Martin Birch, CEO and Chairman of ibmlhas 20 years of experience as a world leader in intelligent information management.
The adoption of AI is rapidly shifting from an experiment to an essential part of business practices and planning. In every industry, more use cases are being developed for AI to increase business efficiency, optimize data, improve customer experience, and support business goals and initiatives. Companies looking to mature their AI programs should keep an eye out for these important conversations about the adoption of AI tools and technology.
Trend 1: When in doubt, automate
Machine learning is a valuable tool that companies have benefited from for decades, but it has only recently become very popular. In this new era of digital acceleration, companies are looking for ways to increase efficiency in their businesses – and for many, the answer lies in automation.
To be a common misconception that machine learning and automation will take over most human jobs. This rumor was spread largely because of reports indicating that large amounts of displaced jobs were more entry-level positions that could easily replace machines. And while machine learning and AI have taken on the more routine day-to-day tasks of many forward-thinking companies, it actually frees up employees to think, create, and do the work that is unique to humans.
Companies looking to drive efficiency should look to machine learning and automation to streamline processes and increase productivity. Automation through intelligent document processing can improve your paperwork and filing, extract information from documents with near-perfect accuracy, and streamline mailrooms. Consolidate Document Capture through machine learning and automation, it reduces errors and helps your employees free their schedules from the tasks best suited for AI: tasks that are repetitive and procedural in nature, where machines can perform faster and more accurately than humans . Human Intelligence (HI) can then focus on the tasks for which they are ideally suited: the more elaborate tasks, where we can use unique human senses of anticipation, feeling, emotion and imagination. Overall, it’s a win-win for you and your employees. Automation can drive efficiency and accuracy while opening doors for people to do what we are uniquely made to do.
Trend 2: Stronger Security
Cybersecurity is critical to business continuity and AI tools improve an organization’s cybersecurity awareness and attitude. Cybercriminals can exploit many attack vectors, especially as more business functions become entrenched in the cloud and hybrid networks. In addition, staff shortages in cybersecurity continue to put pressure on security teams and organizational budgets.
Machine learning can “understand” and identify patterns and relationships between threats, find these threats faster and, in some cases, propose effective solutions. AI tools can continuously monitor network traffic and execute offensive and defensive responses based on algorithmic protocols and learned experience.
Trend 3: Advanced AI Analytics
The data generated by digital transformation initiatives can provide valuable insights that enable companies to discover more opportunities for business process optimization. Especially for companies and industries that want to integrate paper-based, historical data into analytics models, which will drive current and future business plans through digitization. Artificial intelligence analytics tools pull and process large amounts of real-time and historical data to create deep predictive insights that can be as broad or detailed as needed.
These insights can be used to identify and predict market trends and customer behavior and to analyze risks. These findings can be used to make risk management, supply chain management, data visualization and the decision-making process faster and more effective. Predictive models can even be used to identify and address issues as they arise through continuous monitoring and rapid application of corrective protocols. Improved productivity, reduced downtime and smoother processes are all positive downstream effects of applying advanced AI to analytics systems. Positive downstream effects of powerful analytics are beneficial for improving productivity, reducing employee downtime and driving innovation.
Trend 4: Language Processing 3000
A few months ago, a Google engineer shocked the world by announcing that the company’s LAMda conversation system was aware. He was probably fooled by the massive improvements that have been made to AI-powered language models. Programmers have given machines unprecedented capabilities for natural language processing. Powerful tools can understand and convert text input into source code in multiple software languages and creative writing, and generate unique responses in real-time on conversational AI platforms. Projects such as OpenAI and BLOOM are also democratizing AI language models by opening their platforms to the public, further expanding the possibilities for the software in the future.
Natural language processing (NLP) will have a significant impact on numerous industries. Using NLP technologies in IDP helps IDP address broader use cases across multiple industries that impact operational areas, such as customer experience. NLP technologies integrated into IDP help understand unstructured content from email conversations and chat conversations to extract critical information and direct the customer to the right process and resource within an organization.
The AI market is rapidly maturing and today’s trends will determine tomorrow’s innovations. Evolving within a digital landscape means taking calculated risks to ensure long-term business success. Artificial intelligence is clearly the key to maximizing the abundance of data and information to give enterprises a competitive advantage. However, the real value comes from increasing efficiency and using unique human skills. This is how a true partnership between artificial and human intelligence can lead to real business benefits.
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