The world of digital transformation is constantly evolving and changing, and so are the tech trends that shape it. In 2024, we can expect to see some amazing workplace technology innovations and breakthroughs in the fields of artificial intelligence, cloud computing, cybersecurity, automation and more. These digital transformation trends will not only enhance our personal and professional lives, but also challenge us to adapt and learn new skills.
One of the driving forces behind these trends is Moore’s law, which states that the number of transistors on a microchip doubles every two years, increasing the speed and power of computing devices. This law has been proven true for decades, and it shows no signs of slowing down.
In fact, some experts predict that by 2024, we will reach the quantum supremacy, where quantum computers will outperform classical computers in certain tasks. This will open up new possibilities and opportunities for solving complex problems and creating new applications.
Some of the specific trends that we can look forward to in 2024 are:
- AI-powered personal assistants that can understand natural language, context, emotions and provide personalized and proactive services.
- Cloud-native applications that can run on any platform, scale on demand, and leverage the power of edge computing and 5G networks.
- Cybersecurity solutions that can detect and prevent threats in real-time, using advanced analytics, machine learning and blockchain technology.
- Augmented reality and virtual reality devices that can create immersive and interactive experiences for entertainment, education and collaboration.
- No code/low code platform explosion, like the Power Platform, enabling amazing innovation, solving business problems and enabling hyper-automation.
More importantly, there are several macro-economics factors and mega-trends shaping business. Next, we explore those macro-trends and opportunities for all businesses in two areas.
Opportunities and Risks
AI-driven business models are the future of innovation and growth. They leverage the power of artificial intelligence to create value for customers, employees and stakeholders. AI-driven business models can optimize processes, enhance products, personalize services and generate insights. They can also enable new ways of working, collaborating and competing.
Someone said, “A business that does not embrace AI will be dead in five years”. This is a bold statement, but it reflects the reality of the digital transformation that is happening across industries and sectors. AI is not a luxury or a nice-to-have feature. It is a necessity and a competitive advantage. Businesses that do not adopt AI will fall behind their rivals, lose market share and become irrelevant.
In a recent survey on Tech Trends from Info-Tech Group, we’ve learned that AI is expected to play a key role in shaping the strategic direction of many IT organizations by 2024. This will be a novel and challenging experience for many of them, as they will have to deal with various risks that come with the emerging business models.
Generative AI is a rapidly evolving technology that IT organizations cannot afford to overlook. As the enterprise requires ever-increasing and ever-shrinking computing power in the digital age, following Moore’s law, IT now has to enable and support new AI capabilities. This will have a profound impact on the enterprises, from their internal operations to their business models. At the same time, IT has to establish a new set of controls that reduces the risks associated with AI. From securing the new systems to preventing misuse, IT departments will have to provide governance for an area that is facing more scrutiny from regulators and legal systems.
IT also has the responsibility to balance the organizational demand for AI’s potential with the need to safeguard the organization from the emerging threats, to shape the terms of this symbiotic relationship that is already underway. This is the era of The Generative Enterprise.
In this post, I review two of the top trends that will shape 2024:
- The Generative Enterprise; creating AI-driven business models
- Boosting cognitive efficiency with automated back-office solutions
1. The Generative Enterprise Creating AI-Driven Business Models
AI opinions often swing from one extreme to another – it will either destroy or save humanity; it will either create or eliminate jobs. But, most IT experts have a more balanced and optimistic view of AI’s impact. According to an Info-Tech survey, AI users are more positive than doubters. Two-thirds of them expect AI to benefit their businesses. Doubters are not all negative – half are undecided, expecting both advantages and drawbacks.
Organizations plan to use AI in strategy and risk management. Among AI users, “Business analytics or intelligence” is the top choice in this category, with over three-quarters planning to use AI there by 2024. Seven in 10 organizations also plan to use AI to detect risks and enhance security by 2024.
Let’s analyze what are the opportunities for the Generative Enterprise
Generative Enterprise is a term that describes the use of artificial intelligence to create new products, services and solutions that are tailored to the needs and preferences of customers, employees and stakeholders. It is a way of leveraging the power of data, creativity and innovation to disrupt existing markets, improve decision making and scale up operations.
Some of the opportunities around disruption are:
- Creating new value propositions that address unmet or underserved needs in the market.
- Challenging incumbents with superior customer experience, quality, and efficiency.
- Exploring new business models that generate recurring revenue, reduce costs or increase margins.
Some of the opportunities around improving decision-making are:
- Using data-driven insights to optimize processes, strategies and outcomes.
- Enhancing human capabilities with augmented intelligence and automation.
- Fostering a culture of experimentation and learning from failures.
Some of the opportunities around scaling are:
- Expanding into new markets, segments and geographies with localized offerings.
- Leveraging network effects and platforms to create positive feedback loops and viral growth.
- Building scalable infrastructure and systems that can handle increasing complexity and demand.
Opportunities are not without risks; here are some of the main ones:
- Ease to replicate: Generative Enterprise relies on data and algorithms that can be easily copied or stolen by competitors or malicious actors. This can undermine the value and uniqueness of the original creations, as well as expose sensitive information or intellectual property.
- Rapid obsolescence: Generative Enterprise can accelerate the pace of change and disruption in the market, making existing solutions obsolete faster. This can create challenges for businesses and consumers who must constantly adapt and upgrade to keep up with the latest trends and demands.
- Ethical concerns: Generative Enterprise can raise ethical questions about the quality, safety and impact of the generated outputs. For example, how can we ensure that the generated content is accurate, reliable and fair? How can we prevent the misuse or abuse of the generated products or services? How can we respect the rights and preferences of the data owners and the end users?
These are some of the risks that need to be considered and addressed when using Generative Enterprise. However, they should not stop us from exploring and embracing this exciting new frontier of innovation and creativity.
Recommendation
As an IT leader, you should work with your business partners to identify how AI can enhance your customer and employee experience. Focus on the challenges your customers and employees face, the pain points they encounter in their day-to-day work and your offerings, and the data you have to train a model. Start with a small-scale project to validate your assumptions and learn from real-world feedback before scaling up your AI transformation.
2. Boosting Cognitive Efficiency With Automated Back-Office Solution
The goal of IT has always been to automate business systems by providing capabilities that enable systems to self-manage and self-optimize according to company objectives to drive efficiency. With generative AI, a broad range of new tasks can be automated toward this goal. These AI models are versatile and flexible, able to process large volumes of unstructured data and provide classification, editing, summarization, new content creation and more.
Therefore, even for organizations that are not transforming their business model around AI, there will be value to gain from streamlining current operations. Some of this increase in efficiency will be delivered by using new applications or web services, such as ChatGPT, but much of it will be delivered through new features in software that’s upgraded with new AI-powered features. With the software as a service (SaaS) model, in many cases, enterprises won’t even need to deploy an upgrade to harness these new features. Existing solutions will be the most likely avenue to add generative AI to many enterprises’ IT arsenal. As an example we’ve seen many vendors integrating Generative AI into their products:
- Salesforce uses generative AI to power its natural language processing and natural language generation capabilities, such as Einstein Voice Assistant, which can transcribe and analyze voice conversations, and Einstein Language, which can generate personalized email responses.
- Adobe uses generative AI to enable its content creation and editing tools, such as Photoshop, which can generate realistic images from sketches or text descriptions, and Premiere Pro, which can generate synthetic voices for video narration.
- Microsoft uses generative AI to support its productivity and collaboration applications, such as Word, which can generate summaries and captions for documents and Teams, which can generate meeting transcripts and action items.
The potential of generative AI is immense, and it will affect different sectors at different rates. The case study will show how the legal sector is an example of a field where generative AI tools are more advanced and used by innovators.
Let’s delve into the opportunities.
- Potential for Cost Reduction and Productivity Enhancement
By automating more cognitive tasks, employees can focus on higher-value activities or fewer resources may be needed to run a process. Organizations will be able to grow and support more business without being slowed down by administrative hassles, though using generative AI will also incur some costs.
- Better Output Quality
By producing a first draft faster, workers can spend more time refining their message and polishing the finer details. Using generative AI to augment workers can lead to improved quality and moderate time savings.
- Convenience of Access
With major enterprise vendors competing to launch new generative AI features, the new capabilities may be included as a bonus component to existing contracts. Organizations can work with vendors where they have established a trusted relationship.
Here are examples of automation across several industries:
- Automate repetitive low-level tasks
- Augment operational staff for better decision-making
- Optimize content creation
- Streamline IT operations
Automation of back office and front office business with AI can bring many benefits, such as increased efficiency, reduced costs and improved customer satisfaction. However, there are also some risks that need to be considered and mitigated. Here are three examples of such risks:
- Ethical and legal issues: AI systems may not always act in accordance with the ethical and legal standards of the organization or the society. For instance, they may discriminate against certain groups of customers or employees, violate privacy or security regulations or cause harm or damage to people or property. Therefore, it is important to ensure that AI systems are transparent, accountable and aligned with the values and norms of the stakeholders.
- Human-AI interaction: AI systems may not always communicate effectively or appropriately with human users or customers. For instance, they may misunderstand the intent or context of the human input, provide inaccurate or irrelevant information, or lack empathy or emotional intelligence. Therefore, it is important to design AI systems that are user-friendly, intuitive, responsive and that can adapt to the needs and preferences of the human users.
- Organizational change: AI systems may not always integrate smoothly or seamlessly with the existing business processes or culture. For instance, they may disrupt the workflow or roles of the human workers, create resistance or conflict among the staff or require significant changes in the skills or training of the employees. Therefore, it is important to plan and manage the change process carefully and to involve and support the human workers throughout the transition.
Recommendation and Conclusion
To take advantage of new generative AI features in the tools they already use, organizations should consult their trusted vendors for opportunities. CIOs should stay updated on new feature releases and any changes to terms of use that may affect them. After ensuring that no additional risk is introduced around sensitive data, there are two ways to realize value: they can either launch a pilot project that targets a specific use case for new features, or they can train business users on how to use new features to enhance their own productivity.
In terms of opportunities, the Generative Enterprise can create AI-driven business models that optimize processes, enhance products, personalize services and generate insights. This can lead to new value propositions, challenging incumbents and exploring new business models. Additionally, AI can be used for strategy and risk management, improving decision-making and scaling operations into new markets and segments.
However, there are also risks to consider. Generative Enterprise relies on data and algorithms that can be easily copied or stolen, leading to a loss of value and exposure of sensitive information. Rapid obsolescence can also be a challenge, as existing solutions can quickly become obsolete in the face of fast-paced change. Ethical concerns also arise, such as ensuring the accuracy and fairness of generated content and preventing misuse or abuse of generated products or services.
To mitigate these risks and take advantage of the opportunities, IT leaders should work with business partners to identify how AI can enhance customer and employee experiences. Starting with small-scale projects to validate assumptions and learn from real-world feedback is recommended before scaling up AI transformations. Additionally, automating back-office tasks with generative AI can boost cognitive efficiency, leading to cost reduction, improved output quality and convenience of access. However, ethical and legal issues, human-AI interaction, and organizational change should be considered and managed.
In conclusion, the digital transformation trends for 2024 include advancements in AI, cloud computing, cybersecurity and automation. AI-driven business models and generative AI present opportunities for innovation and growth but also come with risks that need to be addressed. It is important for organizations to embrace AI and leverage its capabilities while managing the associated challenges.
In our next post, we will address the additional key workplace technology trends for 2024 including the blend of physical and virtual reality, responsible AI concerns and security by design. Stay tuned!
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