Benchmarking against the Industrial Revolution
The Industrial Revolution was a defining moment in human history which marked a monumental shift from manual labour to mechanised production. Yet, the productivity increase was initially only about 3% per decade between 1600 and 1760, before accelerating to 6% per decade from 1770 to 1860. Still, this era reshaped the global economic landscape and the transition to steam engines marked a new epoch of productivity and economic transformation.
Fast forward to November 2022 with the launch of ChatGPT, and just a year later, AI’s promised impact is so significant that it has attained a central position in the agenda of governments, enterprises and professionals. Literally everyone – both individuals and organisations now regard AI as the essential tech capability to acquire and utilise.
But will it really deliver productivity gains to rival the Industrial Revolution?
Fusing AI into the Fabric of Business
We are at the very early stages of Enterprise AI and the use of tools like ChatGPT to analyse and manipulate specific, proprietary data to improve the productivity of organisations, but we can already make out some trends.
Integration of Existing Solutions
Integrating existing AI solutions into business processes is an obvious move for companies aiming to boost efficiency and foster innovation. These applications range from deep data analysis and predictive decision-making to enhancing customer interactions.
For example, Klarna, the BNPL shopping service that is morphing into a retail bank and payments platform, has incorporated ChatGPT into its shopping experience with an integrated plugin for ChatGPT designed to elevate the online shopping experience with personalised recommendations and shopping advice.
Tailored LLM Solutions
The one-size-fits-all approach to AI is starting to giving way to customised solutions designed to meet the unique challenges and needs of different industries and organisations. These bespoke AI systems can deeply analyse trends, predict outcomes, and provide invaluable insights, leading to more effective and informed decision-making processes.
For example, Bloomberg’s development of BloombergGPT has created a custom 50-billion parameter LLM (Large Language Model) tailored to the financial industry, harnessing a vast dataset of Bloomberg’s financial documents and public data. BloombergGPT conducts financial natural language processing tasks, such as sentiment analysis, entity recognition, and news classification, outperforming existing models in finance-specific areas while also maintaining robust performance in general NLP (Natural Language Processing) benchmarks.
AI’s Profound Impact on Personal Productivity
AI not only promises to revolutionise organisational processes, but also to drastically improve personal productivity. In many fields, AI tools have become an indispensable asset for professionals, augmenting performance in tasks ranging from email drafting to complex project planning. For content creators, the ability to use AI tools as brainstorming buddies and rapidly generate high-quality outputs could free up individuals to focus on more strategic and creative endeavours.
Remember the 3%-6% productivity increase per decade during the Industrial Revolution? To put this in perspective, Boston Consulting Group employees using GPT-4 experienced a remarkable 25% increase in task speed and a 40% improvement in work quality in certain consulting tasks, demonstrating the significant impact and potential of AI in modern work environments. It remains to be seen if this is just a bold claim or whether it has a genuinely material impact overall – it is still too early to tell. But the potential for productivity improvement as a result of AI is so significant that Goldman Sachs estimates a 7% increase of Global GDP over a decade.
Personal Productivity Paths Forward
Content Creation and Curation – we all started by leveraging AI to assist in reading content, and provide summaries, insights, and help us content-create. But these tools can also help in producing drafts, or gathering and synthesising research quickly. Most impressive is AI’s ability to offer creative suggestions that augment the content creation process.
The Automation Path – eliminating repetitive and mundane tasks. This includes scheduling, data entry, or even more complex work such as testing. As an example, Autify features Step Suggestions, a no-code testing assistant powered by OpenAI’s GPT-4.
The Copilot Path – using specialised or general Copilots to enhance performance, decision-making and problem-solving, which could fundamentally change the way we interact with machines. Co-pilots can drastically enhance human capability, analysing vast amounts of data, to provide insights, suggest solutions and predict future trends. For example, Microsoft has already launched its Copilots for Microsoft 365 and Github, and they are evolving fast.
Implications of AI adoption
Changing Work Environments
AI adoption is not just about the technology, but about how work environments need to adapt, in order to maximise its potential. This includes rethinking organisational structures, processes, and workspaces to accommodate collaborative interactions between humans and AI. Most importantly, companies need to embrace continuous learning and experimentation so they can stay ahead in this evolving landscape.
For example, Cerys Hearsey recently discussed the rapid transformation facing HR departments, emphasising the need to evolve from traditional roles to strategic, technology-driven functions. Cerys highlighted the importance of a layered transformation strategy, involving automation, agile methodologies, and continuous improvement to enhance efficiency and support organisational growth.
The Role of Leadership in the AI Era
Leadership in the AI-driven era, as explored by Lee Bryant, demands a transformative approach. Senior leaders should focus on strategic direction and empowering decision-makers at all levels. Middle management needs to evolve into organisational architects, crafting environments that enable teams to excel in a digital landscape. Operational leaders, as the front-line facilitators, should focus on coaching and nurturing team skills. This modern leadership paradigm is essential for harnessing the full potential of AI.
Evolving Skill Sets and Capabilities
As AI becomes more embedded in business processes, the demand for new skill sets and capabilities grows. It’s not just about understanding AI technology, but also about adapting to a landscape where decision-making is increasingly data-driven. This evolution calls for a shift in skill sets – from routine operational tasks to more strategic, analytical, and creative roles.
As Seb Kowalski suggests in a recent link*log – hiring and developing people with a focus on long-term capability development is needed too support a shift in skill sets towards roles that are more strategic and adaptable to changing technology and business needs.
Ethical Considerations and AI Governance
Of course, organisations will need to develop robust ethical frameworks and governance models to ensure AI is used responsibly. This involves not just complying with existing regulations but also proactively engaging with ethical dilemmas and public concerns about AI. Companies will need to balance innovation with responsibility, ensuring that their use of AI aligns with broader societal values and norms.
Mapping the future of AI productivity
We stand at the edge of a new era of productivity fuelled by AI. This revolution could likely have an impact on our productivity that is even more significant that the first Industrial Revolution. The potential for AI to augment human capabilities, streamline operations, and foster predictive problem-solving is exciting. Nobel Prize-winning economist Christopher Pissarides believes that advancements in AI technology like ChatGPT could lead to the realisation of a four-day workweek.
Realising this potential, however, depends on building new capabilities, adapting mindsets, skills, and organisational structures, and the truth is many organisations are still too locked into older models of bureaucratic management to do this. This week, ZDNet reported a survey of technology executives commissioned by Celonis that suggests readiness remains a problem:
“… more than two-thirds (68%) express concern that suboptimal process shortcomings may “hold back further successful implementation of AI — as well as automation and other emerging technologies — in the next two years.”