How AI Augmentation Will Reshape the Future of Marketing

Marketing organizations are increasingly adopting artificial intelligence to help analyze data, uncover insights, and deliver efficiency gains, all in the pursuit of optimizing their campaigns. The era of AI augmentation to assist marketing professionals will continue to gain momentum for at least the next decade. As AI becomes more pervasive, this shift will inevitably reshape the makeup and focus for marketing teams everywhere.

Humans will retain control of the marketing strategy and vision, but the operational role of machines will increase each year. By 2025, it is projected that 70% of lower-level administrative duties will largely disappear as artificial intelligence tools become more deeply entwined in the operations of marketing departments. Similarly, many analytical positions will become redundant, with smart chatbots expected to assume up to 60% of daily responsibilities.

However, the jobs forecast is not all doom and gloom because the demand for data scientists will explode. The ability to aggregate and analyze massive amounts of data will become one of the most sought-after skillsets for the rest of this decade. By 2028, the number of data science positions is expected to grow by 30%, remaining immune to economic pressures. These roles will be less susceptible to budget cuts, highlighting the critical importance of data analysis in the evolving marketing landscape.

Effects of the AI Rollout on Marketing Functions

As generative AI design tools are increasingly adopted, one thorny issue involves copyright protection. Many new AI solutions scrape visual content without being subjected to any legal or financial consequences. In the year ahead, a lot of energy and effort will be focused on finding a solution to the copyright problem by clarifying ownership and setting out boundaries for AI image creation. This development will drive precious cost and time savings by allowing marketing teams to embrace AI design tools more confidently, without the fear of falling into legal traps.

In addition, AI will become more pivotal as marketing teams struggle to scale efforts for customer personalization. The gathered intelligence from improved segmentation will enable marketing executives to generate more customized experiences. In addition, the technology will optimize targeted advertising and marketing strategies to achieve higher engagement and conversion levels.

By the end of 2024, most customer emails will be AI-generated. Brands will increasingly use generative AI engines to produce first drafts of copy for humans to review and approve. However, marketing teams will have to train large language models (LLMs) to fully automate customer content as a way of differentiating their brands. By 2026, this practice will be commonplace, enabling teams to shift their focus to campaign management and optimization.

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Urbanizing Smart Cities With Digital Twins

Digital twin (DT) is a rapidly growing concept that has gained traction as it can improve product designs, optimize performance at an industrial level, and create proactive maintenance services. This upgrading technology has started taking shape on an entirely new and different scale as it has become the pillar for futuristic smart cities.

In the scenario of smart cities, digital twins work as virtual replicas of the city’s assets, such as buildings, road lighting systems, energy and grid capabilities, and mobility solutions. However, it is not enough to develop a third-dimensional (3D) model of these sources. Therefore, the digital twin of smart cities pairs the 3D information with spatial modeling (for building the environment), simulations and mathematical models (for workable electric and mechanical systems), and other components that use real-time data feeds from the Internet of Things (IoT) platforms.

In this exclusive AITech Park, we will explore how digital twins will help smart cities evolve in 2024.

Twinning With the New Age Smart Cities

With the introduction of digital twins in the construction field, this technology has the potential to unlock data that was traditionally trapped in silos.

When constructing a new building, the digital twin is developed from the initial phases of the project by the architects, engineers, and construction (AEC) teams to work together to define each other’s performance goals and get the desired outcomes. Now, as the project progresses, the data is continuously collected and fed into the model using any digital twin solution. When the infrastructure is handed over to the owner, the virtual twin collects operations data that will fine-tune performance and manage maintenance in the long term.

As the digital twin mostly revolves around data supplies, it’s the physical twin that helps in performing predictions and simulations in response to real-world conditions. For instance, in the construction industry, the physical twin can be used to align a building’s solar facade that follows the path of the sun and modifies airflow to minimize the spread of germs.

Therefore, it is evident that DT allows the AEC teams to connect better throughout the entire assignment lifecycle, from design to decommissioning. Further, integrating static data aids in specifying the segment and creating maintenance schedules based on the dynamic data of occupancy rates and environmental conditions.

When DT is combined with building information modeling (BIM), the AEC team is well connected to data, which processes dynamic, real-time, bidirectional information management, bringing out the full potential of integrated workflows and information sharing with clients.

As DT is integrated with artificial intelligence (AI) and machine learning (ML), this technology will evolve from being a conceptual tool to becoming more competent and autonomous as software capabilities expand. The application areas for digital twins will continue to reach new heights in the coming years and will change the way AEC teams create, use, and optimize physical spaces and multiple processes.

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How The Concept of Digital Twins Can Be Used Within AIOps to Develop Self-Healing Closed Loop Ecosystems

Digital twins have become an influential technology in recent years, particularly in manufacturing or heavy industries such as transportation or energy. A simple definition of a digital twin is a faithful, detailed digital model of a real-world system or process – anything from a consumer product prototype to an entire factory or telecommunications network.  

Digital models make great testing grounds, one significant advantage being that systems can be tested virtually, with any number of ‘what if’ scenarios being run, outcomes examined and changes to the virtual version of the system made instantaneously. It’s a quicker, cheaper, lower-stakes way to test those changes as opposed to making them in the physical version. This parallels software’s move towards agile development, with its smaller, faster feedback loops.

AIOps as a Digital-to-Digital Twin

Interestingly, the concept of digital twins can be a powerful tool within the field of artificial intelligence for IT Operations (AIOps) to develop self-healing closed-loop ecosystems.

To elaborate, a ‘classic’ digital twin is a representation of a piece of physical reality, and very accurate in emulating and predicting the behavior of mechanical components. For example, a jet engine, a manufacturing line, or even a human heart. This digital representation requires a steady flow of data to stay current. It isn’t a closed loop. In addition, any changes that need to be incorporated into the original version of the twin need to be manually added. This creates a delay and the possibility of errors, which can compromise the digital twin’s speed and agility. That in itself limits its value, because the ability to respond quickly to change is a key for success in today’s highly agile business environment.  

By contrast, IT production environments exist solely in a digital reality. While they obviously contain physical elements such as computers, mobile devices, servers, cables and so on, those

only come alive when connected by digital components such as software and data flows. Driven by AI algorithms that enable intelligent automation, digital twins work within AIOps for IT operations, representing the entire IT environment, including hardware, software, and their interactions. This translates to the self-management of IT environments, the ability to predict incidents, offer ways to prevent them, and even suggest improvements when permanently resolving a problem requires a change in the IT environment’s design or architecture.

Taking the principles of digital twins and integrating that into AIOps, organizations can move beyond reactive problem-solving and achieve a proactive, self-healing closed-loop ecosystem that can detect and respond to IT issues in real-time. This approach minimizes manual intervention and allows IT teams to proactively address problems before they impact end-users.

Only digital-to-digital can close the loop seamlessly. Of course, all of this does not mean that humans will lose control of IT as it remains a software platform controlled by IT staff. It does, however, free up IT expertise from repetitive tasks to focus on more complex high value tasks.

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