As every company becomes a technology company, so too must every company become proficient in the art and science of technology adoption. I’ve written about technology adoption and adoption hacking many times before since I work in this area in my professional capacity, with customers. As I wrote in a recent post too, we are on a major new technology adoption curve wrought by AI: Generative AI is here but enterprise adoption is not – here are your strategy choices. As I noted in that article, there are opportunities and challenges for organisations in light of this.

I’m always collecting interesting articles and research for my writing and learning process (mostly I curate and automate them into blog posts). But today they have a specific focus and I wanted to add some input. Naturally I used AI to summarise the main articles with a link if you want to dig deeper :

• Ultimate guide to artificial intelligence in the enterprise: This article provides a comprehensive overview of the benefits, challenges, and best practices of implementing AI in various business domains, such as customer service, marketing, sales, and operations. It also covers the key AI technologies, such as machine learning, natural language processing, computer vision, and deep learning, and how they can be applied to solve real-world problems.
• Enterprise AI Adoption: 5 Key Trends to Watch Out For in 2023: This article identifies five major trends that will shape the adoption of AI in enterprises in 2023, such as the rise of AI-powered platforms, the democratization of AI skills, the integration of AI with other technologies, the ethical and social implications of AI, and the need for AI governance and explainability.
• The AI-powered enterprise: Unlocking the potential of AI at scale (pdf): This report by IBM and Oxford Economics presents the findings of a global survey of 6,000 executives on how they are using AI to transform their businesses. It reveals that the most successful AI adopters are those who have a clear vision, strategy, and culture for AI, as well as the capabilities, data, and governance to support it.
• The state of AI in 2022 and a half-decade in review: This article by McKinsey and QuantumBlack analyzes the trends and developments of AI in the past five years, based on a survey of more than 2,000 executives across industries and regions. It highlights the progress, gaps, and opportunities of AI adoption, as well as the impact of AI on business performance, innovation, and social good.
• 5 Forces That Will Drive the Adoption of GenAI: This article by Harvard Business Review argues that the next generation of AI, or GenAI, will be driven by five forces: the convergence of AI with other technologies, the emergence of new AI paradigms, the availability of massive and diverse data, the demand for personalized and contextualized experiences, and the pressure for sustainability and responsibility.
• Companies aren’t ready to succeed with AI: This article by InfoWorld warns that many companies are not prepared to leverage AI effectively, due to the lack of a clear business case, a coherent strategy, a skilled workforce, a robust data infrastructure, and a trustworthy AI system. It suggests that companies need to adopt a holistic and agile approach to AI, and align their AI initiatives with their strategic goals and values.
• Strategy, Not Technology, Is the Key to Winning with GenAI: This article by Harvard Business Review contends that the success of GenAI depends not on the technology itself, but on the strategy behind it. It proposes a framework for developing and executing a winning AI strategy, based on four dimensions: value proposition, operating model, data and analytics, and organization and culture.
• Businesses Use AI to Improve Products, Services, and Operations: This article by the U.S. Census Bureau reports on the results of a survey of more than 580,000 businesses on their use of AI in 2022. It shows that 11.6% of businesses used AI in some form, mainly for improving products and services, enhancing operations and decision making, and automating tasks and processes. It also reveals the variations of AI use by industry, size, and region.

Conclusions

Based on these latest articles but also the research I have explored so far as part of my new trend report (Future of HumAIn Work), I am drawing the following conclusions. Indeed you can see this as a culmination of my thinking that will likely frame the work in my new trend report. A good way to end my year of writing by way of consolidating my thinking and sensemaking.

1. ChatGPT adoption has been nothing less than phenomenal, beating all other major technology adoption curves in recent decades​.
2. The adoption curve [rate] mostly relates to consumer adoption of ChatGPT​ and now many other equivalent AI technologies are adding to this consumer rate of adoption.
3. Enterprise adoption generally lags consumer adoption of new technologies because an enterprise is complex and there are security risks that require careful navigation.​
4. Forward thinking and innovative enterprises however, are often fast followers (and appreciate the need for speed) when they see disruptive technologies on the horizon that they can leverage​ and which might give them a competitive advantage.
5. They also realize they are different and more complex so will put in place strategies to ensure their adoption of new technology, by employees essentially, is successful and meets their company goals​ and the business outcomes they are striving for.
6. Technologies like Microsoft Viva (disclosure), the platform positioned to transform the employee experience by engaging employees and aligning them to new enterprise strategies, are a key tool when leveraging market or technology disruptions. I wrote a little about that here: The ways and means of technology adoption and strategy.
7. Transforming your company is not easy and the term is applied so liberally and loosely these days so I refer to it advisedly. But I do believe that the advent of new AI technologies does need to consider the wholesale change of the way your organisation must do business.
8. Finally, other than these preceding points, we will only succeed with AI if we consider its use alongside humans, not in isolation. This is a fundamental premise I’m exploring in my new trend report – how will AI make our companies, employees, customers and humanity better.

Adoption Lab

To come to the title of this post, a few words on why should you build an AI culture and what is an adoption lab?

Nothing scientific or verifiable here, just some open riffing and notions.

As mentioned we need strategies plus tools or platforms to support us going forward.

The two fundamental aspects of strategy and tooling need to consider culture.

We know why in the first instance, since otherwise your strategy will be eaten for breakfast.

When it comes to tooling, here too, you need to embrace the low code / no code, citizen development mindset and culture that empower users (once you have the correct guard rails in place).

Empower users (employees) with access to tools to create and innovate but also to learn, collaborate and engage between themselves around that creativity and innovation.

And keep it light on the planning. Experimentation has long been the new planning.

This is what I mean by creation of adoption labs – its is about the right environment for new technologies and mindsets to flourish The outcomes will be creativity, innovation and if channelled in the right way, good business outcomes.

Read my many posts on experimentation to get a better sense of what I mean. And don’t forget to subscribe for more posts like this.