The State of AI

Generative AI and the futrue of business from McKinsey

What the State of AI in 2023 Tells Us About the Future

McKinsey recently released their annual report on the State of AI based on a survey of over 1,600 global executives. The report provides a comprehensive look at how companies are leveraging both established and emerging AI technologies in 2023. There are several important takeaways that give insight into the future of AI and its business impacts.

Generative AI Adoption Reaches an Inflection Point

Up to a third of organizations now use generative AI like DALL-E and ChatGPT regularly in at least one business function. This shows that generative AI has progressed rapidly from research projects to real-world adoption. The most common uses are in marketing, product development, and customer service, demonstrating the practical business applications going beyond the usual tech hype cycle - and around 40% of companies already using AI will increase investments because of generative AI's promise.

However, just 21% have formal policies governing generative AI use and only 32% are mitigating risks like inaccuracy. As the McKinsey report emphasizes, this signals there is still substantial work required to integrate generative AI safely and strategically. But its adoption has clearly hit an inflection point and can provide competitive advantage to businesses willing to undertake the necessary work in data, organizational change and cuture (not small things to do).

AI-Driven Companies Widen Their Lead

The research identified a segment of high performing companies where AI contributes over 20% of EBIT. These organizations adopt generative AI far more aggressively than others, especially for revenue generating use cases. They invest 5X more in AI overall than peers.

However, even high performers have room to improve. Only 35% leverage practices like reusing model components versus reinventing them and just 25% have full monitoring and alerting on live AI systems. As AI expands from pilots to large-scale deployments, specialized MLOps skills and rigor become crucial. Leaders must continue building robust systems and operations to match their bold AI strategies.

The Need for Reskilling Outweighs Job Losses

Many fear AI will displace jobs, but respondents predict more employees will be reskilled than separated over the next 3 years. 38% say over 20% of staff will need reskilling due to AI adoption. This indicates organizations see AI as enhancing human capabilities versus wholesale automation. Companies will need to rethink talent strategies, workflows, and culture to make augmentation successful.

While hiring general tech talent has become perhaps a little easier with current cutbacks, most report ongoing challenges acquiring specific AI talent. Creative approaches like upskilling programs, gig work, crowdsourcing, and AI tools themselves will be required to access the expertise needed to scale AI.

Overall AI Adoption Remains Steady But Narrow

55% of companies now use AI in at least one business function, showing gradual mainstreaming. However, only 31% leverage AI across multiple functions and reported financial impacts remain limited for most. Generative AI may spur investment, but broader barriers constrain adoption. Challenges include identifying high-value use cases beyond the obvious (content, customer service) integrating AI operationally, getting buy-in across silos, and measuring impact.

To drive greater adoption and impact, organizations must take a programmatic approach. This includes auditing data and tech infrastructure, developing organization-wide AI literacy, implementing MLOps, and creating cross-functional AI product teams. While generative AI brings excitement, harnessing the full spectrum of AI technologies in an enterprise context remains difficult. Patience and systemic thinking are vital.

The explosion in Generative AI is clearly shaking things up, but leading organizations should continue to combine emerging innovations with robust strategies, improving capabilities, and value focus to maximize results - while minimizing risks.

Developing an AI-Ready Culture

The technical challenges of AI get a lot of attention - data infrastructure, model development, MLOps. However, culture and mindset are equally important for AI success and safety.

Companies need to nurture understanding, literacy and trust across the workforce. Training helps employees anticipate how AI will impact their roles and prepares them to utilize AI tools. and giving workers opportunities to voice concerns and ideas about AI systems will be important. Distributed governance can give diverse stakeholders input on AI priorities and policies.

"Our biggest challenge is balancing the obvious cost savings with AI and employees wondering about their job security"

Head of creative services, FTSE 100 company

Leaders play a crucial role in cultivating openness to AI-driven change. They must clearly articulate the human vision (benefit and challenges) behind AI efforts. And they need to model agile learning and growth mentalities as organizations evolve.

Adopting Responsible AI Practices

With advanced AI like generative systems, ethical risks around bias, misinformation, and misconduct can be big challenges to adoption. Companies will need safeguards - for reputational, financial and cultural reasons.

Responsible AI begins with understanding what goes into AI systems and these should be tested continuously for harmful outputs - human in the loop and human reinforcement during training can help somewhat with these. Policies and controls should limit overly-risky applications. And transparency helps stakeholders interpret AI reasoning and build appropriate trust.

Of course, no framework is perfect. But organizations who invest early and continuously in responsible AI practices will better navigate challenges. Better to be prepared now and put in the right guardrails.

Focusing on Value - And Valuing Focus

The most successful businesses will home in on use cases that generate clear strategic value - enhanced product or service offerings, improved customer operations, transformed decision-making. They also start small with tight scoping, applying test and learn metholodgies before scaling - or discontinuiing

Unfortunately, many organizations spread their AI investments thinly across too many low-impact pilots and proofs-of-concept. This fails to produce material benefits or build sustainable capabilities, which can lead to 'we tried that, it didn't work' syndrome

AI leadership requires focus - picking targets with care then doubling down on what works. It also takes integration with business priorities and processes. When AI is isolated as a pure technology play, it's harder to make impactful and show ROI.

Thestate of AI in 2023 is full of potential - still to be fully realized. Turning possibility into practical advantage takes foresight, responsibility and focus. The businesses who are leaning in, testing, adopting new ways to incorporate AI in their strongest use cases are more likely to gain a competitive advantage.


Roy Murphy is co-founder of G3NR8 writes about generative AI, exponential technology and how brands can keep competitive in a rapidly shifting world. He has 20+ years of experience advising businesses on strategy, products and services to drive revenue and has worked with organisations including Sony, Oracle, BBC, Disney, Bayer, Perrigo, The British Museum and Absolut

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