Leveraging AI for Pay, Performance and Promotions


AI-Powered Chatbots in Customer Service and Engagement

Using AI for customer service in your company is a definite method to save time and money. If you’re like most business owners, you’re constantly searching for fresh, creative ways to improve your enterprise. We’re here to inform you that improving AI customer service is a simple and rapid win.

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How can you use an AI Assistant in conducting pay, performance bonus, and promotion reviews? Why would you want to? The reasons start with easing the time load on BI, HR, and Engineering Managers to make better data-driven decisions. Data-driven decisions help to offset bias which gets a lot of companies into legal trouble. But, today’s teams are more complex combinations of in-house developers, freelancers, contractors, and third-party vendors (IT staffing agencies). All of this factors into the equation framed by talent availability, budgets, and ROI.

The list of advantages goes on but this much will keep us busy… for a few minutes, aye?

Performance Metrics On-Demand

An AI Assistant provides HR managers with almost everything they need “on-demand” when preparing for pay, performance bonus, and promotion interviews:

  • Performance metrics (productivity, efficiency, quality, teamwork).
  • Benchmarks relative to team, project, company, or industry performance.
  • Recommendations relative to pay and promotions.

Also, by simply asking how the developer can improve their performance, an AI Assistant can provide the HR manager feedback to help reinforce the training efforts of the developer’s own engineering manager.

Almost everything? Depends on your organization as some may be too small to have a dedicated HR manager. The engineering manager may be acting as CTO, HR manager, and half a dozen other roles while embracing cross-functionality in an extreme sense!

Either way, when it comes to pay, performance bonus, and promotion reviews, you will still want to talk with the developer and their manager or team lead. Some organizations also conduct peer reviews that are factored into these decisions.

How Does AI Fit into HR Decision-Making Processes?

Our stance is that AI Assistants presently serve best in an advisory capacity by offering Next Best Action capabilities that rely upon human discretion and experience. There are things that today’s AI cannot and should not know, that may factor into these decisions. Overall though, AI is capable of providing three different levels of support:

  • Decision Support – Analytics provides you with real-time access to the data needed to make an informed decision. It may be on you to find actionable insights within the data, then decide upon and implement the best course of action. Applied in HR: You can make data-driven decisions but you still need to do most of the time-consuming heavy-lifting in each employee’s pay, performance, or promotion evaluation.
  • Next Best Actions – AI responds to questions posed to it with a visual representation of the data, data definitions, a summary of the situation, and a course of action with the highest probability of generating the best possible outcome. You can accept and implement its recommendations, or override them completely. Applied in HR: “Evaluations On-Demand” provide you core performance data upfront so you can spend more time talking with the developer and their manager lead for additional insight before implementation.
  • Automated Decision-Making – Decisions are made and implemented directly by the AI with little or no human intervention. Applied in HR: This would be inconsistent with the human element of performance and promotion reviews but could apply perfectly for pay increases.

The Increasing Complexity of HR Decisions

Not too long ago, it was typical for developers in most software development companies to predominantly work in the office. Telecommuting was an exception, not the rule. The Gig Economy and Global Talent Shortage accelerated the trend toward distributed work environments. The pandemic lockdowns effectively standardized it.

Previously, most developers seemed to want to work in the office alongside others. Now that everyone’s been forced to work from home, most don’t want to return to the office. This presents an interesting scenario, perhaps even a dilemma, for employer hiring practices and outsourcing strategies.

On the one hand, there’s a distinction between what it means to have a work-from-home employee and a freelancer. The fully-loaded cost of an “in-house developer” is much greater than a freelancer or most contracted developers. Employment laws vary by location, but you can typically require employees to work specific hours at a specific location, conform to specific rules, and even wear a uniform. With freelancers and contractors, not so much.

On the other hand, the talent shortage is acute enough that companies may not want to upset the status quo. For most managers today it is talent, not funding, that’s the real bottleneck. Even so, budgets, ROI, and company profitability remain major issues. As always though, managers need to juggle these competing interests and ideally show steady improvements in their overall project and team performance.

More Complex Team Composition and Distribution

Technically speaking, anything involving contracted or outsourced development and staffing is a matter of vendor management. Practically speaking, the lines of where one ends and the next begins can get very blurry. Many large companies have more contractors than employees, suffice that your “team” could easily include:

This is likely to be an interesting basis for more collaboration between BI and HR Managers, among others. Either way, organizations still need a transparent view into the cost and performance of “all development.”

  • In-house developers
  • Freelancers and contractors
  • IT staffing agencies for temporary assignments
  • IT staffing agencies for augmented teams – their developers report to you, but most of the agency handles most of the ad

The Big Mag Index

Another element complicating the cost and performance equation is that the value of the dollar (Euro, Yen, Dinar, BitCoin, and Seashell) varies considerably by location.

This can be partially attributed (in part) to Purchasing Power Parity (PPP). The Big Mac Index helps to illustrate PPP in how the same burger costs $7.06 in Switzerland, $5.65 in the United States, $3.46 in China, or just $2.46 in Ukraine.

There’s no appreciable difference in the composition or quality of the Big Mac anywhere. If your “economy” revolved around Big Macs, you’d probably want to go to where you could get the most Big Macs for your Buck. That’d be Lebanon, at just $1.68 each, you could get 3-4 compared to what you’d pay in other markets.

A “Big Mac” Developer Index?

I can’t resist…

Developers are sort of like Big Macs in that they can do their work from… anywhere.

/hiding under the desk as millions of developers around the world virtually pelt me with two all-beef patties, special sauce, lettuce, cheese, pickles, onions, on three sesame-seed buns.

Unlike the Big Mac, however, no two developers are identical.

Developer wages vary just as significantly by country, though not always in relation to the Big Mac Index. Developer skill, experience, work habits, and capacity for teamwork can also vary dramatically. For companies yet to seriously consider their available global talent sourcing options, it’s worth providing two resources to help initial evaluations.

The Global Services Location Index is produced by Kearney, a global management consulting firm that works with three-quarters of the Fortune Global 500. The index evaluates 60 countries by 47 metrics including financial attractiveness, people skills and availability, digital skills, digital resonance, etc. From another angle, SkillValue by Pentalog, is one of a number of technical assessment services that score developers’ technical skills (on over 550 points) by country.

Combining reports like this can help you triangulate which countries are likely to provide access to the quantity, quality, and wage rates of developers. The information can also be helpful in identifying strategic regional market opportunities.

Using AI to Optimize Budgets and ROI

For many managers engaged in software development, the bottom line is developer performance vs. cost. This issue segues quite rapidly into talent availability, performance, and cost. Some of you may be thinking in terms of Euros and Dollars, while others think in terms of “developer hours” as the main unit of measure.

A ton of metrics and other factors play into these equations.

  • How do developer wages correlate to code quality?
    What vendors are performing well and should be allocated more tasks?
  • What’s behind my low-performing vendors and how can they improve?
  • To what extend have my developers or vendors improved since their last review?
  • How can I optimize my available talent for my existing and future projects?

While it may be unfair to generalize on a per country basis, you’d certainly want to factor international wage disparity by performance on a per developer and vendor basis. Companies like Google (or Alphabet) have more contractors than employees,

The average wage of a US developer per the Bureau of Labor Statistics was $110k in 2020. Fully loaded, it easily jumps to $145k. Base average developer wages in Israel run about $87k, $50k in Ukraine, and perhaps $10k in India. But, each country has its own high-end agencies competing with US premium rates.

Don’t get too uppity on wage disparity in this context. Making just $2000 per month in one Eastern European city can be like making $9000 per month in San Francisco. The implications are truly quite radical. In actuality, the developer can live better on their $2k than the other developer getting $9k.

Removing Bias from Pay and Promotion Evaluations

Performance metrics provide an objective means to evaluate whether to offer employment to a freelancer, to increase a developer’s pay, or assess their suitability for promotion. That’s not to say that metrics are or even should be the only basis for making decisions. The individual could be involved in important non-coding efforts, providing a highly specialized skillset, filling an interim role, or any number of value-added functions.

However, if data isn’t involved in reaching a decision, it’s subjective, could be biased, and is essentially guesswork. Eliminating bias in the workplace and the appearance of bias is important for retention and company reputation. The risk grows in relation to company size simply because there are more chances for bias to happen. Apple, Amazon, Cisco, Facebook, Google, Microsoft, Uber, have all been hit by lawsuits over discrimination in one form or another. The United States is an especially trigger-happy environment for bias-based lawsuits.

The core issue is maintaining transparent standards and performance metrics that are accessible to everyone.

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