The two aspects that I am most interested in across-the-board are:
The AI generated testing in Agentforce Testing Center where I think it is going to be vital for agents to be stress tested with the help of AI. It will obviously be easier for AI to bring up a wide variety of potential tests for an agent.
In the next few months, it will honestly be fairly trivial to build a standard agent within most large enterprise application platforms. But the challenge will be in testing these agents to run at enterprise scale, and with the variety of languages, context, grammar, jargon, and patois that may exist across the world in describing demands.
As George Bernard Shaw said “England and America are two countries separated by a common language”. and that can be multiplied by the countries and rules and backgrounds that global companies are trying to support with their Salesforce agents.
The other part that I most excited about is what Salesforce calls Utterance Analysis. This is a real time analysis on the usage of an agent based on the user inputs, requests, and query outputs. There has long been a struggle in translating event logs into useful data simply because logs are overwhelming. Salesforce’s efforts in this area are an important step forward in incorporating log data into more practical and consumable analytic form factors.
The one big question this press release does not tackle is around the orchestration and ongoing management of agent portfolios. Is it possible to find duplicate or similar agents and avoid the technical debt associated with managing 100s or thousands of agents going forward? It is a stated goal of Mark Benioff to have 1 billion agents built in a year. That is a great goal, but anyone who has ever worked IT or in sales ops knows that 1 billion custom objects, workflows, tests, agents, or any other documented item is always going to be an administrative burden.
Although I believe that Salesforce is making progress in this area, it is no secret that we look to Salesforce as providing a standard around enterprise governance for CRM and related applications. And I think this is an opportunity for Salesforce to show leadership in the ongoing management of agent portfolios at a time when the data and metadata in Salesforce are increasingly important to the valuation of the company as a strategic partner and to a publicly traded market capitalization.
Amalgam Insights recently had the privilege of attending Informatica World 2024. This is a must-track event for every data professional if for no other reason than Informatica’s market leadership across data integration, master data management, data catalog, data quality, API management, and data marketplace offerings. It is hard to have a realistic understanding of the current state of enterprise data without looking at where Informatica is. And at a time when data is front-and-center as the key enabler for high-quality AI tools, 2024 is a year where companies must be well-versed in the various levels of data governance, management, and augmentation needed to make enterprise data valuable.
Of course, Informatica has embraced AI fully, almost to the point where I wonder if there will be a rebrand to AInformatica later this year! But all kidding aside, my focus in listening to the opening keynote was in hearing about how CEO Amit Walia and a group of select product leaders, customers, and partners would help build the case for how Informatica increases business value from the CFO office’s perspective.
Of course, there are a variety of ways to create value from a Data FinOps (the financial operations for data management) perspective, such as eliminating duplicate data sources, reducing the size of data through quality and cleansing efforts, optimizing data transformation and analytic queries, enhancing the business context and data outputs associated with data, and increasing the accessibility, integration, and connectedness of long-tail data to core data and metadata. But in the Era of AI, there is one major theme and Informatica defined exactly what it is.
Everybody’s ready for AI except your data.
Informatica kicked off its keynote with an appeal to imagination and showing “AI come to life” with the addition of relevant, high-quality data. Some of CEO Amit Walia’s first words were in warning that AI does not create value and is vulnerable to negative bias, lack of trust, and business risks without access to relevant and well-contextualized data. His assertion that data management (of course, an Informatica strength) “breathes life into AI” is both poetic and true from a practical perspective. The biggest weakness in enterprise AI today is the lack of context and anchoring because of dirty data and missing metadata that were ignored in an era of Big Data when we threw everything into a lake and hoped for the best. Informatica faces the challenge of cleaning up the mess created over the past decade as both the number of apps and volume of data have increased by an order of magnitude.
From a customer perspective, Informatica provided context from two Chief Data Officers during this keynote: Royal Caribbean’s Rafeh Masood and Takeda’s Barbara Latulippe. Both spoke about the need to be “AI Ready” with a focus on starting with a comprehensive data management and integration strategy. Masood’s 4Cs strategy for Gen AI of Clarity, Connecting the Dots, Change Management, and Continual Learning spoke to the fundamental challenges of anchoring AI with data and creating a data-driven culture to get to AI. As Amit Walia stated at the beginning: everybody is ready for AI except your data.
Latulippe’s approach at Takeda provided some additional tactics that should resonate with financial buyers, such as moving to the cloud to reduce data center sites, purchasing data from a variety of sources to augment and improve the value of corporate data as an asset, and consolidating data vendors from eight to two and increasing the operational role of Informatica within the organization in the process. Latulippe also mentioned a 40% cost reduction from building a unified integration hub and a data factory investment that provided a million dollars in savings from improved data preparation and cleansing. (In using these metrics as a guidepost for potential savings, Amalgam Insights cautions that the financial benefits associated with the data factory are dependent on the value of the work that data engineers and data analysts are able to pursue by avoiding scut work: some companies may not have additional data work to conduct while others may see even greater value by shifting labor to AI and high business value use cases.)
Amit Walia also brought four of Informatica’s product leaders on stage to provide roadmaps across Master Data Management, Data Governance, Data Integration, and Data management. Manouj Tahilani, Brett Roscoe, Sumeet Agrawal, and Gaurav Pathak walked the audience through a wide range of capabilities, many of which were focused on AI-enhanced methods of tracking data lineage, creating pipelines and classifications, and improved metadata and relationship creation above and beyond what is already available with CLAIRE, Informatica’s AI-powered data management engine.
Finally, the keynote ended with what has become a tradition: enshrining the Microsoft-Informatica relationship with a discussion from a high-level Microsoft executive. This year, Scott Guthrie provided the honors in discussing the synergies between Microsoft Fabric and Informatica’s Data Management Cloud.
Recommendations for the CFO Looking at Data Challenges and CIOs seeking to be financial stewards
Beyond the hype of AI is a new set of data governance and management responsibilities that must be pursued if companies are to avoid unexpected AI bills and functional hallucinations. Data environments must be designed so that all business data can now be used to help center and contextualize AI capabilities. On the FinOps and financial management side of data, a couple of capabilities that especially caught my attention were:
IPU consumption and chargeback: The Informatica Data Management Cloud, the cloud-based offering for Informatica’s data management capabilities, is priced in Informatica Pricing Units based on its billing schedule. The ability to now chargeback capabilities to departments, locations, and relevant business units is increasingly important in ensuring that data is fully accounted for as an operational cost or as a cost of goods sold, as appropriate. The Total Cost of Ownership for new AI projects cannot be fully understood without understanding the data management costs involved.
Multiple mentions of FinOps, mostly aligned to Informatica’s ability to optimize data processing and compute configurations. CLAIRE GPT is expected to further help with this analysis as it provides greater visibility to the data lineage, model usage, data synchronization, and other potential contributors to high-cost transactions, queries, agents, and applications.
And the greatest potential contribution to data productivity is the potential for CLAIRE GPT to accelerate the creation of new data workflows with documented and governed lineage from weeks to minutes. This “weeks to minutes” value proposition is fundamentally what CFOs should be looking for from a productivity perspective rather than more granular process mapping improvements that may promise to shave a minute off of a random process. Grab the low-hanging fruit that will result in getting 10x or 100x more work done in areas where Generative AI excels: complex processes and workflows defined by complex human language.
CFO’s should be aware that, in general, we are starting to reach a point where every standard IT task that has traditionally taken several weeks to approve, initiate, assign resources, write, test, govern, move to production, and deploy in an IT-approved manner is becoming either a templated or a Generative AI supported capability that can be done in a few minutes. This may be an opportunity to reallocate data analysts and engineers to higher-level opportunities, just as the self-service analytics capabilities a decade ago allowed many companies to advance their data abilities from report and dashboard building to higher-level data analysis. We are about to see another quantum leap in some data engineering areas. This is a good time to evaluate where large bottlenecks exist in making the company more data-driven and to invest in Generative AI capabilities that can quickly help move one or more full-time equivalents to higher value roles such as product and revenue support or optimizing data environments.
Based on my time at Informatica World, it was clear that Informatica is ready to massively accelerate standard data quality and governance challenges that have been bottlenecks. Whether companies are simply looking for a tactical way to accelerate access to the thousands of apps and data sources that are relevant to their business or if they are more aggressively pursuing AI initiatives in the near term, the automation and generative AI-powered capabilities introduced by Informatica provide an opportunity for companies to step forward and improve the quality and relevance of their data in a relatively cost-effective manner compared to legacy and traditional data management tools.
This Week in Enterprise Tech, brought to you by the DX Report’s Charles Araujo and Amalgam Insights’ Hyoun Park, explores six big topics for CIOs across innovation, the value of data, strategic budget management, succession planning, and enterprise AI.
1) We start with the City of Birmingham, which is struggling with its SAP to Oracle migration. We discuss how this IT project has shifted from the promise of digital transformation to the reality of being in survival mode and the cautions of mistaking core services for innovation.
2) We then take a look at Salesforce’s earnings, where the Data Cloud is the Powerhouse of the earnings and CIOs are proving the value of data with their pocketbooks and the power of the purse. We break down the following earnings chart.
3) We saw NVIDIA’s success in AI as a sign that CIO budgets are changing. Find out about the new trend of CIO-led budgets that are independent of the traditional IT budget, as well as Charles’ framework of separating the efficiency bucket from the innovation bucket from his first book, The Quantum Age of IT.
4) One of the hottest companies in enterprise software sees a big leadership change, as Frank Slootman steps down from Snowflake and Sridhar Ramaswamy from the Neeva acquisition takes over. We discuss why this is a good move to avoid stagnation and discuss how to deal with bets in innovation.
5) Continuing the trend of innovation management, we talk about what Apple’s exit of the electric car business means in terms of managing innovative moonshots and what CIO’s often miss in terms of setting metrics around leadership and innovation culture.
6) And finally, we talk about the much-covered Google Gemini AI mistakes. We think the errors themselves fall within the range of issues that we’ve seen from other large language models, but we caution why the phrase “Eliminate Bias” should be a massive red flag for AI projects.
The past week has been “Must See TV” in the tech world as AI darling OpenAI provided a season of Reality TV to rival anything created by Survivor, Big Brother, or the Kardashians. Although I often joke that my professional career has been defined by the well-known documentaries of “The West Wing,” “Pitch Perfect,” and “Sillcon Valley,” I’ve never been a big fan of the reality TV genre as the twist and turns felt too contrived and over the top… until now.
Starting on Friday, November 17th, when The Real Housewives of OpenAI started its massive internal feud, every organization working on an AI project has been watching to see what would become of the overnight sensation that turned AI into a household concept with the massively viral ChatGPT and related models and tools.
So, what the hell happened? And, more importantly, what does it mean for the organizations and enterprises seeking to enter the Era of AI and the combination of generative, conversational, language-driven, and graphic capabilities that are supported with the multi-billion parameter models that have opened up a wide variety of business processes to natural language driven interrogation, prioritization, and contextualization?
The Most Consequential Shake Up In Technology Since Steve Jobs Left Apple
The crux of the problem: OpenAI, the company we all know as the creator of ChatGPT and the technology provider for Microsoft’s Copilots, was fully controlled by another entity, OpenAI, the nonprofit. This nonprofit was driven by a mission of creating general artificial intelligence for all of humanity. The charter starts with“OpenAI’s mission is to ensure that artificial general intelligence (AGI) – by which we mean highly autonomous systems that outperform humans at most economically valuable work – benefits all of humanity. We will attempt to directly build safe and beneficial AGI, but will also consider our mission fulfilled if our work aids others to achieve this outcome.”
There is nothing in there about making money. Or building a multi-billion dollar company. Or providing resources to Big Tech. Or providing stakeholders with profit other than highly functional technology systems. In fact, further in the charter, it even states that if a competitor shows up with a project that is doing better at AGI, OpenAI commits to “stop competing with and start assisting this project.”
So, that was the primary focus of OpenAI. If anything, OpenAI was built to prevent large technology companies from being the primary force and owner of AI. In that context, four of the six board members of OpenAI decided that open AI‘s efforts to commercialize technology were in conflict with this mission, especially with the speed of going to market, and the shortcuts being made from a governance and research perspective.
As a result, they ended up firing both the CEO, Sam, Altman and removed President COO Greg Brockman, who had been responsible for architecting that resources and infrastructure associated with OpenAI, from the board. That action begat this rapid mess and chaos for this 700+ employee organization which was allegedly about to see an 80 billion dollar valuation
A Convoluted Timeline For The Real Housewives Of Silicon Valley
Friday: OpenAI’s board fires its CEO and kicks its president Greg Brockman off the board. CTO Mira Murati, who was called the night before, was appointed temporary CEO. Brockman steps down later that day.
Saturday: Employees are up in arms and several key employees leave the company, leading to immediate action by Microsoft going all the way up to CEO Satya Nadella to basically ask “what is going on? And what are you doing with our $10 billion commitment, you clowns?!” (Nadella probably did not use the word clowns, as he’s very respectful.)
Sunday: Altman comes in the office to negotiate with Microsoft and OpenAI’s investors. Meanwhile, OpenAI announces a new CEO, Emmett Shear, who was previously the CEO of video game streaming company Twitch. Immediately, everyone questions what he’ll actually be managing as employees threaten to quit, refuse to show up to an all-hands meeting, and show Altman overwhelming support on social media. A tumultuous Sunday ends with an announcement by Microsoft that Altman and Brockman will lead Microsoft’s AI group.
Monday: A letter shows up asking the current board to resign with over 700 employees threatening to quit and move to the Microsoft subsidiary run by Altman and Brockman. Co-signers include board member and OpenAI Ilya Sutskever, who was one of the four board votes to oust Altman in the first place.
Tuesday: The new CEO of OpenAI, Emmett Shear, states that he will quit if the OpenAI board can’t provide evidence of why they fired Sam Altman. Late that night, Sam Altman officially comes back to OpenAI as CEO with a new board consisting initially of Bret Taylor, former co-CEO of Salesforce, Larry Summers (former Secretary of the Treasury), and Adam d’Angelo, one of the former board members who voted to figure Sam Altman. Helen Toner of Georgetown and Tasha McCauley, both seen as ethical altruists who were firmly aligned with OpenAI’s original mission, both step down from the board.
Wednesday: Well, that’s today as I’m writing this out. Right now, there are still a lot of questions about the board, the current purpose of OpenAI, and the winners and losers.
Keep In Mind As We Consider This Wild And Crazy Ride
OpenAI was not designed to make money. Firing Altman may have been defensible from OpenAI’s charter perspective to build safe General AI for everyone and to avoid large tech oligopolies. But if that’s the case, OpenAI should not have taken Microsoft’s money. OpenAI wanted to have its cake and eat it as well with a board unused to managing donations and budgets at that scale.
Was firing Altman even the right move? One could argue that productization puts AI into more hands and helps prepare society for an AGI world. To manage and work with superintelligences, one must first integrate AI into one’s life and the work Altman was doing was putting AI into more people’s hands in preparation for the next stage of global access and interaction with superintelligence.
At the same time, the vast majority of current OpenAI employees are on the for-profit side and signed up, at least in part, because of the promise of a stock-based payout. I’m not saying that OpenAI employees don’t also care about ethical AI usage, but even the secondary market for OpenAI at a multi-billion dollar valuation would help pay for a lot of mortgages and college bills. But tanking the vast majority of employee financial expectations is always going to be a hard sell, especially if they have been sold on a profitable financial outcome.
OpenAI is expensive to run: probably well over 2 billion dollars per year, including the massive cloud bill. Any attempt to slow down AI development or reduce access to current AI tools needs to be tempered by the financial realities of covering costs. It is amazing to think that OpenAI’s board was so naïve that they could just get rid of the guy who was, in essence, their top fundraiser or revenue officer without worrying about how to cover that gap.
Primary research versus go-to-market activities are very different. Normally there is a church-and-state type of wall between these two areas exactly because they are to some extent at odds with each other. The work needed to make new, better, safer, and fundamentally different technology is often conflicted with the activity used to sell existing technology. And this is a division that has been well established for decades in academia where patented or protected technologies are monetized by a separate for-profit organization.
The Effective Altruism movement: this is an important catchphrase in the world of AI, as it is not just defined as a dictionary definition. This is a catchphrase for a specific view of developing artificial general intelligence (superintelligences beyond human capacity) with the goal of supporting a population of 10^58 millennia from now. This is one extreme of the AI world, which is countered by a “doomer” mindset thinking that AI will be the end of humanity.
Practically, most of us are in between with the understanding that we have been using superhuman forces in business since the Industrial Revolution. We have been using Google, Facebook, data warehouses, data lakes, and various statistical and machine learning models for a couple of decades that vastly exceed human data and analytic capabilities.
And the big drama question for me: What is Adam d’Angelo still doing on the board as someone who actively caused this disaster to happen? There is no way to get around the fact that this entire mess was due to a board-driven coup and he was part of the coup. It would be surprising to see him stick around for more than a few months especially now that Bret Taylor is on board, who provides an overlap of experiences and capabilities that d’Angelo possesses, but at greater scale.
The 13 Big Lessons We All Learned about AI, The Universe, and Everything
First, OpenAI needs better governance in several areas: board, technology, and productization.
Once OpenAI started building technologies with commercial repercussions, the delineation between the non-profit work and the technology commercialization needed to become much clearer. This line should have been crystal clear before OpenAI took a $10 billion commitment from Microsoft and should have been advised by a board of directors that had any semblance of experience in managing conflicts of interest at this level of revenue and valuation. In particular, Adam d’Angelo as the CEO of a multi-billion dollar valued company and Helen Toner of Georgetown should have helped to draw these lines and make them extremely clear for Sam Altman prior to this moment.
Investors and key stakeholders should never be completely surprised by a board announcement. The board should only take actions that have previously been communicated to all major stakeholders. Risks need to be defined beforehand when they are predictable. This conflict was predictable and, by all accounts, had been brewing for months. If you’re going to fire a CEO, make sure your stakeholders support you and that you can defend your stance.
You come at the king, you best not miss.” As Omar said in the famed show “The Wire,” you cannot try to take out the head of an organization unless your followup plan is tight.
OpenAI’s copyright challenges feel similar to when Napster first became popular as a streaming platform for music. We had to collectively figure out how to avoid digital piracy while maintaining the convenience that Napster provided for supporting music and sharing other files. Although the productivity benefits make generative AI worth experimenting with, always make sure that you have a back up process or capability for anything supported with generative AI.
OpenAI and other generative AI firms have also run into challenges regarding the potential copyright issues associated with their models. Although a number of companies are indemnifying clients from damages associated with any outputs associated with their models, companies will likely still have to stop using any models or outputs that end up being associated with copyrighted material.
From Amalgam Insights’ perspective, the challenge with some foundational models is that training data is used to build the parameters or modifiers associated with a model. This means that the copyrighted material is being used to help shape a product or service that is being offered on a commercial basis. Although there is no legal precedent either for or against this interpretation, the initial appearance of this language fits with the common sense definitions of enforcing copyright on a commercial basis. This is why the data collating approach that IBM has taken to generative AI is an important differentiator that may end up being meaningful.
Don’t take money if you’re not willing to accept the consequences. This is a common non-profit mistake to accept funding and simply hope it won’t affect the research. But the moment research is primarily dependent on one single funder, there will always be compromises. Make sure those compromises are expressly delineated in advance and if the research is worth doing under those circumstances.
Licensing nonprofit technologies and resources should not paralyze the core non-profit mission. Universities do this all the time! Somebody at OpenAI, both in the board and at the operational level, should be a genius at managing tech transfer and commercial utilization to help avoid conflicts between the two institutions. There is no reason that the OpenAI nonprofit should be hamstrung by the commercialization of its technology because there should be a structure in place to prevent or minimize conflicts of interest other than firing the CEO.
Second, there are also some important business lessons here.
Startups are inherently unstable. Although OpenAI is an extreme example, there are many other more prosaic examples of owners or boards who are unpredictable, uncontrollable, volatile, vindictive, or otherwise unmanageable in ways that force businesses to close up shop or to struggle operationally. This is part of the reason that half of new businesses fail within five years.
Loyalty matters, even in the world of tech. It is remarkable that Sam Altman was backed by over 90% of his team on a letter saying that they would follow him to Microsoft. This includes employees who were on visas and were not independently rich, but still believed in Sam Altman more than the organization that actually signed their paychecks. Although it never hurts to also have Microsoft’s Kevin Scott and Satya Nadella in your corner and to be able to match compensation packages, this also speaks to the executive responsibility to build trust by creating a better scenario for your employees than others can provide. In this Game of Thrones, Sam Altman took down every contender to the throne in a matter of hours.
Microsoft has most likely pulled off a transaction that ends up being all but an acquisition of OpenAI. It looks like Microsoft will end up with the vast majority of OpenAI’s‘s talent as well as an unlimited license to all technology developed by OpenAI. Considering that OpenAI was about to support a stock offering with an $80 billion market cap, that’s quite the bargain for Microsoft. In particular, Bret Taylor’s ascension to the board is telling as his work at Twitter was in the best interests of the shareholders of Twitter in accepting and forcing an acquisition that was well in excess of the publicly-held value of the company. Similarly, Larry Summers, as the former president of Harvard University, is experienced in balancing non-profit concerns with the extremely lucrative business of Harvard’s endowment and intellectual property. As this board is expanded to as many as nine members, expect more of a focus on OpenAI as a for-profit entity.
With Microsoft bringing OpenAI closer to the fold, other big tech companies that have made recent investments in generative AI now have to bring those partners closer to the core business. Salesforce, NVIDIA, Alphabet, Amazon, Databricks, SAP, and ServiceNow have all made big investments in generative AI and need to lock down their access to generative AI models, processors, and relevant data. Everyone is betting on their AI strategy to be a growth engine over the next five years and none can afford a significant misstep.
Satya Nadella’s handling of the situation shows why he is one of the greatest CEOs in business history. This weekend could have easily been an immense failure and a stock price toppling event for Microsoft. But in a clutch situation, Satya Nadella personally came in with his executive team to negotiate a landing for openAI, and to provide a scenario that would be palatable both to the market and for clients. The greatest CEOs have both the strategic skills to prepare for the future and the tactical skills to deal with immediate crisis. Nadella passes with flying colors on all accounts and proves once again that behind the velvet glove of Nadella’s humility and political savvy is an iron fist of geopolitical and financial power that is deftly wielded.
Carefully analyze AI firms that may have similar charters for supporting safe AI, and potentially slowing down or stopping product development for the sake of a higher purpose. OpenAI ran into challenges in trying to interpret its charter, but the charter’s language is pretty straightforward for anyone who did their due diligence and took the language seriously. Assume that people mean what they say. Also, consider that there are other AI firms that have similar philosophies to OpenAI, such as Anthropic, which spun off of OpenAI for reasons similar to the OpenAI board reasoning of firing Sam Altman. Although it is unlikely that Anthropic (or large firms with safety-first philosophies like Alphabet and Meta’s AI teams) will fall apart similarly, the charters and missions of each organization should be taken into account in considering their potential productization of AI technologies.
AI is still an emerging technology. Diversify, diversify, diversify. It is important to diversify your portfolio and make sure that you were able to duplicate experiments on multiple foundation models when possible. The marginal cost of supporting duplicate projects pales in comparison to the need to support continuity and gain greater understanding of the breath of AI output possibilities. With the variety of large language models, software vendor products, and machine learning platforms on the market, this is a good time to experiment with multiple vendors while designing process automation and language analysis use cases.
Over the past year, Generative AI has taken the world by storm as a variety of large language models (LLMs) appeared to solve a wide variety of challenges based on basic language prompts and questions.
A partial list of market-leading LLMs currently available include:
The biggest question regarding all of these models is simple: how to get the most value out of them. And most users fail because they are unused to the most basic concept of a large language model: they are designed to be linguistic copycats.
As Andrej Karpathy of OpenAI stated earlier this year,
And we all laughed at the concept for being clever as we started using tools like ChatGPT, but most of us did not take this seriously. If English really is being used as a programming language, what does this mean for the prompts that we use to request content and formatting?
I think we haven’t fully thought out what it means for English to be a programming language either in terms of how to “prompt” or ask the model how to do things correctly or how to think about the assumptions that an LLM has as a massive block of text that is otherwise disconnected from the real world and lacks the sensory input or broad-based access to new data that can allow it to “know” current language trends.
Here are 8 core language-based concepts to keep in mind when using LLMs or considering the use of LLMs to support business processes, automation, and relevant insights.
1) Language and linguistics tools are the relationships that define the quality of output: grammar, semantics, semiotics, taxonomies, and rhetorical flourishes. There is a big difference between asking for “write 200 words on Shakespeare” vs. “elucidate 200 words on the value of Shakespeare as a playwright, as a poet, and as a philosopher based on the perspective on Edmund Malone and the English traditions associated with blank verse and iambic pentameter as a preamble to introducing the Shakespeare Theatre Association.”
I have been a critic of the quality that LLMs provide from an output perspective, most recently in my perspective “Instant Mediocrity: A Business Guide to ChatGPT in the Enterprise.” https://amalgaminsights.com/2023/06/06/instant-mediocrity-a-business-guide-to-chatgpt-in-the-enterprise/. But I readily acknowledge that the outputs one can get from LLMs will improve. Expert context will provide better results than prompts that lack subject matter knowledge
2) Linguistic copycats are limited by the rules of language that are defined within their model. Asking linguistic copycats to provide language formats or usage that are not commonly used online or in formal writing will be a challenge. Poetic structures or textual formats referenced must reside within the knowledge of the texts that the model has seen. However, since Wikipedia is a source for most of these LLMs, a contextual foundation exists to reference many frequently used frameworks.
3) Linguistic copycats are limited by the frequency of vocabulary usage that they are trained on. It is challenging to get an LLM to use expert-level vocabulary or jargon to answer prompts because the LLM will typically settle for the most commonly used language associated with a topic rather than elevated or specific terms.
This propensity to choose the most common language associated with a topic makes it difficult for LLM-based content to sound unique or have specific rhetorical flourishes without significant work from the prompt writer.
4) Take a deep breath and work on this. Linguistic copycats respond to the scope, tone, and role mentioned in a prompt. A recent study found that, across a variety of LLM’s, the prompt that provided the best answer for solving a math problem and providing instructions was not a straightforward request such as “Let’s think step by step,” but “Take a deep breath and work on this problem step-by-step.”
Using a language-based perspective, this makes sense. The explanations of mathematical problems that include some language about relaxing or not stressing would likely be designed to be more thorough and make sure the reader was not being left behind at any step. The language used in a prompt should represent the type of response that the user is seeking.
5) Linguistic copycats only respond to the prompt and the associated prompt engineering, custom instructions, and retrieval data that they can access. It is easy to get carried away with the rapid creation of text that LLM’s provide and mistake this for something resembling consciousness, but the response being created is a combination of grammatical logic and the computational ability to take billions of parameters into account across possibly a million or more different documents. This ability to access relationships across 500 or more gigabytes of information is where LLMs do truly have an advantage over human beings.
6) Linguistic robots can only respond based on their underlying attention mechanisms that define their autocompletion and content creation responses. In other words, linguistic robots make judgment calls on which words are more important to focus on in a sentence or question and use that as the base of the reply.
For instance, in the sentence “The cat, who happens to be blue, sits in my shoe,” linguistic robots will focus on the subject “cat” as the most important part of this sentence. The cat “happens to be,” implies that this isn’t the most important trait. The cat is blue. The cat sits. The cat is in my shoe. The words include an internal rhyme and are fairly nonsensical. And then the next stage of this process is to autocomplete a response based on the context provided in the prompt.
7) Linguistic robots are limited by a token limit for inputs and outputs. Typically, a token is about four characters while the average English content word is about 6.5 characters (https://core.ac.uk/download/pdf/82753461.pdf). So, when an LLM talks about supporting 2048 tokens, that can be seen as about 1260 words, or about four pages of text, for concepts that require a lot of content. In general, think of a page of content as being about 500 tokens and a minute of discussion typically being around 200 tokens when one is trying to judge how much content is either being created or entered into an LLM.
8) Every language is dynamic and evolves over time. LLMs that provide good results today may provide significantly better or worse results tomorrow simply because language usage has changed or because there are significant changes in the sentiment of a word. For instance, the English language word “trump” in 2015 has a variety of political relationships and emotional associations that are now standard to language usage in 2023. Be aware of these changes across languages and time periods in making requests, as seemingly innocuous and commonly used words can quickly gain new meanings that may not be obvious, especially to non-native speakers.
Conclusion
The most important takeaway of the now-famous Karpathy quote is to take it seriously not only in terms of using English as a programming language to access structures and conceptual frameworks, but also to understand that there are many varied nuances built into the usage of the English language. LLM’s often incorporate these nuances even if those nuances haven’t been directly built into models, simply based on the repetition of linguistic, rhetorical, and symbolic language usage associated with specific topics.
From a practical perspective, this means that the more context and expertise provided in asking an LLM for information and expected outputs, the better the answer that will typically be provided. As one writes prompts for LLMs and seek the best possible response, Amalgam Insights recommends providing the following details in any prompt:
Tone, role, and format: This should include a sentence that shows, by example, the type of tone you want. It should explain who you are or who you are writing for. And it should provide a form or structure for the output (essay, poem, set of instructions, etc…). For example, “OK, let’s go slow and figure this out. I’m a data analyst with a lot of experience in SQL, but very little understanding of Python. Walk me through this so that I can explain this to a third grader.”
Topic, output, and length: Most prompts start with the topic or only include the topic. But it is important to also include perspective on the size of the output. Example, “I would like a step by step description of how to extract specific sections from a text file into a separate file. Each instruction should be relatively short and comprehensible to someone without formal coding experience.”
Frameworks and concepts to incorporate: This can include any commonly known process or structure that is documented, such as an Eisenhower Diagram, Porter’s Five Forces, or the Overton Window. As a simpe example, one could ask, “In describing each step, compare each step to the creation of a pizza, wherever possible.”
Combining these three sections together into a prompt should provide a response that is encouraging, relatively easy to understand, and compares the code to creating a pizza.
In adapting business processes based on LLMs to make information more readily available for employees and other stakeholders, be aware of these biases, foibles, and characteristics associated with prompts as your company explores this novel user interface and user experience.
Note: This piece was accurate as of the time it was written, but on August 11th, Zoom edited its Service Agreement to remove the most egregious claims around content ownership. Its current language is more focused on the limited license needed to deliver content and establishes that user content is owned by the user. Amalgam Insights considers the changes made as of August 11th to be more in-line both with industry standards and with enterprise compliance concerns.
On August 7, 2023, Zoom announced a change to its terms and conditions in response to language discovered in Zoom’s service agreement that gave Zoom nearly unlimited capability to collect data and an unlimited license to use this information going forward for any commercial use. In doing so, Zoom has brought up a variety of intellectual property and AI issues that are important for every software vendor, IT department, and software sourcing group to consider over the next 12-18 months.
Analyzing Zoom’s Service Agreement Language
This discovery seems to have been a few months in the making as these changes seem to have initially been made back in March 2023 as it was launching some AI capabilities. Looking at each section, we can see that 10.2 and 10.3 focus on the usage of data.
Although this data usage may seem aggressive at first, one has to understand that Zoom‘s primary function is video conferencing, which requires moving both video and audio data across multiple servers to get from one point to another. This requires Zoom to have broad permission to transfer all data involved in a standard video, conference, or webinar, which includes all the data being used and all of the service data created. So, in this case, Amalgam Insights believes this access to data is not such a big deal as Zoom probably needed to update this language simply to support even basic augments, such as cleaning up audio or improving visual quality with any sort of artificial or machine learning capabilities.
However, in Amalgam, insights perspective, 10.4 is of much more aggressive set of terms. This change provides Zoom with a broad-ranging commercial license to any data used on Zoom‘s platform. This means that your face, your voice, and any trade, secrets, patents, or trademarks used on Zoom now become commercially usable by Zoom. Whether this was the intention or not, this section both sounds aggressive and crosses the line on the treatment that companies expect for their own data.
This is an extremely aggressive stance by most intellectual property standards. And it stands out as conflicting in comparison, to how data is positioned by Microsoft and Salesforce, enterprise application platform companies that aren’t exactly considered innocent or naïve in terms of running a business.
What went wrong here? Zoom is traditionally known as a company that is for the most part end user-centric. Zoom’s mission includes the goal, to “improve the quality and effectiveness of communications. We deliver happiness.” And Eric Yuan’s early stories about wanting to speak with loved ones remotely and refusing to do on-site meetings in promoting the power of remote meetings are part of the Zoom legend.
However, Zoom is also facing the challenge of meeting institutional shareholder demands to increase stock value. When Zoom’s stock rose in the pandemic, it reached such amazing heights that it led to extreme pressure for Zoom to figure out how to 5X or 10X their company revenue quickly. Knowing that the stock was in a bit of a bubble, Zoom initially tried to purchase Five9, a top-notch cloud contact center solution, but ran into problems during the acquisition process as the stock prices of each company ended up being too volatile to come to an agreement on both the value and price of the stock involved.
And I speculate that at this point Zoom is focused on bringing its stock back up to pandemic heights, a bubble that may honestly never be reached again. For Zoom, 2020 was a dot-com-like event, where its valuation wildly exceeded its revenue. And as other video conferencing, and event software solutions ended up quickly improving their products, Zoom’s core conferencing capabilities started to be seen as a somewhat commoditized capability.
Following the mission of the company would have meant looking more deeply at communications-based processes, collaboration, transcription, and perhaps even emoji and social media enhancement: all of the ways that we communicate with each other. But, the problem is that there is really only one play right now that can quickly leads to a doubling or tripling of stock price and that is AI. There’s no doubt that the amount of video and audio that Zoom processes on a daily basis can train a massive language model, as well as other machine learning models focused on re-creating and enhancing video and audio.
Positioned in a way where it was understood that Zoom would enhance current communicative capabilities, it could’ve been a very positive announcement for Zoom to talk about new AI capabilities. Zoom has taken initial steps to integrate AI into Zoom with Meeting Summary and Team Chat Compose products. But given the limited capabilities of these products, the licensing language used in the service agreement seems excessive.
The language used in section 10 of Zoom’s service agreement is very clear about maintaining the right to license and commercialized all aspects of any data collected by Zoom. And that statement has not been modified. Whether this is because of an overactive lawyer or Zoom’s future ambitions, or promises made to a board or institutional investors is beyond my pay grade and visibility. But I do know that that phrase is obviously not user-friendly, and Zoom is not providing visibility to those changes at the administrative level. The language and buttons used to support zooms, a model and commercialization efforts are very different on the administrative page compared to the language used in the service agreement.
Image from Zoom’s August 7th blog post
Understanding that legal language can take time to change, it makes sense to wait a few days to see if Zoom reverts to prior language or further modifies section 10 to represent a more user-friendly and client-friendly promise. And I think this language reflects a couple of issues that go far beyond Zoom.
First, service agreements for software companies in general, are often treated as an exercise in providing companies with maximum flexibility, while taking away basic rights from end users. This is not just a product management issue; this is an industry issue where this language and behavior is considered status quo both in the technology industry and in the legal profession. When companies like Alphabet and Meta, previously Facebook, were able to get away with the level of data collection associated with supporting each free user without facing governance or compliance consequences in most of the world, that set a standard for tech companies’ corporate counsel. Honestly, the language used in Zoom‘s current service agreement as of August 7, 2023 is not out of scope for many companies in the consumer world that provide social technologies.
The second issue is the overwhelming pressure that exists to be first or early to market in AI. The remarkable success of ChatGPT and other open AI-related models has shown that there is demand for AI that is either interesting or useful and can be easily used and accessed by the typical user or customer. This demand is especially high for any company that has a significant amount of text, data, audio, or video. The recent March 2023 announcement of Bloomberg GPT is only the starting point of what will be a wide variety of custom language, models and machine learning models that come to market over the next 12 to 18 months. Zoom obviously wants to be part of that discussion, and there are other companies, such as Microsoft, Adobe and Alphabet as well as noted start-ups like OpenAI that have done amazing AI work with audio and video already. Part of the reason that this stands out is that Zoom is one of the first companies to change its policies and aggressively seek a permanent commercial license associated with all user content and forcing and opt-out process that lacks auditability or documentation regarding how users can trust that their data is no longer being used to train models or support any other commercial activities Zoom may wish to pursue. But Amalgam Insights is absolutely sure that Zoom will not be the last company to do this by any means. This language and the response should also serve as both a warning and a lesson to all other companies, seeking to significantly change their service agreements to support AI projects.
What is next for Zoom?
From Amalgam Insights’ perspective, there are three potential directions that Zoom can pursue going forward.
One, do nothing or make minimal changes to the current policy. Consumer and social media-based technology policies have set a precedent for the level of data and licensing access in Zoom’s service agreement, but this level of customer data usage is considered extreme in most business software agreements. Will Zoom end up being a test case for pushing the boundaries for business data use? This seems unlikely given that Zoom has not traditionally been considered an aggressive company in pushing customer norms. Zoom does try to move fast and scale fast, but Zoom’s mistakes have typically been more due to incomplete processes rather than acts of commission and intentionally trying to push boundaries.
Two, rewrite parts of Section 10 that are intrusive from a licensing and commercial usage perspective. Amalgam Insights hopes that this is an opportunity for Zoom to lead from an end user licensing or service agreement perspective in making agreements more transparent and in using more exact legal language that feels cooperative instead of coercive. The legal approach of including all possible scenarios may be considered professionally competent, but the business optics are antagonistic.
Three, come out with an explicit enterprise version of technology that is not managed under these current rules set in section 10 so that data is not explicitly used for models and cannot easily be turned on through a simple toggle switch in the administration console. As my friend and data management analyst extraordinaire Tony Baer stated on LinkedIn (where you should be following him) “The solution for Zoom is to be more explicit: an enterprise version where data, no matter how anonymized, is not shared for Generative AI or any other Zoom commercial purpose whatsoever, and maybe a more general and/or freemium edition (which is how many consumers have already been roped in) where Zoom can do its Gen AI thing.”
Recommendations
The first recommendation is actually aimed towards the CIO office, procurement office, and other software purchasers. Be aware that your software provider is going to pursue AI and will likely need to change terms and conditions associated with your account to do so. This is a challenge, as multinational enterprises now face the possibility of approaching or exceeding 1,000 apps and data sources under management and even businesses of 250 employees or less average one app per employee. There is a massive race towards aggregating data, building custom AI models, and commercializing the outputs as benchmarks, workflows, automation, and guidance. But Zoom is not a one-off situation and your organization isn’t going to escape the issues brought up in Zoom’s service agreement language just by moving to another provider. This is an endemic and market-wide challenge, far beyond what Zoom is experiencing.
The second recommendation: One solution to this problem may be for vendors to split their product into public consumer-facing products and private products from a EULA and terms and conditions perspective. This wouldn’t be the worst approach, and would maintain the consumer expectation of free services that are subsidized by data and access while giving businesses, the confidence that they are working with a solution that will protect their intellectual property from being accessed or recreated by a machine learning model. This also potentially allows for more transparency in legal language as this product split is considered. Tech lawyer Cathy Gellis, stated “There can be the lawyerly temptation to phrase them (terms of service) as broadly as possible to give you the most flexibility as you continue to develop your service. But the problem with trying to proactively obtain as many permissions as you can is that users may start to think you will actually use them and react to that possibility.” In 2023, software vendors should assume that corporate clients will be wary of any language that puts trade secrets, patents, trademarks, or personally identifiable information at risk. Any changes to terms of service or service agreements should be reviewed both from a buy-side and sell-side perspective. This may include bringing in procurement or specialized software purchasing teams to reflect the customer’s perspective.
The third recommendation goes back to the ethical AI work that Amalgam Insights did several years ago. AI must be conducted in context of the same culture and goals that are considered pervasive within the company. Any AI policy that goes significantly outside the culture, norms, and expectations of the company will stand out. And this can be a challenge, because AI has been treated as an experiment in many cases, rather than as a formalized, technical capability. As AI development and policy is shaped, this is a time when new products, governance, and documentation need to be tightly aligned to core business and mission principles. AI is a test of every company’s culture and purpose and this is a time when the corporate ability to execute on lofty qualitative ideals will be actively challenged.
Zoom’s misstep in aggressively pursuing rights and access to client data should not just be seen as a specific organizational misstep, but as part of a set of trends that are important for enterprise, IT, purchasing, and legal departments as well as all software and data source vendors seeking to pursue AI and further monetize deep digital assets. The next 12 to 18 months are going to be a wild time in the technology market as every software vendor pursues some sort of AI strategy, and there will be mountains of new legal language, technical capabilities, and compliance aspects to review.
In June of 2023, we are firmly in the midst of the highest of hype levels for Generative AI as ChatGPT has taken over the combined hype of the Metaverse, cryptocurrency, and cloud computing. We now face a deluge of experts who all claim to be “prompt engineering” experts and can provide guidance on the AI tools that will make your life easier to live. At the same time, we are running into a cohort of technologists who warn that AI is only one step away from achieving full sentience and taking over the world as an apocalyptic force.
In light of this extreme set of hype drivers, the rest of us do face some genuine business concerns associated with generative AI. But our issues are not in worshipping our new robot overloads or in the next generation of “digital transformation” focused on the AI-driven evolution of our businesses that lay off half the staff. Rather, we face more prosaic concerns regarding how to actually use Generative AI in a business environment and take advantage of the productivity gains that are possible with ChatGPT and other AI tools.
Anybody who has used ChatGPT in their areas of expertise has quickly learned that ChatGPT has a lot of holes in its “knowledge” of a subject that prevent it from providing complete answers, timely answers, or productive outputs that can truly replace expert advice. Although Generative AI provides rapid answers with a response rate, cadence, and confidence that mimics human speech, it often is missing either the context or the facts to provide the level of feedback that a colleague would. Rather, what we get is “instant mediocrity,” an answer that matches what a mediocre colleague would provide if given a half-day, full-day, or week to reply. If you’re a writer, you will quickly notice that the essays and poems that ChatGPT writes are often structurally accurate, but lack the insight and skill needed to write a university-level assignment.
And the truth is that instant mediocrity is often a useful level of skill. If one is trying to answer a question that has one of three or four answers, a technology that is mediocre at that skill will probably give you the right answer. If you want to provide a standard answer for structuring a project or setting up a spreadsheet to support a process, a mediocre response is good enough. If you want to remember all of the standard marketing tools used in a business, a mediocre answer is just fine. As long as you don’t need inspired answers, mediocrity can provide a lot of value.
A few things for you to consider as your organization starts using ChatGPT. Just like when the iPhone launched 16 years ago, you don’t really have a choice on whether your company is using ChatGPT or not. All you can do is figure out how to manage and govern the use. Our recommendations typically take one of three major categories: Strategy, Productivity, and Cost. Given the relatively low price of ChatGPT both as a consumer-grade tool and as an API where current pricing is typically a fraction of the cost of doing similar tasks without AI, the focus here will be on strategy and productivity
Strategy – Every software company now has a ChatGPT roadmap. And even mid-sized companies typically have hundreds of apps under management. So, now there will 200, 500, or however many potential ways for employees to use ChatGPT over the next 12 months. Figure out how GPT is being integrated into the software and whether GPT is being directly used to process data or indirectly to help query, index, or augment data.
Strategy – Identify the value of mediocrity. The average large enterprise getting mediocrity from a query-writing or indexing perspective is often a much higher standard than the mediocrity of text autocompletion. Seek mediocrity in tasks where the average online standard is already higher than the average skill within your organization.
Strategy – How will you keep proprietary data out of Gen AI? – Most famously, Samsung recently had a scare when it saw how AI tools were echoing and using proprietary information. How are companies both ensuring that they have not put new proprietary data into generative AI tools for potential public use and that their existing proprietary data was not used to train generative AI models? This governance will require greater visibility from AI providers to provide detail on the data sources that were used to build and train the models we are using today.
Strategy – On a related note, how will you keep commercially used intellectual property from being used by Gen AI? Most intellectual property covered by copyright or patent does not allow for commercial reuse without some form of license. Do companies need to figure out some way of licensing data that is used to train commercial models? Or can models verify that they have not used any copyrighted data? Even if users have relinquished copyright for the specific social networks and websites that they initially wrote for, this does not automatically give OpenAI and other AI providers the same license to use the same data for training. And can AIs own copyright? Once large content providers such as music publishers, book publishers, and entertainment studios realize the extent to which their intellectual property is at risk with AI and somebody starts making millions with AI-enabled content that strongly resembles any existing IP, massive lawsuits will ensue. If an original provider, be ready to defend IP. If using AI, be wary of actively commercializing or claiming ownership of AI-enabled work for anything other than parody or stock work that can be easily replaced.
Productivity – Is code enterprise-grade: secure, compliant, and free of private corporate metadata? One of the most interesting new use cases for generative AI is the ability to create working code without having prior knowledge of the programming language. Although generative AI currently cannot create entire applications without significant developer engagement, it can quickly provide specifically defined snippets, functions, and calls that may have been a challenge to explicitly search for or to find on a Stack Overflow or in git libraries. As this use case continues to proliferate, coders need to understand their auto-generated code well enough to check for security issues, performance challenges, appropriate metadata and documentation, and reusability based on corporate service and workload management policies. But this will increasingly allow developers to shift from directly coding every line to editing and proofing the quality of code. In doing so, we may see a renaissance of cleaner, more optimized, and more reusable code for internal applications as the standard for code now becomes “instantly mediocre.”
Productivity – Quality, not Quantity. There are hundreds of AI-enabled tools out there to provide chat, search-based outputs, generative text and graphics, and other AI capabilities. Measure twice and cut once in choosing the tools that you use to help you. It’s better to find the five tools that matter than the 150 tools that don’t maximize the mediocre output that you receive.
Productivity – Are employees trained on fact-checking and proofing their Gen AI outputs? Whether employees are creating text, getting sample code, or prompting new graphics and video, the outputs need to be verified against a fact-based source to ensure that the generative AI has not “hallucinated” or autocompleted details that are incorrect. Generative AI seeks to provide the next best word or the next best pixel that is most associated with the prompt that it has been given, but there are no guarantees that this autocompletion will be factual just because it is related to the prompt at hand. Although there is a lot of work being done to make general models more factual, this is an area where enterprises will likely have to build their own, more personalized models over time that are industry, language, and culturally specific. Ideally, ChatGPT and other Generative AI tools are a learning and teaching experience, not just a quick cheat.
Productivity – How will Generative AI help accelerate your process and workflow automation? Currently, automation tends to be a rules-driven set of processes that lead to the execution of a specific action. But generative AI can do a mediocre job of translating intention into a set of directions or a set of tasks that need to be completed. While generative AI may get the order of actions wrong or make other text-based errors that need to be fact-checked by a human, the AI can accelerate the initial discovery and staging of steps needed to complete business processes. Over time, this natural language generation-based approach to process mapping is going to become the standard starting point for process automation. Process automation engineers, workflow engineers, and process miners will all need to learn how prompts can be optimized to quickly define processes.
Cost – What will you need to do to build your own AI models? Although the majority of ChatGPT announcements focus on some level of integration between an existing platform or application and some form of GPT or other generative AI tool, there are exceptions. BloombergGPT provides a model based on all of the financial data that it has collected to help support financial research efforts. Both Stanford University Alpaca and Databricks Dolly have provided tools for building custom large language models. At some point, businesses are going to want to use their own proprietary documents, data, jargon, and processes to build their own custom AI assistants and models. When it comes time for businesses to build their own billion-parameter, billion-word models, will they be ready with the combination of metadata definitions, comprehensive data lake, role definitions, compute and storage resources, and data science operationalization capabilities to support these custom models? And how will companies justify the model creation cost compared to using existing models? Amalgam Insights has some ideas that we’ll share in a future blog. But for now, let’s just say that the real challenge here is not in defining better results, but in making the right data investments now that will empower the organization to move forward and take the steps that thought leaders like Bloomberg have already pursued in digitizing and algorithmically opening up their data with generative AI.
Although we somewhat jokingly call ChatGPT “instant mediocrity,” this phrase should be taken seriously both in acknowledging the cadence and response quality that is created. Mediocrity can actually be a high level of performance by some standards. Getting to a response that an average 1x’er employee can provide immediately is valuable as long as it is seen for what it is rather than unnecessarily glorified or exaggerated. Treat it as an intern or student-level output that requires professional review rather than an independent assistant and it will greatly improve your professional output. Treat it as an expert and you may end up needing legal assistance. (But maybe not from this lawyer. )
Businesses face the challenge of managing a variety of workforce challenges across the wide variety of people at the company: freelancers, outsourcing firms, consultants, contingent labor, and full-time labor. The United States Government Accountability Office estimates that about 40% of workers are not full-time workers and fall within a variety of roles including contractors, part-time workers, and on-call workers that may track time through different systems and methods. With this challenge in mind, companies need a comprehensive management view that automates processes and helps companies focus on conducting work more quickly rather than be mired in a sea of paperwork and processes. Workforce management is no longer simply a matter of managing active full-time employees, but supporting a comprehensive practice that consolidates workforce management across contingent, part-time, full-time, and other categories of workers.
To effectively manage their hybrid workforce effectively across financial, operational, and management capacities, companies must consolidate workforce management tasks onto a single platform and a consistent set of data to avoid constant switching back and forth across inconsistent data. This platform should include contingent labor, internal labor, time and payroll, workforce scheduling, financial budgeting, employee engagement, and onboarding capabilities, including governance, risk, and compliance management across all areas. Data across all of these areas should ideally be within a single data store that provides a shared version of the truth for all stakeholders in workforce management across HR, finance, and line-of-business management roles.
Amalgam Insights believes the following capabilities should be considered in developing a comprehensive workforce management system.
Manage payroll, performance, and relevant benefits for employees, consultants, and freelancers.
Workforce management efforts must consider the combination of standard payroll systems, time and attendance systems, scheduling systems, contingent labor management, on-demand services, third-party temporary labor and consulting firms, and self-employed contractors. In doing so, companies must decide which benefits and services are consistent across various labor types and what resources are needed to maximize the productivity of each class of workers. Regardless of labor type, compensation must be timely, accurate, and provided based on contractual agreements based on relevant labor law. By managing all classes of workers across a shared and consistent set of characteristics, companies may be better positioned to see if there are part-time or contingent workers who should be made full-time employees or to see which tasks are better supported by specific workers, skillsets, geographies, shifts, and other identifying work characteristics.
Supporting differing compliance requirements based on geography, status, and corporate asset access.
Workers with privileged access to trade secrets or classified information must all be treated with relevant compliance and confidentiality standards regardless of their work categorization. At the same time, companies must manage differing standards across wages, benefits, and tax obligations that exist in each jurisdiction where a worker is located.
Standardizing Key Performance Indicators (KPIs) and Management by Objectives (MBOs) across different work categories by focusing on the quality and quantity of relevant outputs and deliverables.
Even within a single department, the combination of roles, geographies, seniority, and employee status can lead to widely disparate individual goals. As companies identify appropriate KPIs and MBOs on an individual level that maximizes the value that each person brings to the workplace, they must also ensure that teams are aligned to shared corporate success metrics rather than disparate and disconnected metrics that may inadvertently pit workers against each other to pursue personal success.
Using a feedback-based set of processes to create a consistent employee experience and corporate culture that provides all workers with a shared set of expectations, goals, worker preferences, and employee support.
Employee feedback is only as useful as the corpus of data created and the management response associated with the suggestions and criticisms provided. At the same time, feedback can also be part of a continuous learning and continuous improvement initiative if feedback is stored as analytic and decision-guiding data that is tracked and monitored over time. Feedback can also be analyzed to see if workers are engaged in processes that are designed to improve the worker or corporate experience.
Understand the top-line and bottom-line financial contribution of contract and contingent work.
Although revenue per full-time employee is an outward-facing metric used by public companies to show efficiency, the business reality is that contractors and part-time employees also represent investments that should be reflected in workforce costs in determining corporate productivity and profitability. If companies are effectively replacing skills with contingent labor, this should be noted and tracked. Conversely, if there are significant gaps between full-time and other employees, companies should figure out the cause of these gaps and whether they can be closed through training, onboarding, or technical augmentation.
Taking Steps to Create a Consolidated Workforce Management Environment
Ultimately, companies have a responsibility to support the relevant stakeholders and shareholders associated with the company. However, this responsibility cannot be met if the company lacks consistent visibility to every worker who is attached to corporate work output, regardless of employment status, geography, department, or role. As companies seek to improve productivity and to allow executives to be more strategic in their approach to support productive workers while maintaining all relevant compliance responsibilities and a shared version of all relevant data, Amalgam Insights provides the following recommendations for human resources, finance, and managerial roles tasked with creating a better work environment.
First, ensure that you have the data necessary to maintain consistency of work expectations. Workers should be able to expect some baseline of employee experience even as they differ in location, employment status, and compensation if for no other reason than to provide every worker with a standard set of expectations and professional responsibilities.
Second, measure the profitability and revenue across the entire workforce based on a holistic view of hours, skills, geographies, and business goals. This capability can potentially uncover if specific hiring or labor sourcing strategies may be more profitable, or at least aligned to higher revenue, rather than simply treating all hiring and contracting exercises as an exercise in managing costs.
Finally, manage contingent labor with metrics and standards similar to traditional employee labor. When 40% of labor consists of either part-time, contractors, or on-demand workers, a workforce management solution that only looks at full-time payroll, onboarding, time, attendance, and benefits is no longer sufficient to understand the finance and operational details of the holistic workforce. Frontline and hourly workers seeking to manage their scheduling and time need a consistent and mobile experience on par with full-time workers. Regardless of how these metrics are presented from a public perspective, companies must have an internal basis for tracking the skills and work of every person who conducts work for a company, regardless of formal employment status.
By taking these steps, companies can fully empower all workers to acknowledge their contributions, manage skill portfolios, and further invest in the success of the complete workforce.
“The status quo is not a neutral state, but a mindset to uphold the decisions of the past.”
In 2023, effective business planning, budgeting, and forecasting is a necessary capability to keep organizations running. Already this year, we have seen unexpected banking failures, unpredictable labor markets, and continued supply chain and logistics challenges based on geopolitical challenges. In light of these challenges, Amalgam Insights believes that businesses must have a shared version of the truth that they use as they look at their budget and finances.
And, in this case, we specifically talk about a “shared” version of the truth rather than the “single version of the truth” typically associated with data warehouses and enterprise applications. This is because data changes quickly and every stakeholder can potentially make different decisions to define and augment their data, even basic changes such as language and currency translation that can lead to different versions of the truth. In this analytically enhanced and globally complicated world, it makes more sense to have a shared version of the truth that is augmented with personalized or localized data and assumptions. However, this consistently shared version of the truth can be hard to accomplish in organizations where planning is handled in a distributed and personalized manner through spreadsheets. In the enterprise world, finance professionals are inured to the basic realities of auditable data, processes, and results. And they are often asked to provide reports and memos that are used at the executive level or by external investors and public markets to ascertain the health of the market. Given the assumed importance of this formality, why would experienced professionals use spreadsheets in the first place?
Let’s face it; spreadsheets are easy to use. They are the lingua franca of data; a format that every experienced data user has been trained on. And with plug-ins and Visual Basic, spreadsheets can now handle relatively complex analytic use cases. Even if they aren’t quite data science tools, spreadsheets can provide structured analytic outputs. Also, spreadsheets are accessible on every computer through Excel, Google Sheets, or other common spreadsheet software. And with the emergence of cloud-based spreadsheets, it is now possible for two or more people to collaborate within a single spreadsheet.
Spreadsheets also provide users with the ability to customize their own analytic views with their own personalized views of data and the ability to hypothesize by building their own models. Who hasn’t looked at data and wondered “what if the data looked a bit differently?” or “what if we have a drastic scenario that suddenly increases or decreases a fundamental aspect of the business?” In light of COVID, rapid interest rate hikes, global shortages in commodities production, trained labor shortages, and the increasingly unstable banking environment we are in, it is important to be able to test potential extreme assumptions and support a wide variety of scenarios. Between the ease of use, availability, and personalization aspects of spreadsheets, it is not hard to figure out why spreadsheets are still a leading tool for financial planning and analysis. Even so, Amalgam Insights has found that once organizations pass Dunbar’s number (approximately 150 employees), they start to struggle with collaborative tasks simply because it becomes difficult for any one employee to know all of the other employees who need to be involved in the business planning process and spreadsheets have been designed to maximize individual productivity, rather than collaborative work, for decades. From a practical perspective, people tend to work with the people they know best. This is fine for a small company with a dedicated office where everyone knows each other. According to US Census data, the typical 1,000-person company has 19 locations, making it highly unlikely that all of the key budget stakeholders will be in one office. In this regard, Amalgam Insights finds the following challenges in supporting spreadsheet-based planning at scale.
The distributed nature of work also makes spreadsheet governance a challenge, as it is easy for spreadsheets to suffer from version control issues, a structured workflow process, and for file owners to lose control of the inputs and outputs that they are responsible for supporting. The lack of version control, workflow, and activity tracking is especially challenging in industries and geographies that require tracking of any personal data either related to employees or customers.
Spreadsheets also struggle in large data environments, which are quickly becoming commonplace in the business planning world. Although a core enterprise database may only be a few gigabytes, accurate planning now often includes access to sales, operations, and potentially even IT transactional data sources that can quickly expand beyond the memory and data size constraints that spreadsheets are designed to use. From Amalgam Insights’ perspective, the size and variety of data are the biggest technical constraints that spreadsheets face as planning solutions.
Spreadsheets lack advanced analytic and machine learning capabilities. Although algorithmic, statistical, and machine learning tools are increasingly becoming part of the FP&A world, especially in forecasting, Amalgam Insights finds in practice that most organizations have not yet embraced complex analytics as a core part of their FP&A approach. Based on current job site metrics, Amalgam Insights estimates that less than 2% of FP&A professionals currently have a machine learning or data science certification or degree, making this an early innovator capability that has still not crossed the chasm to become a standard job requirement for FP&A.
But perhaps the most significant challenge with spreadsheet models is that they are often fragile: created based on the logic of a single person rather than on defined business logic and with little to no documentation associated with the plans, forecasting algorithms, and multi-tabular complexity that inevitably occurs when a spreadsheet is the primary planning tool for a business, which can also lead to costly data accuracy issues. The model is only as adaptable as the spreadsheet creator’s knowledge of the industry and is dependent on that employee staying employed. Considering that it is unrealistic to expect an FP&A senior analyst to remain in that role for more than five years before either getting promoted or getting a better offer, this human risk is a significant challenge for business planning solutions.
As organizations grow in size to support more than a handful of locations and a set of workers that exceeds Dunbar’s number of 150 colleagues, Amalgam Insights believes that it becomes necessary to adopt a formalized planning solution that supports collaboration, scale, advanced analytics, continuous planning across many scenarios, and advanced forecasting analytics. Otherwise, it is difficult for businesses to maintain a consistent and shared version of the truth across financial planning and analysis personnel that can drive both departmental and executive planning efforts.
Ultimately, the use of spreadsheets as a formal system of record for business planning is a risky one for any organization with a formal corporate structure, governed industry or geography, or any organization that has a significantly distributed business. The ubiquity of the spreadsheet makes it an easy place to start modeling a budget, and the value of the spreadsheet in helping users to structure small datasets will exist for the foreseeable future. But the fragility of the data structure, lack of user and version control governance, inability to scale, and the difficulty of verifying data with other sources while avoiding human error all lead to the need of supporting a more formalized planning solution over time. As organizations face a future of keeping distributed groups focused on a shared version of the truth and collectively consider a variety of scenarios at any given time, the risk of spreadsheet fragility needs to be matched up against the value of using a formalized FP&A solution designed to analyze, govern, and protect all relevant business data, formulas, and outcomes.
Analytics in the Retail and Consumer Packaged Goods (CPG) markets is more complex than the average corporate data ecosystem because of the variety of analytic approaches needed to support these organizations. Every business has operational management capabilities for core human resources and financial management, but retail adds the complexities of hybrid workforce management, scheduling, and operational analytics as well as the front-end data associated with consumer marketing, e-commerce, and transactional behavior across every channel.
In contrast, when retail organizations look at middle-office and front-office analytics, they are trying to support a variety of timeframes ranging from intraday decisions associated with staffing and customer foot traffic to the year-long cycles that may be necessary to fulfill large wholesale orders for highly coveted goods in the consumer market. Over the past three years, operational consistency has become especially challenging to achieve as COVID, labor skill gaps, logistical bottlenecks, commodity shortages, and geopolitical battles have all made supply chain a massive dynamic risk factor that must be consistently monitored across both macro and microeconomic business aspects.
The lack of alignment and connection between the front office, middle-office, and administrative analytic outputs can potentially lead to three separate silos of activity in the retail world— connected only by some basic metrics, such as receipts and inventory turnover, that are interpreted in three different ways. Like the parable of the blind men and an elephant where each person feels one part of the elephant and imagines a different creature, the disparate parts of retail organizations must figure out how to come together, as the average net margin for general retail companies is about 2% and that margin only gets lower for groceries and for online stores.
Analytic opportunities to increase business value exist across the balance sheet and income statement. Even though consumer sentiment, foot traffic, and online behavior are still key drivers for retail success, analytic and data-driven guidance can provide value across infrastructure, risk, and real-time operations. Amalgam Insights suggests that each of these areas requires a core analytic focus that is different and reflects the nature of the data, the decisions being made, and the stakeholders involved.
Facing Core Retail Business Challenges
First, retail and CPG organizations face core infrastructure, logistics, and data management challenges that typically require building out historic analysis and quantitative visibility capabilities often associated with what is called descriptive or historical analytics. When looking at infrastructure factors such as real estate, warehousing, and order fulfillment issues, organizations must have access to past trends, costs, transactions, and the breadth of relevant variables that go into real estate costs or complex order fulfillment associated with tracking perfect order index.
This pool of data ideally combines public data, industry data, and operational business data that includes, but is not limited to, sales trends, receipts, purchase orders, employee data, loyalty information, customer information, coupon redemption, and other relevant transactional data. This set of data needs to be available as analytic and queryable data that is accessible to all relevant stakeholders to provide business value. In practice, this accessibility typically requires some infrastructure investment either by a company or a technology vendor willing to bear the cost of maintaining a governed and industry-compliant analytic data store. By doing so, retail organizations have the opportunity to improve personalization and promotional optimization.
A second challenge that retail analytics can help with is associated with the risk and compliance issues that retail and CPG organizations face, including organized theft, supplier risk, and balancing risk and reward tradeoffs. A 2022 National Retail Federation (NRF) survey showed that organized retail crime had increased over 26% year over year, driving the need to identify and counter organized theft efforts and tactics more quickly. Retailer risk for specific goods and brands also needs to be quantified to identify potential delays and challenges or to determine whether direct store delivery and other direct-to-market tactics may end up being a profitable approach for key SKUs. Risk also matters from a profitability analysis perspective as retail organizations seek to make tradeoffs between the low-margin nature of retail business and the consumer demand for availability, personalization, automation, brand expansion, and alternative channel delivery that may provide exponential benefits to profits. From a practical perspective, this risk analysis requires investment in a combination of predictive analytics and the ability to translate the variance and marginal cost associated with new investments with projected returns.
A third challenge for retail analytics is to support real-time operational decisions. This use case requires access to streaming and log data associated with massive volumes of rapid transactions, frequently updated time-series data, and contextualized scenarios based on multi-data-sourced outcomes. From a retail outcome perspective, the practical challenge is to make near-real-time decisions, such as same-day or in-shift decisions to support stocking, scheduling, product orders, pricing and discounting decisions, placement decisions, and promotion. In addition, these decisions must be made in the context of broader strategic and operational concerns, such as brand promise, environmental concerns, social issues, and regulatory governance and compliance associated with environmental, social, and governance (ESG) concerns.
As an example, supply chain shortages often come from unexpected sources. An unexpected geopolitical example occurred in the United States when the government’s use of containers as a temporary barrier to block illegal immigration checkpoints on the US-Mexico border led to shortages at US ports for delivery. This delay in accessing containers was not predictable based solely on standard retail metrics and behavior and demonstrates one example of how unexpected political issues can affect a hyperconnected logistical supply chain.
Recommendations for Upgrading Retail Analytics in the 2020s
To solve these analytic problems, retail and CPG organizations need to allow line-of-business, logistics, and sourcing managers to act quickly with self-service and on-demand insights based on all relevant data. This ultimately means that to take an analytic approach to retail, Amalgam Insights recommends the following three best practices in creating a more data-driven business environment.
Create and implement an integrated finance, operational, and workforce management environment. Finance, inventory, and labor must be managed together in an integrated business data store and business planning environment or the retail organization falls apart. Whether companies choose to do this by knitting together multiple applications with data management and integration tools or by choosing a single best-in-breed suite, retail businesses have too many moving parts to split up core operational data across a variety of functional silos and business roles that do not work together. In the 2020s, this is a massive operational disadvantage.
Adopt prescriptive analytics, decision intelligence, and machine learning capabilities above and beyond basic dashboards. When retail organizations look at analytics and data outputs, it is not enough to gain historical visibility. In today’s AI-enabled world, companies must have predictive analytics, statistical analysis, detect anomalies quickly, and have the ability to translate business data into machine learning and language models for the next generation of analytics and decision intelligence. Retail can be more proactive and prescriptive with AI and ML models trained to their enterprise data to support more personalized and contextualized purchasing experiences.
Implement real-time alerts with relevant and actionable retail information. Finally, timely and contextual alerts are also now part of the analytic process. As retail organizations have moved from seasonal purchases and monthly budgeting to daily or even hourly decisions, regional and branch managers need to be able to move quickly if there are signs of business danger coordinated revenue leakage, brand damage across any of the products held within the store, unexpected weather phenomena, labor issues, or other incipient macro or microeconomic threats.
