McKinsey - Gen AI adoption soars as organizations reap early rewards

Welcome back to TalentTech Weekly! In this edition, we delve into the latest 麦肯锡 report, The state of AI in early 2024: Gen AI adoption spikes and starts to generate value.?

In early 2024, the survey gathered input from 1,363 participants, and the results were disclosed last week. McKinsey has confirmed that the data set encompasses a diverse range of regions, industries, company sizes, functional specialties, and tenures.?

We’ll explore the surge in gen AI adoption, the tangible business value it generates, and the emerging best practices of high-performing organizations.

Read the full report here: LINK

AI adoption has skyrocketed to 72%?

If 2023 was the year that introduced us to gen AI's potential, 2024 is the year it went mainstream. According to McKinsey's survey, a staggering 65% of respondents report their organizations regularly use gen AI – nearly double the rate from 10 months ago. This phenomenon is represented globally in various industries, with adoption rates soaring beyond 66% in almost every part of the world.

Artificial intelligence is a broad term that encompasses various technologies designed to emulate human intelligence, and generative AI is a specific subset that focuses on creating new content. Traditional AI systems often analyze and interpret data, make decisions, and solve problems based on pre-existing information. Generative AI uses deep learning techniques to generate new data that resembles the training dataset. This can include creating text, images, music, and even limited 3D worlds.

But the surge isn't limited to gen AI alone. Overall AI adoption has skyrocketed to 72%, a big jump from the stagnant 50% range of previous years. And organizations aren't just testing the waters. Half of the respondents state their companies have adopted AI in two or more business functions, signaling a deep integration of these technologies.

Organizations are primarily incorporating AI in marketing, sales, and product development. Previous studies from 2023 have indicated that leveraging AI in these areas can generate significant value, along with in the IT sector. The most considerable surge from 2023 is observed in marketing and sales, where reported adoption has more than doubled.

Realizing gen AI's use risks

While the report highlights an optimistic view of gen AI's adoption and impact, it also emphasizes the challenges organizations face. Data management, including governance processes, integration capabilities, and access to sufficient training data, emerge as hurdles, particularly for high performers.

Operational challenges, such as implementing agile methodologies and effective performance management, are also important to note. Some organizations have already experienced negative consequences from the use of gen AI, with 44% of respondents saying their organizations have experienced at least one consequence.

These findings highlight the need for robust data strategies and organizational agility as critical enablers for learning gen AI's full potential. As the gen AI revolution continues to unfold, one thing is clear: the early movers and high performers are already setting the pace, demonstrating the value this technology can unlock.?

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Gen AI strategies for capability building

You might ask: How can we actually acquire and deploy these gen AI capabilities in our organization??

McKinsey identifies three distinct archetypes:

• The Takers: Leveraging off-the-shelf, publicly available gen AI solutions with little to no customization.
• The Shapers: Customizing and fine-tuning public models with proprietary data and systems to address specific business needs.
• The Makers: Developing proprietary foundation models from scratch, tailored to unique requirements.

Across industries, the survey reveals that most organizations are currently using the Taker approach, with around half of the reported gen AI uses relying on off-the-shelf public models. However, sectors like energy, materials, technology, and telecommunications are more inclined to follow the Shaper or Maker routes, significantly customizing or building their own models.

The archetype chosen can significantly impact the time-to-value ratio. While organizations typically require one to four months to operationalize gen AI capabilities, those opting for highly customized or proprietary models are 1.5 times more likely to face implementation timelines exceeding five months. This trend emphasizes the balance organizations must strike between rapid deployment and tailored solutions.?

Ultimately, the capability-building strategy should align with the organization's strategic priorities, data maturity, and technology roadmap. As gen AI continues to evolve, we may see more organizations transitioning from Takers to Shapers or even Makers.

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Written by: Alexandra Paasch