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The major players in tech are bullish on AI integration to boost profitability.
Microsoft has its eyes on Google to leveraging AI to capture more of the search market.
Meta chairman, CEO, and co-founder Mark Zuckerberg pointed to AI as a driving force behind increasing productivity and profitability. “We had a good quarter, and our community continues to grow,” he said after Meta’s positive quarterly earnings were announced. “Our AI work is driving good results across our apps and business. We’re also becoming more efficient so we can build better products faster and put ourselves in a stronger position to deliver our long-term vision.”
“[A.I. will] impact every single one of our apps and services,” he said, pointing to increased engagement with Instagram. “Since we launched Reels, A.I. recommendations have driven a more than 24 percent increase in time spent on Instagram,” he said, referring to the short-video clip feature that’s a big growth engine for the company and an attempt to reclaim market share from TikTok.
Generally, AI integration will prove a generational shift in the technology landscape, and it will be the businesses that capitalize on this development that will come out ahead. From customer service chatbots to predictive analytics, businesses are focused on AI to optimize their operations, improve customer experience, and gain a competitive advantage.
Here are the likely candidates we believe will see adoption in the next year.
Natural Language Processing (NLP) technology enables machines to understand and interpret human language, which has many practical applications for business. In the next few years, we'll see a significant increase in the adoption of NLP technology for chatbots, virtual assistants, and voice-activated devices. As more businesses adopt these technologies, they'll become more sophisticated, enabling more natural and intuitive interactions with customers.
Robotic Process Automation (RPA) is a technology that enables machines to automate repetitive tasks that were previously performed by humans. It has already made significant inroads in industries such as finance and accounting, and we'll see continued growth in RPA adoption in the coming years.
According to Digital Journal, “the global Robotic Process Automation (RPA) market size was valued at USD 3758.53 million in 2022 and is expected to expand at a CAGR of 24.31% during the forecast period, reaching USD 13871.72 million by 2028.”
As RPA technology continues to improve, businesses will be able to automate more complex tasks and processes, further reducing costs and improving efficiency.
Predictive analytics is a powerful tool for businesses, enabling them to use historical data to predict future outcomes and make better decisions. In 2023, we'll see more advanced predictive analytics capabilities powered by machine learning algorithms and big data analytics. These advanced analytics tools will enable businesses to gain deeper insights into customer behavior and market trends, helping them to stay ahead of the competition.
However, many companies struggle with even knowing what kind of data they have. The key to mastering advanced predictive analytics for your organization is often found in the democratization of data throughout the organization.
Personalization is becoming increasingly important for businesses, as customers expect tailored experiences across all touchpoints. AI is playing a critical role in enabling businesses to deliver personalized experiences at scale.
In the coming years, we'll see more AI-driven personalization powered by machine learning algorithms and predictive analytics. This will enable businesses to deliver personalized content, offers, and recommendations to customers in real time.
According to the Harvard Business Review, “The brands that have had the most success pursue five pivotal practices, which define the craft of building intelligent experience engines. They connect data signals and insights from a constantly expanding range of sources. They reimagine the end-to-end experience as a seamless flow, powered by automated decisions. They activate the experience across channels, connecting touchpoints to engage customers wherever they may be. They fulfill according to the customer’s context, always recognizing who and where someone is. And they test relentlessly, injecting new innovations, rigorously measuring their impact, and understanding how things affect people differently.”
As businesses continue to rely on AI for critical decision-making, there will be a greater emphasis on explainable AI. Explainable AI refers to the ability of machines to explain their decision-making processes in a way that is understandable to humans. This is becoming increasingly important in industries such as finance and healthcare, where transparency and accountability are critical.
According to QuantumBlack AI by McKinsey, “Our research finds that companies seeing the biggest bottom-line returns from AI—those that attribute at least 20 percent of EBIT to their use of AI—are more likely than others to follow best practices that enable explainability. Further, organizations that establish digital trust among consumers through practices such as making AI explainable are more likely to see their annual revenue and EBIT grow at rates of 10 percent or more.”
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