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Workday AI and ML Innovation Summit: Chasing the Eye of the AI Storm

We are in a time of transformational change as the awareness of artificial intelligence (AI) grows during a time of global uncertainty. The labor supply chain is fluctuating quickly and the economy is on rocky ground as interest rates and geopolitical strife create currency challenges. Meanwhile, the commodity supply chain is in turmoil, leading to chaos and confusion. Rising interest rates and a higher cost of money are only adding to the challenges faced by those in the global business arena. In this world where technology is dominant in the business world, the global economic foundation is shifting, and the worlds of finance and talent are up for grabs, Workday stepped up to hold its AI and ML Innovation summit to show a way forward for the customers of its software platform, including a majority of the Fortune 500 that use Workday already as a system of record.

The timing of this summit will be remembered as a time of rapid AI change, with new major announcements happening daily. OpenAI’s near-daily announcements regarding working with Microsoft, launching ChatGPT, supporting plug-ins, and asking for guidance on AI governance are transforming the general public’s perception of AI. Google and Meta are racing to translate their many years of research in AI into products. Generative AI startups already focused on legal, contract, decision intelligence, and revenue intelligence use cases are happy to ride the coattails of this hype. Universities are showing how to build large language models such as Stanford’s Alpaca. And existing machine learning and AI companies such as Databricks are showing how to build custom models based on existing data for a fraction of the cost needed to build GPT.

In the midst of this AI maelstrom, Workday decided to chase the eye of the hurricane and put stakes in the ground on its current approach to innovation, AI, and ML. From our perspective, we were interested both in the executive perspective and in the product innovation associated with this Brave New World of AI.

Enter the Co-CEO – Carl Eschenbach

Workday’s AI and ML Innovation Summit commenced with an introduction of the partners and customers that would be present at the event. The Summit began with a conversation between Workday’s Co-CEOs, Aneel Bhusri and Carl Eschenbach, where Eschenbach talked about his focus on innovation and growth for the company. Eschenbach is not new to Workday, having been on its board during his time at Sequoia Capital, where he also led investments in Zoom, UIPath, and Snowflake. Having seen his work at VMware, Amalgam Insights was interested to see Eschenbach take this role and help Workday evolve its growth strategy from an executive level. From the start, both Bhusri and Eschenbach made it clear that this Co-CEO team is intended to be a temporary status with Eschenbach taking the reins in 2024, while Bhusri becomes the Executive Chair of Workday.

Eschenbach emphasized in this session that Workday has significant opportunities in providing a full platform solution, and its international reach requires additional investment both in technology and go-to-market efforts. Workday partners are essential to the company’s success and Eschenbach pointed out a recent partnership with Amazon to provide Workday as a private offering that can use Amazon Web Service contract dollars to purchase Workday products once the work is scoped by Workday. Workday executives also mentioned the need for consolidation, which is one of Amalgam Insights’ top themes and predictions for enterprise software for 2023. The trend in tech is shifting toward best-in-suite and strategic partnering opportunities rather than a scattered best-in-breed approach that may sprawl across tens or even hundreds of vendors.

These Co-CEOs also explored what Workday was going to become over the next three to five years to take the next stage of its development after Bhusri evolved Workday from an HR platform to a broader enterprise software platform. Bhusri sees Workday as a system of record that uses AI to serve customer pain points. He poses that ERP is an outdated term, but that Workday is currently categorized as a “services ERP” platform in practice when Workday is positioned as a traditional software vendor. Eschenbach adds that Workday is a management platform across people and finances on a common multi-tenant platform.

From Amalgam Insights’ perspective, this is an important positioning as Workday is establishing that its focus is on two of the highest value and highest cost issues in the company: skills and money. Both must exist in sufficient quantities and quality for companies to survive.

The Future of AI and Where Workday Fits

We then heard from Co-President Sayan Chakraborty, who took the stage to discuss the “Future of Work” across machine learning and generative AI. As a member of the National Artificial Intelligence Advisory Committee, the analysts in the audience expected Chakraborty to have a strong mastery of the issues and challenges Workday faced in AI and this expectation was clarified by the ensuing discussion.

Chakraborty started by saying that Workday is monomaniacally focused on machine learning to accelerate work and points out that we face a cyclical change in the nature of the working age across the entire developed world. As we deal with a decline in the percentage of “working-age” adults on a global scale, machine learning exists as a starting point to support structural challenges in labor structures and work efforts.

To enable these efforts, Chakraborty brought up the technology, data, and application platforms based on a shared object model, starting with the Workday Cloud Platform and including analytics, Workday experience, and machine learning as specific platform capabilities. Chakraborty referenced the need for daily liquidity FDIC requests as a capability that is now being asked for in light of banking failures and stresses such as the recent Silicon Valley Bank failure.

Workday has four areas of differentiation in machine learning: data management, autoML (automated machine learning, including feature abstraction), federated learning, as well as a platform approach. Workday’s advantage in data is stated across quantity, quality associated with a single data model, structure and tenancy, and the amplification of third-party data. As a starting point, this approach allows Workday to support models based on regional or customer-specific data supported by transfer learning. At this point, Chakraborty was asked why Workday has Prism in a world of Snowflake and other analytic solutions capable of scrutinizing data and supporting analytic queries and data enrichment. Prism is currently positioned as an in-platform capability that allows Workday to enrich its data, which is a vital capability as companies face the battle for context across data and analytic outputs. 

Amalgam Insights will dig into this in greater detail in our recommendations and suggestions, but at this point we’ll note that this set of characteristics is fairly uncommon at the global software platform level and presents opportunities to execute based on recent AI announcements that Workday’s competitors will struggle to execute on.

Workday currently supports federated machine learning at scale out to the edge of Workday’s network, which is part of Workday’s differentiation in providing its own cloud. This ability to push the model out to the edge is increasingly important for supporting geographically specific governance and compliance needs (dubbed by some as the “Splinternet“) as Workday has seen increased demand for supporting regional governance requests leading to separate US and European Union machine learning training teams each working on regionally created data sources.

Chakraborty compared Workday’s approach of a platform machine learning approach leading to a variety of features to traditional machine learning feature-building approaches where each feature is built through a separate data generation process. The canonical Workday example is Workday’s Skills Cloud platform where Workday currently has close to 50,000 canonical skills and 200,000 recognized skills and synonyms scored for skill strength and validity. This Skills Cloud is a foundational differentiator for Workday and one that Amalgam Insights references regularly as an example of a differentiated syntactic and semantic layer of metadata that can provide differentiated context to a business trying to understand why and how data is used.

Workday mentioned six core principles for AI and ML, including people and customers, built to ensure that the machine learning capabilities developed are done through ethical approaches. In this context, Chakraborty also mentioned generative AI and large language models, which are starting to provide human-like outputs across voice, art, and text. He points out how the biggest change in AI occurred in 2006 when NVIDIA created GPUs, which used matrix math to support the constant re-creation of images. Once GPUs were used from a computational perspective, they made massively large parameter models possible. Chakraborty also pointed out the 2017 DeepMind paper on transformers to solve problems in parallel rather than sequentially, which led to the massive models that could be supported by cloud models. The 1000x growth in two years is unprecedented even from a tech perspective. Models have reached a level of scale where they can solve emergent challenges that they have not been trained on. This does not imply consciousness but does demonstrate the ability to analyze complex patterns and systems behavior. Amalgam Insights notes that this reflects a common trend in technology where new technology approaches often take a number of years to come to market, only to be treated as instant successes once they reach mainstream adoption.

The exponential growth of AI usage was accentuated in March 2023 when OpenAI, Microsoft, Google, and others provided an unending stream of AI-based announcements including OpenAI’s GPT 4 and GPT Plugins, Microsoft 365 Copilot and Microsoft Security Copilot, Google providing access to its generative AI Bard, Stanford’s $600 Alpaca generative AI model, and Databricks’ Dolly, which allows companies to build custom GPT-like experiences. This set of announcements, some of which were made during the Workday Innovation Summit, shows the immense nature of Workday’s opportunity as one of the premier enterprise data sources in the world that will both be integrated into all of these AI approaches.

Chakraborty points out that the weaknesses of GPT include bad results and a lack of explainability in machine learning, bad actors (including IP and security concerns), and the potential Environmental, Social, and Governance costs associated with financial, social, and environmental concerns. As with all technology, GPT and other generative AI models take up a lot of energy and resources without any awareness of how to throttle down in a sustainable and still functional manner. From a practical perspective, this means that current AI systems will be challenged to manage uptime as all of these new services attempt to benchmark and define their workloads and resource utilization. These problems are especially problematic in enterprise technology as the perceived reliability of enterprise software is often based on near-perfect accuracy of calculating traditional data and analytic outputs.

Amalgam Insights noted in our review of ChatGPT that factual accuracy and intellectual property attribution have been largely missing in recent AI technologies that have struggled to understand or contextualize a question based on surroundings or past queries. The likes of Google and Meta have focused on zero-shot learning for casual identification of trends and images rather than contextually specific object identification and topic governance aligned to specific skills and use cases. This is an area where both plug-ins and the work of enterprise software companies will be vital over the course of this year to augment the grammatically correct responses of generative AI with the facts and defined taxonomies used to conduct business.

Amalgam also found it interesting that Chakraborty mentioned that the future of models would include high-quality data and smaller models custom-built to industry and vertical use cases. This is an important statement because the primary discussion in current AI circles is often about how bigger is better and how models compete on having hundreds of billions of parameters to consider. In reality, we have reached the level of complexity where a well-trained model will provide responses that reflect the data that it has been trained on. The real work at this point is on how to better contextualize answers and how to separate quantitative and factual requests from textual and grammatical requests that may be in the same question. The challenge of accurate tone and grammar is very different from the ability to understand how to transform an eigenvector and get accurate quantitative output. Generative AI tends to be good at grammar but is challenged by quantitative and fact-based queries that may have answers that differ from its grammatical autocompletion logic.

Chakraborty pointed out that reinforcement learning has proven to be more useful than either supervised or unsupervised training for machine learning, as it allows models to look at user behavior rather than forcing direct user interaction. This Workday focus both provides efficacy of scale and takes advantage of Workday’s existing platform activities. This combination of reinforcement training and Workday’s ownership of its Skills Cloud will provide a sizable advantage over most of the enterprise AI world in aligning general outputs to the business world.

Amalgam Insights notes here that another challenge of the AI discussion is how to create an ‘unbiased’ approach for training and testing models when the more accurate question is to document the existing biases and assumptions that are being made. The sooner we can move from the goal of being “unbiased” to the goal of accurately documenting bias, the better we will be able to trust the AI we use.

Recommendations for the Amalgam Community on Where Workday is Headed Next

Obviously, this summit provided Amalgam Insights both with a lot of food for thought provided by Workday’s top executives. The introductory remarks summarized above were followed up with insight and guidance on Workday’s product roadmap across both the HR and finance categories where Workday has focused its product efforts, as well as visibility to the go-to-market and positioning, approaches that Workday plans to provide in 2023. Although much of these discussions were held under a non-disclosure agreement, Amalgam Insights will try to use this guidance to help companies to understand what is next from Workday and what customers should request. From an AI perspective, Amalgam Insights believes that customers should push Workday in the following areas based on Workday’s ability to deliver and provide business value.

  1. Use the data model to both create and support large language models (LLMs). The data model is a fundamental advantage in setting up machine learning and chat interfaces. Done correctly, this is a way to have a form of Ask Me Anything for the company based on key corporate data and the culture of the organization. This is an opportunity to use trusted data to provide relevant advice and guidance to the enterprise. As one of the largest and most trusted data sources in the enterprise software world, Workday has an opportunity to quickly build, train, and deploy models on behalf of customers, either directly or through partners. With this capability, “Ask Workday” may quickly become the HR and finance equivalent of “Ask Siri.”
  2. Use Workday’s Skills Cloud as a categorization to analyze the business, similar to cost center, profit center, geographic region, and other standard categories. Workforce optimization is not just about reducing TCO, but aligning skills, predicting succession and future success potential, and market availability for skills. Looking at the long-term value of attracting valuable skills and avoiding obsolete skills is an immense change for the Future of Work. Amalgam Insights believes that Workday’s market-leading Skills Cloud provides an opportunity for smart companies to analyze their company below the employee level and actually ascertain the resources and infrastructure associated with specific skills.
  3. Workday still has room to improve regarding consolidation, close, and treasury management capabilities. In light of the recent Silicon Valley Bank failure and the relatively shaky ground that regional and niche banks currently are on, it’s obvious that daily bank risk is now an issue to take into account as companies check if they can access cash and pay their bills. Finance departments want to consolidate their work into one area and augment a shared version of the truth with individualized assumptions. Workday has an opportunity to innovate in finance as comprehensive vendors in this space are often outdated or rigidly customized on a per-customer level that does not allow versions to scale out in a financially responsible way as the Intelligent Data Core allows. And Workday’s direct non-ERP planning competitors mostly lack Workday’s scale both in its customer base and consultant partner relationships to provide comprehensive financial risk visibility across macroeconomic, microeconomic, planning, budgeting, and forecasting capabilities. Expect Workday to continue working on making this integrated finance, accounting, and sourcing experience even more integrated over time and to pursue more proactive alerts and recommendations to support strategic decisions.
  4. Look for Workday Extend to be accessed more by technology vendors to create custom solutions. The current gallery of solutions is only a glimpse of the potential of Extend in establishing Workday-based custom apps. It only makes sense for Workday to be a platform for apps and services as it increasingly wins more enterprise data. From an AI perspective, Amalgam Insights would expect to see Workday Extend increasingly working with more plugins (including ChatGPT plugins), data models, and machine learning models to guide the context, data quality, hyperparameterization, and prompts needed for Workday to be an enterprise AI leader. Amalgam Insights also expects this will be a way for developers in the Workday ecosystem to take more advantage of the machine learning and analytics capabilities within Workday that are sometimes overlooked as companies seek to build models and gain insights into enterprise data.
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Analyst Insight: The Decision to Replace Legacy Planning Solutions with Workday Adaptive Planning

Today, Amalgam Insights has published the following Analyst Insight: The Decision to Replace Legacy Planning Solutions with Workday Adaptive Planning. This report explores the decisions of eight Workday Adaptive Planning customers interviewed in 2022 to understand why companies chose to switch to Workday Adaptive Planning from another financial planning and budgeting solution.

The decision to choose a planning, budgeting, and forecasting solution is a complex one in 2023 as we have had to adjust to the challenges of more agile planning cycles using a wide range of data, the shift from purely financial planning to a broader array of business planning demands, as well as the need to create more scenarios based on the wide variety of potential business drivers and outcomes that are now potentially anticipated. Planning is treated less as a fixed, deterministic exercise and increasingly as a stochastic and broadly variable process that is ongoing and continuous.

In that light, when does it make sense to consider another planning solution? Our research shows that the following traits were most common in organizations that ended up switching to Workday Adaptive Planning.

Key drivers for switching to Workday Adaptive Planning

To learn more about why these traits showed up and the best practices that these companies discovered for making a solution change for a technology used to support executive demands and managing the cash flow lifeblood of the company, visit the Workday website for a free copy of the report.

This report is also available for purchase on the Amalgam Insights website.

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14 Key Trends for Surviving 2023 as an IT Executive

2023 is going to be a tough year for anybody managing technology. As we face the repercussions of inflation and high interest rates and the bubble of tech starts to be burst, we are seeing a combination of hiring freezes, increased focus on core business activities and the hoary request to “do more with less.”

Behind the cliche of doing more with less is the need to actually become more efficient with tech usage. This means adopting a FinOps (Financial Operations) strategy to cloud to go with your existing Telecom FinOps (aka Telecom expense) and SaaS FinOps (aka SaaS Management) strategies. And it means being prepared for new spend category challenges as companies will need to invest in technology to get work done at a time when it is harder to hire the right person at the right time. Here is a quick preview of our predictions.

 

14 Key Predictions for the IT Executive in 2023

To get the details on each of these trends and predictions and understand why they matter in 2023, download this report at no cost by filling out this quick form to join our low-volume bi-monthly mailing list. (Note: If you do not wish to join our mailing list, you can also purchase a personal license for this report.)

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Evaluating the Selection of Platform-Based and Best-in-Breed Apps for Financial Planning

“Innovation distinguishes between a leader and a follower.”

Steve Jobs

In 2023, we face a series of global planning challenges across accounting, finance, supply chain, workforce management, information technology, and data management. Each of these challenges involves a different set of stakeholders, data structures, key performance indicators, and broader economic and environmental drivers.

In light of this increasingly complex and nuanced set of categories that now make up the responsibilities associated with financial performance management (also known as enterprise performance management; corporate performance management; budgeting, planning, and forecasting, and other buzzwords, but all basically coming back to the financial planning and analysis FP&A role that we have known for decades), companies face a technology-related challenge for managing business plans. Is it better to work with a platform-based approach that allows every user to use the same application to support a variety of accounting and finance use cases including consolidation, close, and planning? Or is it better to use a Best-in-Breed application for business planning?

The basic starting point for evaluating this decision starts with a common sense question for enterprises: is it worth spending money on a standalone planning application or is it better to bundle planning with consolidation and transactional accounting such as an ERP or an accounting platform? In making this decision, companies should look at the following considerations:

Is the solution easy to use? In the 2020s, planning apps should be fairly easy to use, including ease of data entry, the ability to analyze data once it is entered, collaborative planning with other colleagues or budget-holding executives, mobile app support, and the ability to drill into planning data to explore specific deltas, outliers, and budget categories that are of specific interest. Ease of use should also extend to model and scenario management as financial professionals seek to bring a wide variety of potential considerations to enterprise forecasting environments. This ease of use is especially important as planning and forecasting exercises have accelerated in the 2020s based on COVID, supply chain challenges, currency value shifts, inflation, and the looming threat of a potential recession. The[1]  need to support flexible planning scenarios can be challenging to accomplish within the accounting framework of creating a fixed and defined set of data that is fully consolidated and auditable.

Is the current solution integrated with all of the data – including operational data – that is needed from a planning perspective? If spreadsheets are considered, this immediately leads to potential governance and consistency problems as each individual will probably have their own specific assumptions. Suppose companies are using a planning solution as part of their ERP. In that case, the planning solution will likely have access to the majority of accounting data associated with planning. Still, companies then have to see how much of their semi-structured data, third-party data (such as weather, government, or market-based data), and other external data are integrated into a solution. And do these integrations require significant IT support or can they be supported either by the vendor, line-of-business operations manager or even by the end users, themselves?

Is the current planning solution flexible enough to both provide each department with the level of planning they are trying to perform while providing a consistent and shared version of the truth? Over the past few decades, the worlds of enterprise analytics and business accounting have both focused on the idea of a rigid “single version of the truth,” but the reality is that there is no single version of the truth as each individual and each department typically has specific goals, assumptions, terminology, and performance drivers specific to their specific job roles. And the moment that data is officially published or defined as “clean,” it immediately starts becoming outdated.

Accordingly, planning data needs to be organized so that every person involved in planning is able to access a consistent set of metrics while also having specialized views of the operational benchmarks and drivers associated with their specific goals as well as the ability to explore specific “what-if” hypothetical scenarios related to the variability of business situations that the organization may encounter. The operational data needed to support this level of flexibility is not always included as part of a core ERP suite and may need to come from a variety of transactional, payment, process automation systems, workflow management, and project management solutions to provide the level of clarity needed to support enterprise planning.[2] [3] 

From Amalgam Insights’ perspective, the answer to this initial question of planning application vs platform is that it is a bit of a red herring. Consolidation, close, and accounting audits are based on the need to lock down every transaction and document what has happened in the past. This historical view provides guidance and can be reviewed as necessary. But planning and forecasting are exercises in constructing the present and future of a business that requires the need to view the company through multiple lenses and scenarios and need to be altered based on possible business or global activities that may never happen. By nature, financial planning and analysis activities involve some level of uncertainty. Organizations seeking to accelerate the pace of planning and to extend planning beyond pure financial planning into sales, workforce, supply chain, information technology, & project portfolio management, will likely find that the need for near real-time analytics and data management increasingly requires an application that combines analytic speed, collaboration, and the ability to experiment within an application in ways that may conflict with or surpass the rate of accounting. Business planning needs to be a Best-in-Breed capability that allows for the flexibility of what-if analysis, the real-time feedback associated with new data and business considerations, the scale of modern data challenges, and the ability to collaboratively work with relevant business stakeholders. Without these supporting capabilities that can help organizations to independently adjust to the future, financial planning is ultimately a compliance exercise that lacks the impact and strategic guidance that executive teams need to make hard decisions.

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Key Planning Trends for 2023 In the Face of Economic Uncertainty

“The will to win means nothing without the will to prepare”

Boston Marathon champion Juma Ikangaa

2023 is undoubtedly a challenging year to forecast from an economic perspective as the tempest of inflation, stock market volatility, foreign exchange challenges, hiring freezes, supply chain delays, and geopolitical conflicts are creating pressure for companies of all sizes and industries. As companies seek to make sense of a complex world and forecast performance, it is important to take full advantage of planning and forecasting capabilities to provide guidance. Of course, it is important to provide visibility and report to business stakeholders. But beyond the basics, what should you be thinking about as we prepare for a bumpy ride? Here are five key recommendations Amalgam Insights is providing for the business community.

  1. Build a planning process that can be changed on a monthly basis. Even if your organization does not need to plan on a continuous basis, there will be at least one or two unexpected planning events that happen this year that will require widespread reconsiderations of the “annual plan.” The “annual planning cycle” concept is dead at companies after the past three years of working through COVID, supply chain issues, and workforce shortages. This means that planning often has to be updated with new and unexpected data to support a wide variety of scenarios. Locking the plan to a specific structure, schedule, or level of data consolidation is increasingly challenging for companies seeking better guidance throughout the year. If you are not building out a variety of scenarios and tweaking changes throughout the year based on business issues and changes, your business is working at a disadvantage to more nimble and agile organizations.

2. Identify planning anomalies quickly. As businesses review their plans, they will find that they are off-plan more quickly than they have historically been. One example of this is in cloud computing, a spend area that is expected to grow 18-22% in 2023, far above general IT spend or the expected rate of inflation in 2023. Other commodities such as complex manufactured goods and food stocks may fall into this category as well based on production delays, logistical shortages, & new novel diseases interrupt supply chains. The ability to quickly identify spend anomalies that exceed budgetary expectations allows companies to affect spend, procurement, and technologies strategies that may further optimize these environments. By identifying these anomalies quickly, finance can work with procurement both to figure out opportunities to reduce spend and to find alternative providers that can either reduce cost or ensure business continuity to meet consumer demand.

3. Interest rates and the cost of money may incentivize longer sourcing contracts to lock in costs. This lesson comes from the sports world, where baseball players are getting long contracts this year. Why? Because the cost of money is increasing and baseball teams can’t play games without players, leading teams to seek the opportunity to lock in costs. Of course, to do this, companies must budget for the potential upfront costs associated with taking on new contracts. This is a story of Haves and Have-nots where the haves now possess an opportunity to lock in costs for the next few years and take advantage of the value of money over the next couple of years while the Have Nots struggling to visualize their spend may be locked in short-term contracts that will cost more over time. However, this ability to make decisions based on the current cost of money is dependent on the ability to forecast the potential ramifications of locking in cost, especially when those costs represent the variable cost of goods to meet the demand for consumer purchases and services.

4. Cross-departmental business planning requires a data strategy that allows organizations to bring in multiple data sources. Finance must start learning about the value of a data pipeline and potentially a data lake for bringing data into a planning environment, processing and formatting the data properly, and maintaining a consistent store of data that includes all relevant information for modern business planning use cases. In the past, it may have been enough for finance to know that there was a database to support financial and payment information and then an OLAP cube to provide high-performance analytics for business planning. But in today’s planning world where finance is increasingly asked to be a strategic hub based on its view of the entire business, planning data now potentially includes everything from weather trends to government-provided data to online sentiment and even social media. These new data sources and formats require finance to both store and interact with data in ways that exceed the challenges of simply having massive row-based tables of business data.

5. Look for arbitrage opportunities across currencies, geographies, and even internal departments. The valuation of mission-critical skills and resources can be valued very differently across different areas. 2023 is an environment where corporate equity and stock values are lower, the US dollar is strong against the majority of global currencies, and skills and commodities can be hard to find. These are both challenges and opportunities, as they allow FP&A professionals to dig into forecasted costs and see if there are opportunities to go abroad or to look internally for skills, goods, and resources that may be less expensive than the typical markets businesses participate in. Finance can work with sourcing, human resources, information technology, and other departments to proactively identify specific areas where the business may have an opportunity to improve.

As we plan for 2023, it is time to prepare sagaciously so that we are ready to execute when challenges and opportunities emerge. By planning now for a wide variety of potential situations, businesses can make better decisions in critical moments that can define careers and the future of the entire organization.

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The Role of Finance in Enhancing the Value of Workforce Planning

“If you think it’s expensive to hire a professional to do the job, wait until you hire an amateur.”

Red Adair

In 2023, workforce planning is significantly more challenging and requires a combination of headcount, skills, finance, sourcing, and automation management. We are facing a remarkable confluence of labor trends that force workforce management to be more closely tied to financial management. Workforce volatility is at a peak as the Great Resignation has led to a mass exodus that has been augmented in recent months by layoffs from companies that overstaffed in the now-halcyon days of the favorable market that has defined our economic environment over the past decade. At the same time, the demand for specialized talent continues as the need to market, sell, deliver, produce, and digitize is still there for companies that are still healthy. And all this is happening at a time of global inflation and currency exchange challenges leading to cost constraints and explorations of geographic arbitrage and automation to introduce and scale up skills. This financial uncertainty leads to the increasing need for finance to support the details of workforce planning to build better businesses.

But this combination of volatility and demand has led to a more uneven distribution of talent that must be reconciled. From a practical perspective, this means that it is more important to make a business case for each hire that accurately estimates the value of a new employee’s skills and capabilities with the expected revenue per employee ratio that the company seeks to achieve. New employees must bring a combination of organizational fit and rapidly deployable skills to their companies to create value in a timely fashion. Considering that the cost of finding, onboarding, and ramping up a new employee can range from $15,000 to $50,000 based on Amalgam Insights’ estimates, companies face the challenge of ensuring that new employees are put in a position to succeed. From a planning perspective, this means having hardware, software licenses, data access, training, and relevant employee relationships all defined on Day Zero or Day One rather than a penny-wise, pound-foolish approach of attempting to provide just-in-time access as employees demand it.

Workforce planning may also include investing in training or learning and development resources proactively as skills needs are forecasted, as the cost of training can be lower than the cost of hiring a new employee or finding a new consultant. From a financial perspective, it is important to conduct a cost analysis of skills acquisition based on the future-facing needs of the organization. Even in a cost-conscious environment, it takes money to make money. However, workforce investments must be focused on employees who will both create value quickly and have the mindset to provide long-term value through their problem-solving, self-improvement, and collaborative approaches. And in considering the cost of skills, companies need to consider both the need for hard skills such as process automation and machine learning as well as the need to teach and train soft skills such as effective project coordination and corporate communications skills. By accounting for skills that may only be needed on a short-term basis compared to those that represent long-term commitments for an organization, companies can prioritize workforce planning from a more quantitative and business growth-oriented perspective.

As companies consider the full cost of employee skills, companies also have to consider the fully loaded cost of an employee, including the resources and benefits associated with bringing an employee on board. The accounting for supporting employee productivity has become more complex in the face of COVID and the subsequent reimagining of the overhead associated with employees. At the peak of COVID, an estimated 40% of employees worked from home. Based on this trend, it was not difficult for organizations to start scrutinizing the real estate and other long-term assets and leases that have traditionally been seen as depreciable aspects of employee cost providing tax benefits over time. The increasing willingness to move headquarters and other large offices to more tax or cost-of-living-friendly locations as well as the tradeoffs between depreciation, asset sales, and leases are increasingly relevant to structuring workforce planning. Companies must readjust the cost assumptions of their workforce to reflect the new reality of their organization.

This set of assumptions does not simply mean that companies can assume that an employee will be fully remote or fully on-site, as this discussion is driven by a nuanced set of considerations. Remote workers have struggled to onboard and reach full productivity and younger workers have sought mentorship and leadership that has traditionally been provided on-site. On the other hand, experienced specialists point to greater productivity and efficiency when they work in remote environments where they run into fewer ad-hoc distractions and interruptions and can work more flexibly. [1] 

From a planning perspective, this may mean setting up scheduled hybrid assumptions for workforce overhead that include office space and physical resources on a monthly or quarterly basis depending on the roles involved. Real estate and other long-term assets/leases that have traditionally been seen as depreciable aspects of employee cost providing tax benefits over time, but with large office vacancies and the increasing willingness to move headquarters and other large offices, the tradeoffs between depreciation, asset sales, and leases are now increasingly relevant to structuring workforce planning. Amalgam Insights expects that 2023 will be a year where companies are still struggling to find the correct balance of office space and may find themselves overcompensating in ways that affect long-term productivity.

The cost of bringing a workforce to full productivity at scale is seen through a variety of data, including the United States economic census, which shows that small companies under 500 employees make approximately $220,000 per employee while large enterprises with over 5,000 employees make over $375,000 per employee. (SOURCE: United States 2017 County Business Patterns and Economic Census) This difference of over $150,000 speaks to the potential difference in productivity between employees who are fully supported at an enterprise level and their small business counterparts who presumably have less support, brand power, and capabilities to enhance their efforts.

But this increase is ultimately only possible by aligning workforce planning efforts with the financial planning and forecasting efforts that align talent and skills with business strategy and outcomes. Ultimately, workforce planning and business planning must be intertwined to be successful across business demand, skills, onboarding, and overhead.