Discover how Generative AI is set to Transform Innovation, Growth, Productivity and Competitiveness

Today, strong evidence points to generative AI (Gen AI) as the next revolutionary technology, which will result in efficiency gains, new and expanded product offerings and the promise of increased profits. What’s more, Gen AI has the potential to provide a renewed impetus for the development of human know-how, particularly in knowledge-intensive sectors such as finance, healthcare and entertainment, among others.

In the past, intellectual and professional work has largely resisted automation. This is because knowledge work involves unstructured tasks as well as sophisticated problem-solving, which cannot be entirely predefined.

Gen AI's tools are reshaping the corporate landscape by expanding the range of tasks that can be performed and supported by computers – according to McKinsey, up to 30% of all business activities will be automated by 2030.

Gen AI is set to complement large-scale routine automation and structured tasks in various sectors, by adding functions such as interactive idea generation, software development, creative writing and content production.

Corporate spending on Gen AI has doubled in 2024; it is expected to reach $151.1 billion in 2027.

What is generative AI? How is it different from traditional (predictive) AI?

Gen AI is a subset of AI. Gen AI can be thought of as the next generation of artificial intelligence. It’s a form of AI that can instantly create novel and customized content, including text, images, music and computer code.

The main difference between Gen AI and traditional AI lies in their capabilities and applications. Let’s consider the example of a travel agency. It uses Gen AI to create textual and visual elements for eye-catching advertising campaigns tailored to each customer. However, it still relies on traditional AI to provide it with predictive data on the type of vacation each customer prefers, the best time to travel and the price point most likely to be right for them.

Traditional AI excels at "left-brain operations” such as analyzing data and making predictions for informed decision-making.

Gen AI is best used to generate "right-brain creative content”. Gen AI goes a step further by creating new data with the help of training algorithms. Gen AI models are trained on large sets of unstructured data and learn underlying patterns to generate new algorithms that mirror the training data.

Gen AI can act as a valuable cognitive resource for knowledge workers. Of note: Gen AI is particularly useful for bridging skills and experience gaps, especially for new recruits. It helps accelerate the learning curve and boosts productivity. That also means less coaching, mentoring and support from more experienced colleagues.

However, successfully integrating Gen AI into business processes and doing so on a large scale remains a challenge. It will require ongoing, sophisticated adaptation, based on reciprocal learning between users and Gen AI tools, to align with business needs.

Gen AI is powerful alone but better in combination with traditional AI. The "magic" is in finding the right balance between Gen AI and Traditional AI to maximize the impact on business objectives.

Business transformation with humans at the center

At the core of the successful implementation of Gen AI is a focus on people and processes.

Regardless of the specific AI technology used, your investment priority should be on the people who will be doing the work: that is 70% of the budget, according to the Boston Consulting Group (BCG). Deploying new technologies accounts for 20% of investments, and algorithms for 10%.

At the outset of every project, it is humans, not algorithms, who set the parameters for the courses of action that autonomous AI-driven agents will explore. It is humans who maximize value by ensuring that the agents' actions are closely aligned with the business scenarios defined by the company.

In any given organization, the Gen AI talent pool is probably larger than we think, and it's about to grow rapidly. The cohort is not limited to technical talent such as data scientists, software engineers and machine learning specialists, although these roles are important. In fact, a recent McKinsey survey of a representative sample of employees found that only 12% fall into the tech-heavy talent category. The remaining 88% work in non-technical positions that use AI to perform routine tasks. These include middle managers, healthcare workers, educators, and administrators, among others.

As more workers use AI to perform repetitive tasks, critical thinking and decision-making skills will become increasingly important. Free from routine tasks, people can be more creative, collaborative, and innovative.

Boosting productivity through automation and Gen AI

Consider the example of a middle manager at a company who identifies as a non-technical creator of Gen AI. Currently, these mid-level managers report spending almost half of their time on administrative and individual-contributor tasks, and only around 25% on people-related activities.

The middle manager's job will evolve with the automation of administrative and reporting tasks, leaving him more time to spend with his staff and use the technology to enhance the team’s output. The ability to reclaim extra time to take the pulse of the team offers the manager an opportunity to make employees realize how vital their ideas and creativity are to the success of the whole organization, especially as the use of AI evolves.

As teams start using Gen AI and gain proficiency with these tools, they can use the freed-up time to focus on value-added activities and collaboration.

Foster a culture of trust and psychological safety

Leaders will have to foster a culture based on trust to encourage the safe exploration and adoption of Gen AI tools. Such a culture is essential to promote transparent and productive interactions between knowledge workers, but also between knowledge workers and their managers, thereby enabling the collaborative efforts to achieve collective goals.

To this end, it is essential to convince knowledge workers that the use of Gen AI tools is not intended to replace or diminish their role, but rather to enhance and facilitate their work.

Apprehensions about job displacement can lead to negative behaviors, such as avoidance of Gen AI tools or secretive and hazardous application of these tools. This unease can further develop into disengagement at work and a decline in performance.

It's essential that leaders recognize that the adoption of Gen AI tools is not a one-off event, but a continuous journey of learning, innovation, and adaptation.

The risks associated with early adoption of Gen AI

The risks associated with Gen AI range from inaccurate outputs and biases embedded in the underlying training data to the potential for large-scale misinformation and malicious influence on politics and personal well-being.

Thus, a nuanced understanding of Gen AI’s strengths and weaknesses needs to be developed to mitigate the risks associated with its application in areas where it is not advantageous.

Corporations must establish a governance structure that balances expertise and oversight to ensure effective risk management from the outset. This means defining frameworks, guardrails, and core principles to guide the work of designers and engineers and challenge the effectiveness of risk assessment and mitigation. Led by an AI Governance Officer, such a structure needs to cover data risks, data privacy, cybersecurity, regulatory compliance, and technology risk.

Training programs must emphasize user accountability and the importance of critically scrutinizing the quality and accuracy of Gen AI-generated outputs. End users of Gen AI tools will play a key role in helping identify risks associated with the use of Gen AI, as they may experience problematic outputs in their interactions with the model.

Final thoughts: What can executives do about Gen AI?

Gen AI is still decades away, at the very least. But AI is here to stay—and it is advancing extremely quickly. We need to think of AI as a powerful resource that can assist and augment human creative abilities and workmanship, rather than replace them (transition into the age of AI).

Here are a few things to consider:

· Stay informed about developments in AI and Gen AI

Connect with start-ups and develop a framework for tracking progress in Gen AI that is relevant to your business. Also start thinking about the right governance, conditions and barriers for success within your company and communities.

· Invest in AI now?

Does the current state of AI align with your business strategy? You should assess the extent to which your strategic ambitions can be fueled by AI technologies that are ready to be consumed and used today. Should you pause or even deprioritize technological experimentation until the risks are better understood – while the regulatory environment around AI continues to evolve in the US and in Europe? Most notably, can your corporate culture absorb, let alone embrace, transformative and disruptive technologies that will radically change the way you do things? Do you have the right skills, talent and mindset, particularly at executive level, to make a proper incursion into the AI game?

· Continue to place humans at the center

Invest in human–machine interfaces, or “human in the loop” technologies that augment human intelligence. People at all levels of an organization need training and support to thrive in an increasingly automated world. AI is just the latest tool to help individuals and companies alike boost their efficiency.

· Consider the ethical and security implications

This should include addressing cybersecurity, data privacy, and algorithm bias.

· Build a strong foundation of data, talent, and capabilities

AI runs on data; a solid base of high-quality data is critical to its success.

· Organize your workforce to achieve new economies of scale and skills

Yesterday's rigid organizational structures and operational models are no match for the reality of rapidly advancing AI. One way to remedy this is to institute flow-to-work models, where people can move seamlessly from one initiative to another and from one group to another.

· Place small bets to preserve strategic options in areas where your business will be exposed to AI developments

Consider, for example, investing in technology firms that are pursuing ambitious AI research and development projects in your industry. Not all of these bets will pay off, but they could help hedge some of the existential risk.

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