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7 AI Trends for 2023 to Rock Your Business World
What are the most important AI technology trends that business leaders need to invest in to grow customer satisfaction and business revenue in 2023?
According to McKinsey, top-performing businesses invest twice more in new technologies to build digital businesses than their peers. But for business leaders, the real challenge is deciding which digital technologies to invest in and ensuring these technologies function at full potential with high ROI.
So, what are the most important AI technology trends that business leaders need to invest in as they enter 2023?
1. Generative AI
Generative AI is starting to enter mainstream adoption. For example, Sequoia predicts that by 2030, generative AI will be able to put together scientific papers and visual design mock-ups, write, design, and code smarter and more efficiently than humans. Businesses can use it to produce content, such as articles, whitepapers, press releases, blogs, or social media posts.
Generative AI can also help with client segmentation by learning from the available data to predict the response of a target group to advertisements and marketing campaigns. It can synthetically generate outbound marketing messages to enhance upselling and cross-selling strategies. In addition, it can transcribe board meetings or customer service calls which can, in turn, be analyzed for insight and action.
By 2025, Gartner estimates that generative AI will produce 10% of all data. It will contribute towards 50% of pharmaceutical discovery/development, and by 2027, 30% of manufacturers will use generative AI to enhance their product development effectiveness. Therefore, it would be remiss of businesses not to pay close attention to this technology, how it works, and its practical application in business operations.
To survive an ever-evolving business environment, organizations need to apply automation across their operations. Businesses need to look to hyperautomation as a solution to the staff, customer, and economic challenges they face.
Gartner describes "Hyperautomation" as the orchestration of multiple technologies, tools, or platforms such as artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), business process management (BPM), integration platform as a service (iPaaS), and other types of decision, process, and task automation tools.
There is an urgent need to address service, capability, and capacity gaps within organizations. Variations in work demand necessitate far greater flexibility in workforces than ever before. Recruiting, training, and maintaining large communities of permanent workers must meet the ever-changing needs of modern organizations.
3. Fast IT
To compete in an operationally efficient and cost-effective manner, businesses will need to automate as many processes as possible and use digital workforces. Fast IT can solve business costs and growing workforce challenges in a cash-constrained business environment.
It’s a new category of technology composed of low-code/no-code development tools, RPA, and APIs, all infused with AI, providing CIOs a toolbox to help them digitize fast and inexpensive, leveraging legacy systems and existing databases. Fast IT allows companies with legacy infrastructures to move at high speed and be competitive against digitally native organizations that developed their infrastructure on client-centric workflows. On top of that, the deployment doesn’t have to be done by the IT professionals only, but by the business users too, becoming a co-production between the two.
4. Conversational AI
There is nothing more natural than a conversation. It is true in our personal lives, and it’s also true in our business life. Customers value intelligent conversations, much more so during uncertain times. Great conversations generate engagement, brand loyalty, and, ultimately, great business.
The conversation is not a new interface, but it’s the oldest. It is how we have interacted with one another across the ages, making it the most natural and friendliest form of human interaction. Born from the challenge to build machines that could understand and respond to human language, Conversational AI is a synthetic brainpower wired to understand, process, and generate speech just like a human being would. Think of conversational AI as the 'brain' that powers a virtual AI agent.
Conversational Business Applications (CBA) use conversational AI and an application development environment to create a layer of conversational intelligence on top of existing business applications for customers and staff.
The AI brain behind CBA learns from every interaction with an employee or customer, creating a layer of organizational knowledge that everyone in the company can easily access. For example, through conversational AI, companies can enable customers to complete an entire end-to-end online mortgage application using voice, video, or text. No agent is required; thus, firms can benefit from great service availability at much-reduced costs and provide better customer experiences to satisfy their growing demands.
Conversational AI will become a critical tool in any client-facing business. Why? It will enable businesses to deliver consistent, personalized services at scale and at an affordable price, 24/7.
5. Cyber AI
When a network enlists AI and ML to enhance its security stance, it uses cybersecurity AI. As cyberattacks grow in volume and complexity, it is increasingly difficult for humans to keep up, never mind responding to the speed, complexity, and scale of any potential attacks they face.
As companies digitize and generate ever-increasing amounts of data, the number of cyber threats they face will grow. Cyber AI improves firms’ security posture by providing enhanced detection, protection, and remediation of cyber threats thanks to its ability to detect and proactively respond to cyber-attacks.
AI's capacity for perpetual learning is its most useful feature in cybersecurity. Deep and machine learning enables AI to understand attack behaviors and identify behavioral biometrics to enable cyber-AI systems to successfully detect and respond to cyber threats with less ongoing human intervention.
6. Intelligent Document Processing (IDP)
IDP solutions use AI, natural language processing (NLP), and ML technologies to help businesses process large volumes of unstructured and semi-structured data, e.g., images, forms, invoices, emails, contracts, etc. IDP platforms still require thousands of data samples to develop and improve their accuracy. Nevertheless, IDP solutions have progressed beyond simple character recognition systems that converted character images into machine-encoded text. Now IDP platforms can use computer vision, deep learning, and ML models to help businesses classify, categorize, extract, validate and organize a wide range of data at speed and scale.
Across all industries, businesses are facing pressure to do more—and to do it faster—with fewer skilled resources. IDP emulates human understanding and comprehension of context to save effort, time, and cost, all while reducing the risk of human error. IDP platforms will free employees from detailed and laborious document processing work to concentrate on more interesting, higher-value activities.
7. Digital Twins and AI
"Digital twins" is not a new term, but when paired with advances in AI, they become increasingly valuable in intelligently transforming operations or creating new value. Gartner defines a digital twin as a digital representation of a real-world entity or system. Data from multiple digital twins can be aggregated for a composite view across many real-world entities, such as a power plant or a city, and their related processes.
Digital twins are virtual simulations of real-world processes, operations, or products that can be used to test new ideas in a safe digital environment. AI enhances these capabilities by enabling the technology to look at ‘what-if’ scenarios, run simulations, and provide previously unavailable insights. For example, in a manufacturing plant, product quality can be enhanced by reducing defects, or product manufacturing costs can be reduced by reducing waste materials. Factory up times can be increased by undertaking preventative maintenance, as predictive AI can help identify machinery defects in advance of their happening. As organizations incorporate technology solutions to drive new revenue streams and keep costs down, they will increasingly turn to AI-powered digital twins to find answers.
Organizations are undergoing a digital transformation journey. They are modernizing their existing tech stacks and investing in new applications, data, and infrastructures. They are more focused than ever on improving experiences for customers and employees as a key to improving revenue, margins, and retention.
But business and technology professionals are inundated with too many "spinning plates." There is too much data, too few skills, and limited time. Yet, in 2023 a key element of organizational success will be the speed and agility with which they can digitize their business processes. So, companies will need to invest in AI solutions to enable them to compete in these extraordinary times.
AI will be everywhere. It has moved from nice to have to become indispensable. Forbes says the AI software market will grow to $37bn by 2028 and unleash unprecedented efficiency gains for businesses.
A new era of enterprise digitization has arrived. A set of AI technologies was created to match human ingenuity's speed and agility. And that is revolutionary.