AI solutions to create new products, improve existing products and their customer base. These comes in the form of machine learning, computer vision, chatbots, and edge artificial intelligence (AI) drive adoption.
Summing up, Gartner says Ai’s landscape was dominated in 2021 by the following trends:
Operationalizing AI initiatives
Efficient use of data, models and compute
Data for AI
Operationalizing AI initiatives - For the majority of organisations, continuously delivering and integrating AI solutions within enterprise applications and business workflows is a complex afterthought. Gartner expects that by 2025, 70% of organisations will have operationalized AI architectures due to the rapid maturity of AI orchestration initiatives. Organisations should consider model operationalization (ModelOps) for operationalizing AI solutions. ModelOps reduces the time it takes to move AI models from pilot to production with a principled approach that can help ensure a high degree of success. It also offers a system for governance and lifecycle management of all AI (graphs, linguistic, rule-based systems and others) and decision models.
Efficient use of data, models and compute - As organisations continue to innovate in AI, they also need to efficiently use all resources — data, models and compute. For example, composite AI is currently about combining "connectionist" AI approaches like deep learning, with "symbolic" AI approaches like rule-based reasoning, graph analysis, agent-based modelling or optimization techniques. The result of combining those techniques (among others) is a composite AI system that solves a wider range of business problems in a more efficient manner. Organisations can apply generative AI that creates original media content, synthetic data and models of physical objects. For example, generative AI was used to create a drug to treat obsessive compulsive disorder (OCD) in less than 12 months. Gartner estimates that by 2025, more than 30% of new drugs and materials will be systematically discovered using generative AI techniques.
Responsible AI - The more AI replaces human decisions at scale, the more it amplifies the positive and negative impacts of such decisions. Left unchecked, AI-based approaches can perpetuate bias leading to issues, loss of productivity and revenue. Moving forward, organisations must develop and operate AI systems with fairness and transparency and take care of safety, privacy and society at large.
Data for AI - By 2025, more than 30% of new drugs and materials will be systematically discovered using generative AI techniques. Disruptions such as the COVID-19 pandemic are causing historical data that reflects past conditions to quickly become obsolete, breaking many production AI and ML models. D&A and IT leaders are now turning to new analytics techniques known as “small data” and “wide data.” Taken together, they are capable of using available data more effectively, either by working with low volumes of data or by extracting more value from unstructured, diverse data sources.
By 2025, Gartner expects that 70% of organisations will be compelled to shift their focus from big to small and wide data, providing more context for analytics and making AI less data-hungry.
e& (formerly known as Etisalat Group) operates in 16 countries, an ardent supporter of innovation, has long incorporated AI in many of its products and services which you can harness to better operate your business, collect data, analyse market trends and so on.
The company has even launched an AI graduate program designed to give Emirati graduates a deeper understanding of technology, cybersecurity, AI, big data and analytics. This unique program, according to e&, is aimed at giving the Emiratis future-ready skills to help them prosper.
More importantly, it shows how the telecom firm is committed to developing and harnessing AI for future use, guaranteeing its customers of its expertise and experience on this front.
Reinforcing AI to make important business decisions gives you that necessary check and balance equilibrium to help you make tough but more results-driven goals.