Enterprise Advice: Why & How to Get Started with AI by Mark Hewitt
There has been a significant bend upward to the AI hype curve over the past 12-18 months driven largely by GAI (generative artificial intelligence).
Why has the interest and pace of AI adoption increased so rapidly?
Leading companies have started their AI/data strategy journeys
Consumer experience for GAI is direct for the user and easy to understand/experiment with
Data readiness and accessibility has become a strategic enterprise focus
Additionally, the ease of creating pilots for GAI that indicate the promise of measurable impact toward better customer service, improved escalation processes, decreased error rates, and the number of interactions required to complete a task have driven enterprise and executive appetite for innovation.
Despite these early successes, the movement from pilot to production and scale across functional lines of business requires prior proper planning, a sound process, and a focus on outcomes.
We are in the nascent stages of the change as it relates to process, product and services impact and building blocks that make things tangible for employees and organizations alike are critical.
Building Blocks
A key building block is to embrace AI in the context of company vertical (fiserve, retail, CPG, high tech, bio/pharma, healthcare, etc.), functional area (marketing, sales, finance, legal, operations, engineering, etc.) and perhaps horizontal expertise (machine learning, sales/marketing automation, learning management systems, etc.).
An exercise worth undertaking is to have each member of the company reinvent how they might work with an AI and what use cases may be targeted. For example, in examining one’s future job role, it is important to ask:
How might my role change or be changed in the future?
What might AI influence in my role and how I work in the next 12 months?
What changes can I anticipate?
What skills do I need?
What should my learning program be and include?
The above exercise will assist the employee to make an individual learning plan and highlight use cases and perhaps training priorities on which the company and HR team may want to focus. There is also the additional benefit in finding low risk, high impact use cases for internal use and learning without encountering external, reputational risk. One recommendation would be to consider one use case for each line of business to learn and implement. This will also assist to incrementally and safely begin to transform the company at large.
This ‘hub-and-spoke’ model of embracing a future ready organization focuses on task automation, establishing basic employee familiarity, and beginning the enterprise AI journey. Additionally, employee understanding of their AI - my AI - in context of their job will evolve. There is also benefit to the team in “learning how they learn” so that process, product and service innovation becomes atomic and understandable by design.
The second core building block is to establish a way in which to brainstorm ideas with AI, as AI can understand the organization and person, develop ideas, and set the basis for foundational models specific to jobs, the vertical, and the company. Models prompted for ideas can also be error prone or outlandish. Using an additional AI agent(s) to critique the ideas leads to solution refinement alongside employee input. This type of “idea generation” and “solution validation” will require some trial and error.
It may make sense to employ a partner to assist in setting the enterprise foundation for your AI and GAI direction as it moves from proofs-of-concept (POCs) to enterprise scale (brands, product owners, lines of business) and finally to implementation of enterprise grade solutions.
There is a tremendous amount of internal data, product data, and enterprise data to store, manage and utilize effectively. Leveraging a best-in-class consulting partner like EQengineered to accelerate this journey may be of value to ensure alignment with best practices, tactics and techniques to establish success patterns alongside the establishment of initial goals and target outcomes.
One thing is certain - as adoption of AI increases, having a depth understanding across the enterprise is a requirement to remain relevant and competitive. The goal of utilizing AI is not to disrupt, but to streamline tasks and eliminate inefficiencies.