Five Ways To Achieve Transformative Outcomes
CEO and Co-Founder at Affine, an AI evangelist, business builder, and entrepreneur at heart. Ensuring growth by transformative solutions.
The 2020s marked the start of a decade of artificial intelligence (AI) truly coming into its own. It is forecasted to grow by 33.2% annually as businesses across domains see clear evidence that AI delivers enormous business benefits. By 2030, AI will create an additional $15.7 trillion in global GDP, a whopping 26% increase. But despite its many advantages like maximizing efficiency, reducing cost, improving customer satisfaction, identifying new product and service opportunities and meeting regulatory compliance requirements, the topic still raises doubts among business leaders worldwide.
Based on my experiences in championing AI as a core driver of business success, here are my top five suggestions for leaders to get the most out of AI in 2022.
1. Have a uniform understanding of AI in your business.
As AI becomes a buzzword, there is increasing ambiguity about what it is and what it can do. Different stakeholders often have divergent views about the role of AI in a business, resulting in the failure of implementations from a gap in universal expectations.
Before embarking on your AI journey, it is critical that everybody in your organization—down to the junior-most employee—has a clear understanding of the purpose, objectives and limitations of AI. Town halls, internal communication and informal discussions should elicit opinions and feedback on proposed changes. Doubts and fears need to be alleviated by the top management. Incorporate AI into your strategic business plans only once the proposals are universally understood and accepted.
2. Understand that AI in 2022 is not where it was in 2021.
AI is among the most quickly-evolving technology innovation fields, and many of its aspects taken for granted in 2021 are not so in 2022. For example, machine learning has taken giant leaps, going from a one-trick pony to an agile framework where continuous learning enables AI to adapt to new tasks quickly. Software development will also see significant changes in which AI can write its code in different programming languages depending on the job it is assigned to complete.
A key difference in 2022 will be the increased focus on multimodal AI, blurring the lines between sound, text and images. So, for example, spoken words could be used by AI systems to create pictures and graphs, or conversely, images to generate songs. Thanks to this, AI’s focus will shift from a modeling-based practice to embrace data-centricity, in which AI will examine data and then design a model that best suits it.
3. Use AI to provide swift prediction results.
AI brings to mind robots and widgets busily working their magic, but that’s just a tiny subset of AI’s capabilities, as they are simply following instructions to work on existing data. I believe that the true transformative power of AI is when it can predict outcomes that manual calculations and gut feelings cannot. Machine learning systems can now process vast amounts of data based on multiple parameters to forecast business outcomes accurately. Hence, to derive maximum value from your AI system, make it the foundation of your strategic planning process.
This year we will see the rise of more agile machine learning frameworks such as codeless ML and tiny ML, which speed up data processing for decision-making. Furthermore, as opposed to the current practice of a single track to teach an ML system, generative adversarial networks will employ a multitrack framework to make ML take quantum leaps in learning and predictive speeds. This will make AI even more central to driving business growth and profitability.
4. Go beyond text.
Computer vision and natural language processing will play an even more prominent part in the AI frameworks in 2022. This essentially means that AI systems can now “see” and “hear” data, leading to a considerable increase in the number of potential use cases of AI deployment.
Advances in deep learning, for example, have expanded the reach of NLP to multiple languages other than English and Mandarin. Universal NLP models deploy cross-lingual embeddings, enabling knowledge transfer from languages with sufficient training data to low-resource languages. Similarly, you can train CV systems to link images to sound, allowing the systems to provide accurate spoken descriptions of images. This, for example, has applicability for systems that help people with vision impairments. Understanding these multimodal possibilities will give business leaders insights into improving their operations and increasing customer delight.
5. Know that responsible AI is your responsibility.
Last, and perhaps the most significant, is the increased scrutiny that AI has come under from regulators and activists in the past year. Privacy concerns, cybercrime, discrimination, hate speech and other harmful outcomes of AI adoption have been flagged and protested against, usually justifiably. As a result, governments worldwide have begun putting regulatory frameworks in place, in some cases even requiring companies to divulge the code of their algorithms.
Business leaders need to be sensitive to these concerns and fears. They should also be prepared to submit to regulations that vary from country to country. But, most of all, they should understand that AI systems are devoid of human emotion and make decisions on cold, complex data. Therefore, it is up to business leaders to build ethics and a corporate governance framework that respects and eases the concerns of all its stakeholders.
Let’s sum everything up.
The year 2021 was when business leaders truly began to feel the effects of AI. However, 2022 will be when they see how quickly AI is evolving. Multimodal AI will expand the scope of AI applicability, creating use cases far beyond what anyone could have imagined a few years ago. But some fundamentals remain true: viewing AI as a fundamental part of strategic planning while educating the entire workforce and business partners about their AI vision.
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