Killer Use Case for Generative AI is Empowering Enterprise Citizens

Following on from a recent HBR post about digital democratization, let’s explore how Generative AI might be the killer use case for empowering enterprise citizens to build their own AI solutions. We set out some use cases and key predictions for the rise of AI-powered enterprise citizens.

Empowering enterprise citizens to use AI is part of what we call a holistic AI approach.

Digital Democratization: Tech Osmosis Empowers Citizens

Citizen Coders and Citizen Technologists (henceforth both called Citizens) are vital for the future of digitally complex enterprises. At Frontier AI, we argue that a key way to accelerate digital-transformation dividends is to amplify the efforts of workers closer to customers or closer to in-flight business problems.

Indeed, per Gartner, CEOs increasingly hold the belief that the answer to unlocking greater digital dividends is placing more technology into the hands of business operators. CEOs expect their CIOs to deliver on this promise.

The reality of modern software tooling is that it is often easier for a business-domain worker to gain access to automation via new tools (like embedded data science – e.g. in Tableau – or no-code super-apps or reverse-ETL) than it is for a highly technical engineer or data scientist to grasp the in-moment need and prioritization of an in-flight business problem.

Put simply, increased automation makes it easier for citizens to cross the “data science” divide than it is for data scientists to cross the “business domain” divide. This is more so in increasingly complex organizations.

There is a kind of digital osmosis whereby efficiency flows more easily from technology to the business domain than vice versa.

This is especially true under conditions of uncertainty and complexity that are here to stay in the modern enterprise.

To avoid the Red Queen effect, orgs should help business workers to focus on solving problems themselves using self-serve techniques instead of spending excessive time explaining and justifying them to data scientists or AI experts.

But how? The answer is GenAI.

Citizen built AI Mesh

GenAI, in the form of code generation, can empowers citizens to become citizen coders (e.g. via  Github Co-pilot and variants of ChatGPT). This trend is already underway, but needs to be accelerated via more determined and systematic methods of adoption.

Already, in some orgs, tools like ChatGPT are only available to a select few. They are the usual suspects: IT control freaks and techno-politicians who see a new career in AI gatekeeping.

Imagine empowering Citizens to create countless AI solutions, including their own fine-tuned or prompt-engineered models. We call this the AI Mesh.

Over time, Generative capabilities will widen to drive more tools. For example, Frontier AI demonstrated to one client the possibility of generating website pages using Transformer technologies that underpin GenAI.

There will be several outcomes that accelerate digital dividends:

1. Citizens will become 10x more productive at solving in-flight business problems.
2. $-for-$, Citizen productivity curves will eventually outstrip the productivity curves of in-house technical specialists, except for a very narrow set of core innovation tasks.
3. Specialists will migrate to these core technical innovation tasks, such as customized AI model production which, in turn, will empower Citizens via a virtuous circle of AI adoption. The mesh will grow exponentially.
4. The delivery model of specialists will increasingly be to provide more and more GenAI tools versus solutions.

Accelerated Context Switching

A key part of Citizen democratization is knowledge management. Citizens will become successful when they can quickly understand moving parts (like datasets). Generative techniques will reduce the burden of understanding details, as models continuously fine-tune themselves.

The bottleneck in productivity will become the context-switching between tasks in a constantly moving landscape.

Efficient context-switching is vital for Citizens’ success. GenAI apps will serve as “mental un-blockers” that can enhance task-switching – i.e. instead of looking things up, the GenAI will do the heavy lifting, even keeping track of where Citizens left off.

Task contexts might include parameters for data flow setup, such as understanding Snowflake models, or locating data sources. The emerging data semantic layer will be integrated into GenAI apps, enhancing semantic search and natural language understanding.

GenAI apps won’t replace data analysts but will boost productivity for non-specialists like Citizen Coders. GenAI tools will enable the generation of insights at the speed of thought, a long-coveted goal. However, achieving this requires making data-tools “LLM-indexable” for easy natural language querying.

Data-tools vendors face the challenge of aligning with the emerging Data Fabric architecture to provide easier data access. Many vendors lag in realizing the importance of making their products LLM-indexable.

Gig Citizens

In working with Generative AI tools, Citizens will learn to adopt a new mode of documentation, or knowledge curation. We can’t say what this will look like, as it hasn’t been invented yet. All of us are all still adjusting to the way interfaces like ChatGPT work and how best to use them in our workflows. This adjustment is part of Model Thinking.

We predict that Citizens will learn to write “notes to self” that are actually “notes to self – and the org – via GenAI” – i.e. document my work in a way that I, or anyone else, can pick up the thread using GenAI.

Powerful new techniques in data sharing, as available via tools like Snowflake, and new modes of AI in data permissions and privacy protection will empower Citizens to work from anywhere and become specialists: RevOps Citizens, SalesOps Citizens, Growth-Hacking Citizens etc.

This will slowly pave the way for what some CIOs are already wondering: what are the opportunities for a “Gig-Citizen” economy as a solution to digital talent. We predict that GenAI will fuel this economy.

Impacting The Bottom Line

Citizen Coding is not a new idea. It is already well underway thanks to the rise of automation and expressive tools like no-code and low-code apps makers.

The so-called Citizen Technologist, or Business Technologist, is just an extension of the Citizen Coder concept: someone outside of IT who knows how to drive technology to get results.

How will this impact the bottom line?

Any Citizen ought to be working towards a set of KPIs that roll-up via a KPI hierarchy to business outcomes directly related to revenue.

Insights drive business. They are any data-driven realization that is actionable against improving a KPI. So, if workers are empowered to generative insights for themselves and make workflow adjustments for themselves, this will positively impact KPIs with knock-on improvement to the bottom line.

This is just one way in which Generative AI will help to accelerate dividends from digital technology investment.