Farewell Tedious Tech: A Four-Point Manifesto for Redesigning Work in a AI Native World

By Leon Gauhman, co-founder and CPO/CSO at digital product consultancy Elsewhen

OpenAI’s ChatGPT has triggered a tsunami of Big Tech investment in AI and sparked an arms race among its big beasts: Musk, Zuckerberg and co. At the same time, media and analyst speculation about how this transformational technology might reinvent business has reached fever pitch.

Amid the torrent of predictions and warnings, companies facing the incoming AI boom have a choice. Either they can join the likes of IBM and BT in their – remarkably bullish – forecast of widespread AI-driven job cuts. Or they can flip the narrative to examine how AI might actually be a force for good (for both employers and employees), in a new, supercharged workplace.

Because the advent of AI is a double-edged sword in the global economy. Yes, its arrival and maturity on the market will inevitably spark the loss of some process-driven jobs. Equally, AI and its variants – Large Language Models (LLMs) and generative AI – have the potential to revive and redesign our relationship with technology in an enterprise context.

It’s no coincidence that Microsoft and Google have just launched LLM-powered workplace collaboration tools that can help with meeting prep, suggest and summarise, creating a kind of real-time workflow dialogue.

More broadly, these technologies can revolutionise labour-intensive workflows, increasing productivity and freeing employees to focus on more creative work. So how can leaders tune into this new AI-inspired vision? Here is a four-point blueprint to get started:

1. A co-pilot for every job

Designers and technologists creating the next wave of workplace tools will work closely with the people using them to understand how they do their jobs and how AI can dramatically re-design that experience.

Good early examples include the GitHub Copilot, which helps developers by explaining a piece of code, or fixing an error, in the real-time context of any given project. Users can “spend less time searching and more time learning” with on-the-go troubleshooting. In effect the underlying LLM is hidden behind a newly designed developer environment.

Another early implementation is workplace collaboration tool Notion, which uses generative AI in tasks such as producing automated next-step lists from meeting notes, or finessing reports or emails with “a one-click photo editor, but for your words.”

But there is potential to go a lot further – creating actionable AI-powered tooling that provides individualised on-the-job collaboration and support that people are excited to use. This includes tools that co-pilot with employees, providing timely and contextually relevant access to data and domain knowledge that would otherwise take days to integrate into the job at hand.

Such tools will also help team members navigate higher abstractions of problems and iterate solutions in real-time. This helps to shape a delightful, flow-like employee experience, while ensuring they control the final outcome.]

2. Easy access to a company’s collective expertise

LLMs are capable of ingesting unstructured company data ranging from PDFs to data on Google Sheets and using learned reasoning to summarise that data in a digestible format.

All of a company’s knowledge about everything from successful trading strategies to great business pitches can be fed into an LLM and assimilated to create conversational tools which employees can easily interact with. Morgan Stanley boasts an internal-facing chatbot powered by GPT-4 that can carry out a detailed search of the brand’s wealth management content,  accessing the combined knowledge of Morgan Stanley Wealth Management. Morgan Stanley says this new capability is equivalent to having their “chief investment strategist, chief global economist, global equities strategist, and every other analyst around the globe on call for every advisor, every day.”

With an intelligent adviser at their disposal, employees have access to contextual domain information on the go and can start innovating, thinking creatively and using their brains in a completely different way.

This development has implications for jobs across all sectors, from coding to the trading floor to lawyers and comms professionals – which have the potential to become exponentially more productive as a result.

3. Reinventing tools for digitally native employees

The emerging Gen Z workforce is made up of digital natives accustomed to operating in highly interactive, fast-moving online environments. Players of Fortnite, Minecraft or Roblox, know how to negotiate and create different abstract concepts, use a wide range of interfaces simultaneously, collaborate with different online communities and are exceptionally creative. From their point of view, being expected to work with legacy enterprise software and tools isn’t conducive to a dynamic working experience.

AI presents an opportunity to capitalise on the Gen Z skills acquired in this new age of immersive digital behaviours. Companies can use LLMs to develop an AI-rich armoury of platforms and tools designed to lean into and maximise an exciting set of digital-first skills values including creativity, flexibility and virtual community.

4. The rise of autonomous AI agents

The next stage in AI’s evolution is a move away from ChatGPT’s text in, text out paradigm, toward autonomous AI agents.  These agents can harness LLMs like GPT-4 to sequence end-to-end tasks with almost human-like ability. One of ChatGPT’s limitations is its lack of memory: each ChatGPT conversation is a new one with the previous conversational context being lost. However, developments like BabyAGI  are moving towards an autonomous agent that has a memory.

Imagine how we could redesign current roles if everyone had access to an intelligent companion capable of executing tasks in a fire and forget mode? What if we could offload boring tasks to a capable AI such as Jarvis, Tony Stark’s AI assistant from the Iron Man films? While it sounds far-fetched, this is the vision of pioneers such as developer Div Garg whose company MultiOn is beta testing an autonomous AI agent.

The next chapter

AI, LLMs and generative AI have emerged at a time when the relationship between employers, employees, work and the workplace is already under intense scrutiny thanks to the disruptive impact of the pandemic.

For companies wanting to get ahead, this tide of new AI offers a tantalising invite. Now, more than ever, brands can get closer to what employees want; using personalised tech as their lever into talent retention and supercharged productivity for tomorrow’s world of work.

Tags: AI
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