The AI/ML Analyst Value-Add to the Business

Today, risk-averse business processes can impede innovational opportunities. The systemic nature of today’s business processes creates limitations in understanding the desire and demands of today’s customers because of the unwillingness to increase the risk profile across the organization.

The role of an AI/ML analyst is critical in both pre-service and post-service contract operations. Since most business models today are constantly leveraging the market for value capture and value creation, they can be prone to generate increased risks to the firm’s operations (e.g., structure, processes, and assets).

For example, a telecommunications company today would look to assess AI/ML vendors while at the same time understanding the impact of disrupting current and traditional vendor and supplier management models. Because intelligent systems are omnipresent and encompass numerous environmental variables with degrees of pre-configured bias in machine learning models (e.g., Supply, Demand, Labor, and Social/Economics), these intelligent agents can aid in the early identification of emerging trends while augmenting employee decision-making.

A few example areas of AI/ML technology that a telecommunication’s company may be missing for increasing innovational and new market revenue opportunities:

  • Implementing Virtual Call Centers (Chat Bots, Virtual Agents) that remain deficient in solving for the customer issue.
  • Disruption to traditional advisory models (content creation using Brave rewards instead of the traditional McKinsey/Gartner sourcing models, and consulting frameworks, and the continued sunk costs in the monetization of Knowledge Capital).
  • Failure to identify the convergence of ML, Blockchain, 5G, and IoT (Smart Cities, Supply-Chain, Transparent Economic Modeling).
  • Overlooking the value generated from data mining in Shared Value Ecosystems (Platform Economics, Network Effect, Critical Mass).
  • The ambiguity from using systematic business processes from Natural Language Processing (NLP) and Customer Sentiment Analysis (CSA) that are counter-productive in creating competitive advantages and leading to missing critical new market opportunities earlier in the trending.

Differing Perspectives between Systems Engineers and AI Developers

Systems Engineers (SE’s):

  • SE’s generally align with existing business processes to provide an effective basis (from within the processes themselves) for systems procurement. 
  • SE’s best practices typically align with traditional sourcing model methods (e.g., best practices Vendor Management) that are often implemented by outside advisory and consulting firms.
  • SE’s working in traditional business processes force commitment early in the design phase that limits innovational opportunities that an AI/ML developer looks to identify and monitor throughout the length of the contract (e.g., SCRUM team sprints to meet project deadlines) established by the Project Management Office (PMO) and Vendor Management Office (VMO). 
  • SE’s objectives are to establish a clear relationship between process enhancement for specifications and designs.
  • SE’s are mostly concerned with checking for defects introduced during the building process with minimal focus on if the system is actually solving the user challenges.
  • SE’s assume the design to be correct because it is based on the specifications of the system.

AI Developers (AI DEV):

  • AI DEV addresses augmenting or enhancing the human element through the use of intelligent agents.  
  • AI DEV uses knowledge-intensive methods.
  • AI DEV is more concerned with software and domain integration than systems engineering processes and specifications.
  • AI DEV requires domain understanding to formulate ideas with users.
  • AI DEV will identify functional and non-functional requirements.
  • AI DEV will use abstract modeling to convert user requirements into technical specifications.
  • AI DEV will develop a requirements document to achieve consensus between the end-user and developers. 

AI/ML Analyst Value Add

The role of the AI/ML Analyst operates in tandem with the Vendor Management Office (VMO) while at the same time performing the Relationship Management attributes (common in best practices VMO organizations) with the AI/ML vendors constantly measuring against expected service, value maximization, and budget controls.

The AI/ML Analyst can solve for:

  • Leveraging risk in current IT/Business Processes that are entrenched in today’s ecosystems.  
  • Provide synthesis and communication between systematic business process models and AI/ML enhancements while reducing friction in today’s business ecosystems.
  • Aid in the early identification of new market opportunities and prevent innovation stalls.
  • Assess and remedy business process risks that limit the exposure to new market opportunities.
  • Monitor costs against benchmarks using best practices and business intelligence. 

5-examples of AI/ML Analyst Due-Diligence should perform for AI/ML Vendors

  1. How are disruptive strategies affecting the company’s top clients? Consulting revenue depends on client prosperity, and meeting investor obligations.
  2. How does the company prevent knowledge loss? AI/ML vendors depend heavily on the intellectual capital of employees, yet some companies face high levels of voluntary attrition, and with them, account knowledge.
  3. How dependent is the firm on a dominant customer? Some vendors rely heavily on one or two key clients; the non-diversification can lead to increased risks if a key client should terminate the agreement.
  4. How many projects does the firm have on the books? The backlog is an important predictor of future cash flow but also means susceptibility to not applying the finishing touches.
  5. How many professionals and administrative staff follow the ethical standards for developers? Understanding the division of skill sets in your supplier ensures quality and reductions in agent bias.

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