A Probable Future Role of the AI/ML Analyst in the Supplier Management Office

Utilizing emerging technologies to reduce operational costs and increase a firm's productivity can be counter-productive and introduce new risks to the firm's revenue, operations, and strategy.?Using intelligent algorithms that monitor and amplify human decision-making is the tradeoff for adopting these innovations. To mitigate the risks associated with suppliers of these technologies, we need unique skills that are sometimes very hard to come by.

For example, a telecommunications company might want to evaluate vendors who use AI to create a certain perceived advantage translated into a functioning algorithm that perform the required task. At the same, the company will also have to consider the implications of disrupting the current business model due to onboarding a company that offers an intelligent system supported by a unique and limited set of skills. Among the reasons for non-adoption is the lack of industry standards with these technologies and insufficient case studies to achieve consensus with neophytic stakeholders throughout the organization. The lack of awareness can also signify a failing company strategy incapable of adapting to new technologies. Integration with technology partners stagnates because of these archaic dependencies - for example, companies that still use COBOL on a mainframe in an in-house data center.

Some common signs of innovational pitfalls:

• Investing in virtual call centers (Chat Bots, Virtual Agents) that fail to solve customer issues resulting in sub-par customer satisfaction metrics.
• Failing to acknowledge the inefficiency of conventional advisory models in adopting emerging technologies. Change orders and consulting fees based on Statements of Work increase complexity via a centralized governance model. Content creation models, such as those using Brave's Basic Attention Token (BAT)/ or tipping the author for a specific piece of content used in a decentralized manner, do not require lengthy consulting contracts or high fees.
• The lack of organizational fluency leads to misunderstandings of emerging technologies, such as Blockchain, Web3, and the Internet of Things (IoT). In many cases, System Engineers operate within the parameters of the business functions, leaving them with little room to identify disruptive and augmenting technologies in the early stages of industry adoption.
• Ignoring the value of data mining in shared value ecosystems; can also indicate technological ignorance (staff) or arrogance (leadership), causing this lack of innovational awareness within the organization.
• Consistently failing to see the role of decentralized models in the future (dAPPs and DAOs) and their role in future business operations.

Because innovation stalls can occur for many reasons, the pitfalls described above are only a few. Some of this is likely due to the skillsets that existing Systems Engineers (SEs) use, whereas AI/ML Developers, albeit are those who use similar skill sets but perform an entirely different role within the organization.

So, how are the perspectives of Systems Engineers (SEs) and AI/ML Developers different?

The following could serve as a reference to help distinguish between these two kinds of skillsets.?

• In general, SEs align with existing business processes to provide a sufficient basis to operate from (from within the process itself) that systems procurement use for supplier onboarding. AI/ML Developers use different techniques to augment or enhance the human element through intelligent agents using knowledge-intensive methods to capture the user requirements that are then synthesized into an algorithmic process.?
• The best practices used by SEs are often created from the advisory of traditional integrator models that are often implemented by outside advisory and consulting firms. AI/ML Developers, on the other hand, will develop a requirements document to reach a consensus between themselves and the end-user homogeneously, without process interference, decreasing the administrative and consulting oversight requirements.?
• SEs who work with traditional business processes are bound by commitments from the outset, limiting opportunities for change as new trends emerge. An AI/ML Developer, on the other hand, would understand the user's needs to develop ideas that align with the domain to keep pace with the innovational requirements.?
• In many cases, SEs are primarily concerned with identifying defects introduced during the building process, with little focus on whether the system solves the user challenges. The advantage, however, is a clear relationship between specifications and design.?AI/ML Developer operates with a high degree of fluidity and will develop a requirements document to achieve consensus between the end-user and developers. The use of abstract modeling to convert user requirements into technical specifications satisfies the user requirements by adjusting the builds as necessary.?
• Unlike SEs, who presume that the design is correct because it is based on the system's specifications, AI/ML developers will continuously monitor user and system performance to facilitate continual improvements to the design, spawning further innovations.?

AI/ML Analyst Value Add

The AI/ML Analyst is responsible for identifying algorithmic enhancements to current business operations while managing the value generated from the external AI/ML Service Providers to perform these tasks. In addition to the pre-and-post-service-management tasks, relationship management is required to successfully integrate the provider and achieve the milestones throughout the agreement's lifespan. The AI/ML analysts manage expected user services, value maximization, strategy alignment, and budget control goals during this time.

An AI/ML Analyst's role could entail one or more of the following:

• create technology hedges against the internal pace of innovation for IT and business processes through the identification of algorithmic enhancements into the business;?
• assists in providing the synthesis and communications between systematic business process models and the transformation into AI/ML enhancements that help close the perceived innovation gap;
• contribute to the early identification of new algorithmic opportunities and prevent innovation from stalling;
• perform the tracking and reporting of these innovation metrics.?

Five actions an AI/ML Analyst could perform for AI/ML Developers during the selection process for vendor services:

1. How are disruptive strategies impacting the company's top clients??Consultancy revenue is dependent on client success and meeting investor obligations.
2. What are the company's strategies for preventing knowledge loss??Most AI/ML vendors rely heavily on their developers' intellectual capital, yet a high percentage of employees leave voluntarily, never fully documenting the customer's technology stack or supporting solutions documents.
3. How dependent are they on a single, dominant customer??As a result, some vendors may rely heavily on one or two key clients; the lack of diversification can increase risk if a key client terminates the agreement and can be a sign of limiting perspectives on achieving the innovational gains.
4. How many projects does the firm have on the books??Backlog is an important predictor of future cash flow; however, it also can point to the risk of not finishing the project when the cash flow falls below a certain threshold.
5. What percentage of professionals and administrative staff follow ethical standards for developers??The skills division will reduce agent bias and ensure algorithmic and output quality.

Adopting emerging technologies developed by third parties that develop algorithmic enhancements could significantly impact business operations. System Engineers often provide the framework for systems procurement, creating a stall for innovations provided by non-traditional service providers. As companies transform into Web3 decentralization and adopt algorithmic enhancements, the risk increases exponentially using existing traditional and centralized governance through cloud-based service integrator models. Soon, customer interactions will explain how newly forming innovations will change current business strategies (e.g., customers in Metaverse). An AI/ML Analyst operating alongside a best practices Vendor/Supplier Management Office can provide the right oversight and skill set to determine if the use of highly innovative services can improve customer value.

Are you interested in learning more about how our AI/ML analyst can integrate into your business? If so, please feel free to DM me directly, and let's talk about it!

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