Category: Office of the CFO

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Zoom Faces Challenges in Navigating the Age of Generative AI

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.

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Zluri Raises a $20 Million Series B Round: Is it Enough for the Crowded SaaS Management Market?

Companies Mentioned:

Accel, Apptio, Atlassian Ventures, Bain Capital Ventures, Baird Capital, Bessemer Venture Partners, BetterCloud, Blissfully, Calero, Canaan Partners, Cleanshelf, Cloudability, Coupa Ventures, Craft Ventures, e.ventures, Endiya Partners, Entrée Capital, Flybridge Capital Partners, Founder Collective, F-Prime Capital, Global Founders Capital, Greycroft, High Alpha, Intello, IVP, Kalaari Capital, LeanIX, MassMutual Ventures, Menlo Ventures, New Amsterdam Growth Capital, Norwest Venture Partners, Okta Ventures, Productiv, SailPoint, Scopus Capital, Shine, SoftBank, Sound Ventures, Sozo Ventures, Spring Lake Equity Partners, Tangoe, Tiger Global, Tropic, Uncork Capital, Vendr, Vista Equity Partners, Warburg Pincus, Wing Venture Capital, Y Combinator, Zluri, Zylo

Key Stakeholders:
Chief Information Officers, Chief Technology Officers, Chief Financial Officers, Finance and Accounts Payable Directors and Managers, Procurement Directors, Technology Expense Directors and Managers, FinOps Directors and Managers, IT Architects, Vice President/Director/Manager of IT Operations, Product Managers, IT Sourcing Directors and Managers, IT Procurement Directors and Managers, SaaS Expense Managers, Sales Operations Managers, Marketing Operations Managers.

Why It Matters:
SaaS (Software as a Service) Operations is a hot market where vendors have collectively received over $1 billion in investments. End user organizations are seeking to manage $250 billion in annual spend associated with SaaS subscriptions, which can often be scattered over 1,000 apps in large multi-national enterprises. Even a relatively small 500-person organization can expect to have over 200 apps under management. This combination of vendor sprawl, shadow IT, and governance challenges are quickly forcing businesses to realize that they require SaaS governance across sourcing, spend, access, inventory, and security. With this $20 million Series B round, Zluri enters this fray in earnest in making its automation platform more accessible to the SaaS management market.

Top Takeaway:
Zluri is an Amalgam Insights recommended vendor for automating service orders, managing onboarding and offboarding, monitoring app usage, and managing SaaS spend. It fills multiple core responsibilities within the Amalgam Insights Technology Lifecycle Management relative to SaaS and should be considered by companies seeking to support SaaS environments with over $1 million in total annual spend or with over 100 separate app vendors under management.

Zluri Raises a $20 Million Series B Round

On July 13, 2023, Zluri, a SaaS operations platform, announced a $20 million Series B round headed by Lightspeed with additional participation from existing investors including MassMutual Ventures, Endiya Partners, and Kalaari Capital.

This funding occurs in context of a breadth of investment in managing the operations and procurement of SaaS including the following funding investments and product launches:

  • Feb 2023 – Zylo raises a $5 million round on top of a $31 million Series C round in December 2022.
    Investors include: Baird Capital, Bessemer Venture Partners, Coupa Ventures, High Alpha, Menlo Ventures, Spring Lake Equity Partners,
  • November 2022 – Tangoe announces addition of SaaS management to TangoeOne platform
  • June 2022 – BetterCloud, a SaaS management firm, sells a majority stake to Vista Equity Partners after raising $187 milion over six rounds.
    Previous Investors included: Warburg Pincus, Accel, Bain Capital Ventures, e.ventures, Flybridge Capital Partners, Greycroft, New Amsterdam Growth Capital
  • June 2022 – Vendr raises $150M Series B to support its SaaS buying platform
    Investors include Craft Venturs, SoftBank, Sozo Ventures, F-Prime Capital, Sound Ventures, Tiger Global, Y Combinator
  • April 2022 – Calero (technology expense management vendor managing over $25 billion in tech spend) announces a SaaS expense management solution
  • February 2022 – Vendr acquires Blissfully to add cost management and data offerings.
  • February 2022 – Tropic raises $40M Series B from Insight Partners to improve SaaS procurement, a round that occured four months after a Series A round from Canaan Partners, Founder Collective and Shine
  • February 2022 – Torii raises a $50M Series B round led by Tiger Global
    Investors include Tiger Global, Entree Capital, Global Founders Capital, Scopus Capital, Uncork Capital, and Wing Venture Capital
  • March 2021 – Enterprise Architcture Management company LeanIX acquires Cleanshelf
  • March 2021 – Productiv raises $45M Series C to support SaaS expense management
    Investors include IVP, Accel, Atlassian Ventures, Norwest Venture Partners, Okta Ventures
  • February 2021- SailPoint acquires Intello for $43 million
  • November 2020 – Apptio (IT financial management and Cloud FinOps provider) announced Cloudability SaaS for SaaS discovery and spend management

Suffice it to say that the SaaS management market is both a hot market and one that requires both funding and a high quality offering to be competitive. Top tier venture capital and private equity firms have made one or more investments in this space already. But at the same time, one of the concerns that Zluri does not have to worry about is that this market is an actual market. One of the biggest concerns an analyst typically has about a new market is whether it is real or not and backed by customers, revenue, and market competitors. The SaaS Management market has proven this to be true, both in the quantity and quality of offerings in place.

This said, does Zluri match up with the vendors at large and does it have a competitive niche in this complex market?

About Zluri

Amalgam Insights has spoken with Zluri executives multiple times in the past couple of years as we have explored SaaS management and SaaSOps as a part of our overall Technology Lifecycle Management umbrella. In doing so, we have found so far that key differentiating points include:
• Workflow automation to support app discovery and orders
• Activity-based insight into SaaS usage and spending
• Identity management to audit access and automate onboarding and offboarding

As one of the newer SaaS management solutions that Amalgam Insights covers, founded in 2020 in Bangalore, Zluri has a software solution that currently lacks legacy technical debt issues and is built with a current and modern user interface. Amalgam Insights finds it interesting that Zluri was founded in India, as India has traditionally been an area that has supported much of the help desk, service order, invoice processing, and optimization work associated with telecom expense and cloud FinOps work on behalf of US-owned companies. This company represents a shift in seeing Indian entrepreneurs directly owning the company while also being close to a significant center of the technology lifecycle management value chain. This location also means that Zluri has some cost structure advantages compared to most of its competitors started either in the United States or Israel. And its focus on automating SaaS-related processes and workflows provides a strong foundation towards providing not only the operational support to manage SaaS, but also the lineage and t that are needed to trace how and when specific changes were made to a SaaS account.

Zluri’s offering is compelling enough to win business even as it faces the competition listed above. Amalgam Insights estimates that Zluri currently has around 250 customers and over 200 employees, which is in line with the recent funding round that was announced. However, the capital raised in this Series B round is obviously necessary to gain market share in the 100 – 5,000 employee mid-market where Zluri has succeeded to this point. Even in today’s era of product-led growth, some level of market visibility is needed to support go-to-market solutions, especially in a market where Amalgam Insights has tracked total investment that approaches $1 billion.

Amalgam Insights believes that, though Zluri has a competitive and differentiated product that matches up well with current trends in automation and workflow management that will align well with the current megatrend of Generative AI, its biggest challenge is currently in market visibility where the other companies that Amalgam Insights has mentioned have all made inroads with enterprise buyers, channel partners, consultants, and industry associations relevant to the buying cycle of SaaS.

Recommendations to the IT Expense Community

First, in seeking a SaaS management solution, Amalgam Insights always recommends thinking about the full Technology Lifecycle that goes across sourcing, procurement, expense management, vendor management, resource optimization, compliance, and security. SaaS management and SaaS operations are currently fragmented markets where it is hard to find a single vendor that is strong in all of these areas.

The Amalgam Insights Model for Technology Lifecycle Management

Second, in managing this SaaS lifecycle, look for automation and for skill sets that may fall outside of your organization’s core management or sourcing skills. SaaS can be a complicated and complex spend category, especially as large multi-billion dollar enterprises can expect to manage 1,000 apps at this point across both formal and “Bring Your Own” expensed apps that may hide in a corporate credit card or a phone bill.

Third, expect to see Zluri show up more frequently in your due diligence of SaaS management solutions. Amalgam Insights currently recommends Zluri as a solution to manage SaaS costs, support service orders and onboarding through native workflow automation, and to support application discovery, especially in disaggregated environments. And in our research, we have found that Zluri is a solution that wins deals in the majority of competitive evaluations that Amalgam Insights has seen, which indicates alignment with current customer needs. With this funding round, Zluri now is prepared to compete for its fair share of opportunities in a market that is both deep in competitors and in demand from enterprises seeking to control over $200 billion in annual SaaS spend.

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IBM Plans to Acquire IT Financial Management Leader Apptio: Consequences for the Enterprise IT Market

On June 26, 2023, IBM announced its intention to acquire IT Financial Management vendor Apptio for 4.6 billion dollars. This acquisition is intended to support IBM’s ability to support IT automation and business value documentation. With this acquisition comes the big question: is this acquisition good for IBM and Apptio customers? Who benefits most from this acquisition?

As an industry analyst who has covered the IT expense management space and first coined the Technology Expense Management and Technology Lifecycle Management terms as evolutions of the IT Asset Management and Telecom Expense Management markets, I’ve been looking at these markets and vendors for the past 15 years. In that time, IBM has gone through a variety of investments in the Technology Lifecycle Management space to manage the assets, projects, and costs associated with IT environments and Apptio has evolved from a nascent startup to a market leader.

When Amalgam Insights is asked “What do you think of IBM’s acquisition of Apptio,” this opinion requires exploring the back story and starting points for consideration as there is much more to this acquisition than simply stating that this is “good” or “bad.” Apptio is a market-leading vendor across IT financial management, SaaS Management, Cloud Cost Management (where Apptio is a current Amalgam Insights Distinguished Vendor), and Project Management. But there is a multi-decade history leading up to this acquisition, including both IBM’s pursuit of Technology Lifecycle Management solutions and Apptio’s long road to becoming a market leader in IT financial management.

Contextualizing the Acquisition

To understand this acquisition in its full context, let’s explore a partial timeline of the IBM, IBM partner, and Apptio journeys to get to this point:

1996 – IBM purchases Tivoli Systems for $743 million (approximately $1.4 billion in 2023 dollars) to substantially enter the IT asset management and monitoring business. Tivoli goes to become a market standard for IT asset management.

2002 – IBM acquires Rational Software for $2.1 billion to support software development and monitoring.

2007 – Apptio is founded as an IT financial management solution to support the planning, budgeting, and forecasting needs of CIOs and CFOs seeking to better understand their holistic IT ecosystem. At the time, it is seen as a niche capability compared to Tivoli’s broad set of functionalities but is still seen as promising enough to attract Andreessen Horowitz’ attention as their first investment back in 2009.

February 2012 – IBM acquires Emptoris, which includes a leading telecom expense management called Rivermine, to support sourcing, inventory management, and supply chain management as part of its Smarter Commerce initiative.

May 2015 – IBM Divests Rivermine operations, selling off the technology expense management business unit to Tangoe. Tangoe uses the customization of the Rivermine platform to support complex IT expense and payment management environments for large enterprises.

November 2015 – IBM acquires Gravitant, a hybrid cloud brokerage solution used to help companies to purchase cloud computing services across cloud environments. Later renamed IBM Cloud Brokerage, this capability was intended to support IBM’s Global Technology Services unit in supporting multi-cloud and complex enterprise hybrid cloud environments. This acquisition logic ended up being accurate in the long run, but was too early considering that the multi-cloud era is really only beginning now in the 2020s.

December 2018 – HCL purchases a variety of IBM software products for $1.8 billion, including Appscan and BigFix. Although these Rational and Tivoli products provided enterprise value for many years, they eventually became outdated and seen as legacy monitoring products.

January 2019 – Apptio is acquired by Vista Equity Partners for $1.94 billion. At the time, I thought this was a bargain even though it was a 53% premium to the trading price at the time. At the time, Apptio had gone through a rapid stock price fall due to some public market overreaction and Vista Equity came in with a strong offering that pleased institutional investors. With investments in IT and financial software companies including Bettercloud, JAMF, Trintech, and Vena, Vista Equity was seen as an experienced buyer capable of providing value to Apptio.

May 2019 – Apptio acquires Cloudability, entering the cloud cost management or Cloud FinOps (Financial Operations) space. With this acquisition, Apptio answered one of my long-time criticisms of the vendor, that it did not directly manage IT spend after holding out on directly managing a trillion dollars of enterprise telecom, network, and mobility spend. This transaction put visibility to $9 billion in multi-cloud spend across the Big 3 providers under Apptio’s supervision while maintaining Apptio’s vendor-neutral approach to IT finances.

December 2020 – IT Asset Management vendor Flexera is acquired by private equity firm Thoma Bravo. Over the next couple of years, Flexera develops a strong relationship with IBM to support IT Asset Management.

December 2020 – IBM acquires Instana to support observability and Application Performance Management. As real-time continuity, remediation, and observability have become increasingly important for monitoring the health of enterprise IT, this acquisition provides a crucial granular perspective for IBM clients.

February 2021 – Apptio acquires Targetprocess to support agile product and portfolio management. The ability to plan and budget projects and products allows Apptio to support IT at a more granular, contingent, and business-contextual level.

June 2021 – IBM acquires Turbonomic, an application resource, network performance, and cloud resource management solution. With this acquisition, IBM enters the FinOps space. In our 2022 Cloud Cost and Optimization SmartList, we listed IBM Turbonomic as a Distinguished Vendor noting that it focused “on application performance” and that the “software learns from organizations’ actions, so recommendations improve over time.”

October 2022 – Flexera One with IBM Observability aggregates cloud spend across multiple clouds. This offering combined with Flexera One’s status as an IBM partner gives IBM customers an option for multi-cloud spend management and the ability to purchase cost optimization based on cloud spend.

June 2023 – We come back to the present day, when IBM has agreed to purchase Apptio. So, now we are seeing a trend where IBM has invested in IT management solutions over the past couple of decades but has struggled to maintain market-leading status in those applications over time for a variety of reasons: market timing, market shifts, strategic positioning.

Concerns and Considerations

What is happening here? The problem isn’t that IBM is targeting bad companies, as IBM has consistently chosen top-tier companies and strong enterprise-grade solutions. This trend continues with Apptio, which has managed over 450 billion dollars in IT spend and provides a statistically significant lens for IT spend trends across a wide variety of vertical trends and geographies. From an acquisition perspective, Apptio makes perfect sense as a market leading solution executing on sales, marketing, and targeted inorganic growth to provide financial visibility and operational automation across global IT departments.

And the problem is not a lack of interest, as IBM has consistently targeted IT sourcing, expense, and performance management solutions with some success. IBM usually knows what it is trying to accomplish in purchasing solutions (with the exception of the missed Rivermine opportunity) and has done a good job of identifying where it needs to go next. As an example, IBM was early, perhaps too early, in pursuing multi-cloud brokerage services but in retrospect there is no doubt that multi-cloud management was the future of IT.

Based on my long market perspective of the Technology Lifecycle Management market, I think IBM has run into two main issues in this market: market size and partnership opportunities.

First, look at market size. This Technology Lifecycle Management market simply has not traditionally been an extremely large multi-billion dollar market on the scale of analytics, mainframes, or services. ITFM and related IT cost management services will always struggle to be much larger than a couple of billion dollars in revenue, as proven by market leaders across IT finance and cost such as Apptio, Tangoe, Calero, Zylo, Cass Information Systems, Flexera, Snow Software, CloudHealth (now VMware Aria), and Spot by NetApp. All of these solutions have grown to the point of managing billions of dollars, but none of these standalone businesses or business units have come close to reaching a billion dollars in annual recurring revenue. This is not an issue, other than that it is traditionally hard for behemoth global enterprises with $100 billion+ in annual revenue expectations to be fully committed to businesses of this size without trying to turn them into “larger” solutions that often lose focus.

A second issue is that IBM has a lot of internal pressure to play nicely with partners. The recent Flexera One partnership announcements are a good example where Flexera has quickly emerged as a strong partner to support IT asset management and multi-cloud cost management challenges and now will have to be rationalized in context of the capabilities that Apptio brings to market once this acquisition is completed. But when IBM has made commitments and plans to build significant services practices around a large partnership, it can be difficult to shift away from those plans no matter how significant the acquisition is. The challenge here is that even if the direct software revenue may pale in comparison to the services wrapped around it, the service revenue is still dependent on the quality of software used to provide services.

And despite any internal concerns about these issues, this is not a deal that Apptio and Vista Equity could refuse. The basic math here of adding $2.66 billion in market value in 4 and a half years, or roughly $600 million per year (minus the cost of acquisitions) is a no-brainer decision. Anyone who did not seriously consider this transaction would be considered negligent.

In addition, there are good reasons for Apptio to join a larger organization. There are limits to the organic development that Apptio can pursue across the Technology Lifecycle Management cycle across sourcing, observability, contingent resources and services, continuity planning, and MACH (Microservices, APIs, Cloud-Native, Headless) architecture support compared to what IBM (including Red Hat OpenShift and IBM Consulting) can provide. And IBM is obviously still a core provider when it comes to global IT support with a vested interest in helping global enterprises and highly regulated organizations with their IT planning capabilities.

Recommendations

So, what does this mean for IBM and Apptio customers? This is a nuanced decision where every current client will have specific exceptions associated with the customization of their IT portfolio. But here are some general starting points that we are providing as guidelines to consider this transaction.

For IBM: this is an acquisition where IBM is making a good decision, but success is not guaranteed just because of choosing the right vendor in the right space. There will be additional work needed to rationalize Apptio’s portfolio in light of how Turbonomic goes to market and how the Flexera One  partnership is currently structured, just as a starting point. Amalgam Insights hopes that Apptio will be the umbrella brand for IT oversight in the near future as IBM Rational, IBM Tivoli, and IBM Lotus served as strong brands and focal points. IBM already has a variety of cloud and AIOps capabilities across Turbonomic, Instana, and Red Hat Openshift management tools for Apptio to serve both a FinOps and CloudOps hub as well as a strong go-to-market brand.

There is room for mutual success in this vision, as Flexera One’s ITAM capabilities are outside the scope of Apptio’s core concerns. This does likely mean that Flexera’s cloud cost capabilities will be shelved in favor of Apptio Cloudability and this needs to be a commitment. IBM needs to be a bit more greedy when it comes to supporting its direct software products than it traditionally has been over the last decade in maintaining the best-in-breed capabilities that Apptio is bringing to market, as the talent and vision of the current Apptio team is a significant portion of the value being acquired. IBM can be a challenging environment for software solutions, as every decision is seen through a multitude of lenses with the goal of finding some level of consensus across a variety of conflicting stakeholders. As this balance is sought, Amalgam Insights hopes that IBM focuses on building its direct software business and keeping Apptio’s finance, cost, and project management capabilities at a market-leadership level that will be championed by customers and analysts, even if this comes at the cost of growing partnerships. It can be easy for IBM software solutions to get the short shrift as its direct revenue can sometimes pale in comparison to larger services contracts, but the newest generation of IT to support new data stacks, hybrid cloud, and AI-enabled decisions and generative assets is in its infancy and IBM has acquired both solutions and a product and service team prepared to take this challenge head-on.

For Apptio: The past five years have been a strong validation of the continued opportunities that exist in IT Financial Management across hybrid cloud, software, and project management. There are still massive opportunities in contingent labor and traditional telecom and data center cost management markets as well as the opportunity to get more granular with API, transactional logs, and technological behavior that can be used to align the cost, budget, and health of the IT ecosystem. Amalgam Insights hopes that Apptio is treated similarly to Red Hat as a growth engine for the company and that Apptio has the operational flexibility to continue operating on its current path, but with more ambition matching the scale of IBM’s technology relationships and goals of solving the world’s biggest challenges.

For Apptio customers: You are working with a market leader in some area of IT finance or multi-vendor public cloud management and should hold fast on demands to retain the tech and support structure currently in place. As you move to IBM contractual terms, make sure that Apptio-related service terms, commitments, and responsibilities stay in place. This is an area where Amalgam Insights expects that the Technology Business Council will prove useful as a collective voice of executive demands to drive future Apptio development and evolution. Be aware that there are additional stakeholders at the table when it comes to the future of Apptio and it will be increasingly important for direct Apptio customers to maintain and increase demands in light of the increased complexity that will inevitably become part of the management of Apptio.

For IBM customers: You are likely already an Apptio customer based on Apptio’s current client base: there was a lot of overlap and synergy between the customer bases. But if not, this is a good time to evaluate Apptio as part of the overall IBM relationship as a dedicated solution for finance and cost management. In doing so, get IBM executive commitment regarding core features and functionality that will be strategically important for aligning IT activity to business growth. To deal with the cliches that every company is now a “software company” or a “data-driven company,” companies must have strong financial controls over the technology components that drive corporate change. At the same time, it is important to maintain a best-in-breed approach rather than be locked into an aging ERP-like experience as many companies experienced over the past decade.

These considerations are all a starting point for how to take action as IBM moves towards acquiring Apptio. Amalgam Insights expects there should be little to no concern with the acquisition moving forward as it is both mutually beneficial to all parties and lacks any sort of monopoly or antitrust issue that has slowed down larger acquisitions.

If you are seeking additional guidance to more granular aspects of considering Apptio, Flexera, IBM Turbonomic or other vendors in the IT finance, cloud FinOps, SaaS Management, or other related Technology Lifecycle Management topics, please feel free to contact Amalgam Insights to schedule an inquiry or to schedule briefing time.

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Hybrid Workforce Management: Navigating the Complexities of a Diverse Workforce in the Modern Era

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.

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Why Are Spreadsheets Still A Common FP&A Tool?

“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.

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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 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.

From Amalgam Insights’ perspective, this initial question of planning application vs platform 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.