Implementing AI for Cross Functional Impact Across the Enterprise

Group of finance people meeting around a table discussing business. Starting the meeting with introductions

AI for Business has Massive Potential

There has long been a desire by business executives and CIOs to recognize the value of AI across the organization and not just in specific implementations. Given the current economic climate and the desire to automate as much as possible, AI has become an even greater imperative in business, and Generative AI in particular is garnering attention. A combination of recent economic forecasts, talks of a recession, and the great resignation in recent years has led to a dearth of skilled personnel and resources to continue business momentum. AI is a natural solution for companies that see innovation as a key enabler for improving margins and increasing revenue.

To this point, I have had conversations with many executives at various Fortune 500 companies over the past several months, and a common theme abounds: the goal of achieving a 15% or greater increase in annual revenue while at the same time reducing operating costs by an even greater margin. Revenue per employee is an invaluable metric and a benchmark observed by executives in most leading companies. However, achieving these goals with the current (traditional) widely accepted approaches to business and execution is challenging, especially in highly competitive markets such as Financial Services. 

Furthermore, the impact of FinTech in the Financial Services sector has been notable with new entrants eating away at the available market share and placing pressure on incumbents to adapt to new competitors and competing strategies. Engagement with clients requires innovation along with dramatic improvements in back-office functions to stay competitive while at the same time reducing costs.  One of the most prominent challenges facing business executives and CIOs is the adoption of technology and AI as a game-changing approach. AI is hard, and the current approach of throwing highly skilled data science talent at the problem is not only escalating costs; it is taking far longer than anticipated with only sporadic results across the organization.  Furthermore, most of these systems require an unexpected level of maintenance to continuously improve, train, test and adapt to changing business environments. Costs increase, margins reduce, and required skills become a rarity.

A Breakthrough Approach

The set of difficulties related to broad adoption of AI throughout the business is one of the problems that the team of scientists at Charli AI set out to solve. Indeed, it was a primary driver in the development of the Charli AI Ancaeus platform – a platform designed to implement and maintain cross-functional AI “at-scale”.  The AI at-scale reference is not just about performance or production deployment—it has far greater implications. It refers to the ability of an organization to implement and support diverse AI features across functional units and departments. Scalable AI allows an IT organization to gradually roll out capabilities across teams, while simultaneously supporting the business transformation effort that inevitably comes with new technology and new processes; all while supporting the rapid deployment, provisioning, and maintenance of AI.

The Ancaeus platform has breakthrough technology for choreographing AI models, data and metadata, and was designed to support thousands of AI model ensembles in a production environment.  It also supports the sophisticated sequencing, orchestration and interaction between the models that is necessary for each organization and each team.  Cross-functional AI needs to be resilient across multiple use cases without the need for fresh training in every adjacency.  The Ancaeus platform learns how to coordinate AI models and interactions, and to down-select decisions instead of using machine learning to slowly get to a tolerable fit rate.

The tracking and tracing of all the inputs, outputs, interactions, data, metadata, and governance is necessary for AI at-scale.  In the financial services sector, there is a very specific need for governance and compliance.  Therefore, AI at-scale requires “explainability.”  Explainability provides transparency into the decisions that AI makes, and traces that decision process back to the root elements within the data and the models.  It eliminates bias and creates a full audit trail on the automation conducted by the platform—a much needed capability for IT to scale the AI.

The other key aspect of the Ancaeus platform is the need for out-of-the-box capabilities.  Ramp-up time must be measured in hours, not months (or years, in some cases).  Out-of-the-box AI needs to be adaptable to cross-functional requirements such that Sales, Tax, Audit, Equity, Research, and Marketing can all immediately benefit from its capabilities.  Gone are the days (and costs) of spending countless months analyzing, wrangling, training, testing and retraining models for bespoke implementations.  Cross functional and at-scale AI requires out-of-the-box and self-learning.  

AI that Engages with the Workforce

Since the AI needs to operate out-of-the-box, there is a need to train it live and in production.  Training becomes an essential part of not only teaching the AI to improve accuracy, but also to establish trust between the workforce and the AI.  The user interface therefore becomes an important design element in the delivery of AI, more so than the tooling for data scientists to build and deploy models.  The user interface needs to be intuitive enough for the company’s business professionals to actively interact and to capture knowledge across the workforce. 

To achieve a 15% increase in revenue with the equivalent percentage savings in costs, it is necessary to engage with a high-value workforce.  Teach the AI properly and it can automate faster than the business operates.  This is akin to teaching an intern or assistant to quickly get them up to speed.  This effort cannot happen in a black box, and certainly does not happen quickly using “old-school” methods of coding, development, scripts, and rules-based processing that must be developed in a vacuum and evolved slowly over time.  AI can work fast to learn and achieve a level of accuracy and trust within the workforce if the UI is transparent and interactive; and the feedback loop is continuous.  Human-to-AI interaction is a fantastic accelerator to realizing the ROI from an AI investment—indeed it is an unparalleled transformational agent.

Maintaining a Competitive Advantage

AI can certainly provide a competitive advantage, especially if the model is customized and trained to specifically deliver on advantages such as predictive analytics.  However, AI at-scale is designed with out of the box features—so how does this translate into an advantage if everyone starts using the same model or models?  The same circumstance occurs with commonly available pre-trained models that are used in many third party applications. Competitive advantage is a little talked about requirement in the world of automation, especially as it applies to cross-functional AI, pre-trained models, and/or  easily acquired technology.

It is easy to understand how bespoke and custom models provide companies with a competitive advantage—they are proprietary.  But that does not apply to general out-of-the-box approaches unless the platform was designed from the start to allow a company to train the AI individually to maintain and build on that advantage. The Ancaeus platform was designed to learn based on human interaction and feedback loops. It learns and adapts for each team and each company—retaining as well as extending the competitive advantage sought by financial service organizations. Yes, there are baseline out-of-the-box features; but the learnings are captured and retained for each team/organization independently from other Ancaeus platform users.  The knowledge captured and leveraged becomes a durable advantage to increase market share, tap into new revenue streams, and dramatically reduce operating costs.

Easily Extend Beyond the Initial Scope

For AI to be truly cross-functional, it must be easily extended beyond the initial scope of work/pilots for broader adoption within the enterprise.  In the case of Ancaeus, implementation is straightforward, it is a SaaS platform that can be applied for immediate benefit to teams, departments, and groups.  However, even greater benefits can be realized through extensions that fit right into existing enterprise systems and investments such as ERP, CRM, RPA, Teams, SharePoint, data lakes, ML, and other applications or databases. Cross-functional AI will have the ability to tap into these dissimilar data sets and leverage the unique data that an organization has for forward-facing business advantages as well as accelerating projects and programs within the enterprise.  Existing ML/AI efforts can also be accelerated and extended by leveraging the metadata and automation elements of a cross-functional AI platform.

Leveraging AI for cross-functional benefits in the business does not need to be a multiyear effort, or siloed systems. At Charli AI, we want to change how people perceive AI.  It’s not the dark-art or magic that is left to experts in a backroom. It can be used today with immediate ROI and expanded quickly across the organization.

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