The Partner Opportunity For Microsoft Azure AI Services
A Total Economic Impact™ Partner Opportunity Analysis
A Forrester Total Economic Impact™ Study Commissioned By Microsoft, July 2024
Providers of software and technology services can take advantage of significant revenue and profitability opportunities by embracing artificial intelligence (AI) in their practices. Widespread excitement in all business verticals over generative AI’s potential presents providers with unusual growth prospects based on identifying AI business cases, bridging customers’ capability gaps, and developing innovative tools (both in services and software) to drive process automation and value for their customers. Such rapid growth, coupled with the promise of higher margins as a result of AI-bred automation and productivity improvements in their own processes, provides an unprecedented opening for those firms willing to invest in building their AI capabilities and offerings.
Microsoft Azure AI services is a suite of cloud-based AI services that help developers and organizations create intelligent, cutting-edge, market-ready, and responsible applications with out-of-the-box and prebuilt tools, APIs, and models. These services are designed to help modernize business processes faster and build responsible AI solutions quickly to solve complex business problems and drive continued digital transformation.
Microsoft commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential business opportunity and return on investment (ROI) partners may realize by building and scaling an AI practice using Microsoft Azure AI Services.1 The purpose of this study is to provide potential and existing partners with a framework to evaluate the potential business opportunity associated with building, managing, and selling Azure AI Services as part of the Microsoft partner ecosystem.
Return on investment (ROI)
167%
Net present value (NPV)
$9.79M
To better understand the revenue streams, investments, and risks associated with a Microsoft Azure AI Services practice, Forrester interviewed 15 representatives at eight existing Azure AI Services partners with experience collaborating with Microsoft to build or innovate and ultimately sell and scale Azure AI services. These partners included both systems integrators (SIs) and independent software vendors (ISVs). In several cases, the organizations sold both integration services and software-as-a-service (SaaS) products based on their own intellectual property (IP).
To illustrate the financial impact and subsequent partner business opportunity for Microsoft Azure AI Services partners, Forrester aggregated the characteristics of these interviewees and combined the results into a single composite organization, a $500 million technology company with a $492 million services business and a small but fast-growing $8 million software business of its own.
Key Findings
Revenue opportunities. The composite partner organization captures incremental revenues and profit from four different sources as it leverages Microsoft Azure AI Services to grow its AI practice, which are representative of those experienced by the interviewees’ organizations:
- New customers captured with AI projects represent $13.6 million in new revenue and $5.2 million in gross profit. AI presents the composite partner organization with a powerful opportunity to reach out to and win new customers. With the market leadership Microsoft has established with Azure AI, the composite partner can position itself as a leader in helping customers use AI to transform their businesses. The composite partner thus has an opportunity to attract competitors’ customers and win engagements specifically for AI services.
- New and improved IP products associated with AI deliver more than $8.0 million in revenue and $5.7 million in gross profit. The extensive enablement and upskilling support that Microsoft provides allows the composite partner to use Azure AI Services to quickly improve the functionality of its IP products and release new AI-based products. The resulting speed to market delivers both lower customer churn and additional revenues for the new products.
- New AI projects sold to current customers generate $11.2 million in revenue and $4.3 million in gross profit. Much of the early success the composite partner has with its AI practice is the result of selling AI products and services to current customers. Since it is familiar with the buyers and many of the business problems at these companies, the composite partner is in a strong position to start conversations with the right people and help their customers benefit from AI.
- Direct incentive payments from Microsoft offset selling costs by $480,000. The composite partner benefits from Microsoft’s Azure Innovate program, which provides funding, technical support, and other resources to partners building their AI businesses. The composite partner uses the incentives to help offset the costs of bringing in new customers and developing new AI business at current clients.
Key outcomes. Benefits that provide value for the composite partner organization but are not quantified for this study include:
- Improved employee productivity on traditional projects from using AI internally. The composite partner not only deploys AI for its customers’ benefit but also uses the technology in its own business processes. As AI becomes widespread and ingrained in the composite partner’s workstreams, it improves productivity even among employees working on more traditional, non-AI-infused projects, and this translates to better margins across the board.
- The opportunity to reposition the organization as an industry leader and client advisor. The introduction of generative AI provides the composite partner with an opportunity to reset itself, as it is on an equal footing with its competitors in working with a new technology. By committing early to building its AI capabilities, the composite partner stakes out a position as an expert and leader in its particular geographic, industry, or technical sector.
Investments. Beyond the costs of delivering its services (e.g., consultants, engineers, architects, cloud compute capacity, etc.), which are embedded in the gross margin calculations of each revenue stream, the composite partner organization also invests in the following:
- Research and development initiatives represent a $3.5 million expenditure. To build its business and, in particular, to be seen as a leader in its space, the composite partner must invest significant resources in creating the innovative solutions and products its customers seek.
- Incremental marketing activity involves an additional $1.8 million investment. The composite partner must ensure that its customer base and prospects are aware of its expertise with AI and the success it is delivering to its customers. To gain leadership status, it must establish its credentials early and often with its target audience. This involves ramping up its content engine, sponsoring and/or participating in industry events around the world, and perhaps advertising in appropriate media.
- Employee upskilling costs the partner approximately $604,000. Finally, the composite partner must expand the skill base of all the employees involved in selling and delivering its AI solutions; it must prepare those employees in advance of deploying them to build the AI practice.
The representative interviews and financial analysis found that a $500 million composite partner experiences total present value (PV) incremental profits of $15.66 million over three years versus investments of $5.87 million, adding up to a net present value (NPV) of $9.79 million and an ROI of 174%.
Key Statistics
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Return on investment (ROI):
167%
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Gross margin:
60.5%
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Payback:
<6 months
Pro Forma Revenue And Margin Opportunity: Three-Year Analysis (USD)
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| PL1 | Current customer AI revenues | C6 | $1,000,000 | $3,300,000 | $3,900,000 | $4,500,000 |
| PL2 | New customer AI revenues | A6 | $0 | $1,650,000 | $3,900,000 | $7,500,000 |
| PL3 | Total services revenue | PL1+PL2 | $1,000,000 | $4,950,000 | $7,800,000 | $12,000,000 |
| PL4 | Total services gross profit | At+Ct | $350,000 | $2,252,250 | $3,685,500 | $5,880,000 |
| PL5 | Total services gross margin % | PL4/PL3*100 | 35.0% | 45.5% | 47.3% | 49.0% |
| PL6 | Incremental IP revenues due to AI | B3+B4 | $0 | $1,152,000 | $2,323,200 | $4,531,200 |
| PL7 | Total IP revenue | PL6 | $0 | $1,152,000 | $2,323,200 | $4,531,200 |
| PL8 | Total IP gross profit | Bt | $0 | $973,440 | $2,038,608 | $4,123,392 |
| PL9 | Total IP gross profit margin % | PL8/PL7*100 | 0.0% | 84.5% | 87.8% | 91.0% |
| PL10 | Total AI practice revenue | PL3*PL7 | $1,000,000 | $6,102,000 | $10,123,200 | $16,531,200 |
| PL11 | Total AI practice gross profit | PL4*PL8 | $350,000 | $3,225,690 | $5,274,108 | $10,003,392 |
| PL12 | Total AI practice gross margin % | PL11/PL10 | 35.0% | 52.9% | 56.5% | 60.5% |
| PL13 | Incremental Research & Development | Et | $318,750 | $1,106,250 | $1,275,000 | $1,443,750 |
| PL14 | Incremental marketing spend | Ft | $0 | $610,200 | $759,240 | $826,560 |
| PL15 | Employee upskilling | Gt | $31,380 | $280,841 | $196,000 | $206,400 |
| PL16 | Total incremental expenses | PL13+PL14+PL15 | $350,130 | $1,997,291 | $2,230,240 | $2,476,710 |
| PL17 | AI practice operating income | PL11-PL16 | -130 | $1,228,399 | $3,493,868 | $7,526,682 |
| PL18 | AI practice operating margin | PL17/PL10*100 | 0.0% | 20.1% | 34.5% | 45.5% |
TEI Framework And Methodology
From the information provided in the interviews, Forrester constructed a Total Economic Impact™ framework for those partners considering building and growing a Microsoft Azure AI Services practice.
The objective of the framework is to identify the revenue streams, investments, flexibility, and risk factors that affect the investment decision. Forrester took a multistep approach to evaluate the holistic opportunity for partners building and growing a Microsoft Azure AI Services practice.
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Due Diligence
Interviewed Microsoft stakeholders and Forrester analysts to gather data relative to Microsoft Azure AI Services.
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Interviews
Interviewed 15 representatives at eight partner organizations with existing Microsoft Azure AI Services practices to obtain data about revenue streams and investments.
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Composite Organization
Designed a composite partner organization based on characteristics of the interviewees’ organizations.
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Financial Model Framework
Constructed a financial model representative of the interviews using the TEI methodology and risk-adjusted the financial model based on issues and concerns of the interviewees.
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Case Study
Employed four fundamental elements of TEI in modeling the impact of a Microsoft Azure AI Services practice: revenue, investments, flexibility, and risks. Given the increasing sophistication of ROI analyses related to IT investments, Forrester’s TEI methodology provides a complete picture of the total economic impact of purchase and partnership decisions. Please see Appendix A for additional information on the TEI methodology.
Disclosures
Readers should be aware of the following:
This study is commissioned by Microsoft and delivered by Forrester Consulting. It is not meant to be used as a competitive analysis.
Forrester makes no assumptions as to the potential ROI that other organizations will receive. Forrester strongly advises that readers use their own estimates within the framework provided in the study to determine the appropriateness of an investment in a Microsoft Azure AI Services practice.
Microsoft reviewed and provided feedback to Forrester, but Forrester maintains editorial control over the study and its findings and does not accept changes to the study that contradict Forrester’s findings or obscure the meaning of the study.
Microsoft provided the partner names for the interviews but did not participate in the interviews.
Lead Consultant
Kim Finnerty
Drivers leading to the Microsoft Azure AI Services practice investment
Interviews
| Role | Industry | Region | [Relevant Metric] |
|---|---|---|---|
| • Head of global services • Leader, Microsoft practice | Systems integrator | Global | $13.7 billion |
| Leader, Azure AI practice | Systems integrator | Global | $25.7 billion |
| • AI CTO • Chief architect | Systems integrator | Global | $13.0 billion |
| Leader, Microsoft business group | Systems integrator | Global | $19.4 billion |
| • VP, technology solutions • Head, cloud tech & delivery • Senior AR manager | Systems integrator | Global | $ 4.7 billion |
| Founder, chief AI officer | Manufacturing software | Europe | $11 million |
| • Business unit head • Consultant, product strategy and design | Decision management software | EMEA | $383 million |
| • Chief commercial officer • Head of technology • Head of marketing | Trade finance software | UK | $23 million |
Partner Goals, Challenges, And Drivers
Partners were diverse in size, backgrounds, functional and vertical specializations, types, and degrees of engagement with Microsoft. They partnered with Microsoft to build and scale their Azure AI Services businesses for a myriad of reasons, including:
- An extraordinary level of customer interest in AI. Interviewees told Forrester that they were excited by the level of interest in AI in the marketplace; this was a key factor in their organizations’ decision to commit the necessary resources to building their AI capabilities, services, and products. The AI CTO at one systems integrator spoke for many interviewees when they said: “I see this tsunami of clients coming in with what I would call strategic business challenges. They’re not tiny little technical projects. It’s opening us up to a completely new tranche of business opportunities that are coming in at a very senior level from our clients around the globe.”
- Their frustration with customer conversations that focused primarily on technology trade-offs and SLAs. As the previous quote implies, many of the interviewees at systems integrators expressed frustration with the types of opportunities they were able to access before AI. In a relatively established technology services market, they found it difficult to engage business leaders outside the IT team or to participate in a strategic advisory role. This thwarted their access to potentially larger and more profitable opportunities.
- The opportunity to establish/maintain their sector leadership. The interviewees felt that the introduction and growth of generative AI put them and their competitors on a relatively equal footing. No one had demonstrated expertise because the technology was so new, which gave each of them the opportunity to establish themselves as a leader in the space or in some niche in the space — whether or not they had been perceived as a leader previously. They saw this as a rare opportunity to up their game in their geographic or industry sector.
Selecting The Right Partner Program
In choosing a partner and platform around which to build their organizations’ AI solutions, interviewees emphasized the importance of the following factors:
- Customer demand and familiarity. Partners tend to be cloud vendor neutral when working with their customers. Interviewees explained that most customers come to them with a cloud vendor preference, based either on existing relationships or a belief that one provider is superior. Interviewees didn’t want to begin their relationship or expend resources selling the customer on a cloud vendor in addition to the solution they proposed. Interviewees assured Forrester that this is an important dynamic that favors building their practice with Microsoft Azure AI services.
- Easy access to the related technologies needed to enable AI solutions. Interviewees explained that when they are building out an AI solution for a client, they often need additional tools, not necessarily related to AI, in order to optimize the entire solution. For instance, they might need a speech-to-text translation tool or other tools that help refine the prompts and the responses. The extensive ecosystem of products and services available within Azure provides them with easy access to tools that are designed to support their Azure AI-based solutions and IP products.
- Go-to-market support. Interviewees reported that Microsoft provided strong support for their efforts to build an AI business. They mentioned several types of support, including Azure Marketplace, customer leads, and co-selling participation. In particular, several interviewees mentioned the Azure Innovate program, which provided direct payments to the partner when they brought in new AI services or repeatable IP.
Composite Organization
Based on the interviews, Forrester constructed a TEI framework, a composite partner company, and an ROI analysis that illustrates the areas financially affected. The composite organization is representative of the eight partner organizations whose representatives Forrester interviewed, and it is used to present the aggregate financial analysis in the next section.
Description of composite. The partner organization sells both systems integration services and SaaS products based on its own IP, although it has primarily been known as a systems integrator. Its revenues of $500 million comprise $492 million from services and $8 million from its IP. The partner employs 10,000 people, of which 17 make up the core AI team as they begin expanding their AI practice with generative AI (genAI) projects: eight salespeople, seven AI engineers/developers, and two senior architects.
Quantified revenue data as applied to the composite
Revenue Streams
| Ref. | Revenue Source | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|
| At | New customer acquisition | $1,001,000 | $1,842,750 | $3,675,000 | $6,518,750 | $5,194,016 |
| Bt | Increased IP revenue and margin | $973.440 | $2,038,608 | $4,123,392 | $7,135,440 | $5,667,711 |
| Ct | New AI projects with current customers | $1,251,250 | $1,842,750 | $2,205,000 | $5,299,000 | $4,317,083 |
| Dt | Azure Innovate incentive payments | $150,000 | $180,000 | $260,000 | $590,000 | $480,466 |
| Total incremental profits | $3,375,690 | $5,904,108 | $10,263,392 | $19,543,190 | $15,659,276 | |
New Customer Acquisition
Evidence and data. Interviewees agreed that one of the most attractive opportunities of focusing on building their AI business was the chance to extend their reach to new customers. New customers were particularly attractive for several reasons. First, they represented net new revenue for the partner; these projects were not simply budget trade-outs at an existing customer. Second, they increased the partner’s overall market share by allowing them to win at least some of the revenue available at competitors’ clients. Third, as a result of the kinds of projects that AI opened up, the partner could step into the new customer in a strategic advisory role, rather than simply as a technology services provider. Finally, they provided a chance for the partner to identify and win additional (AI and non-AI) work with the new customer downstream.
One important strategy that interviewees cited for building their AI practices quickly was not charging a premium price for AI projects or AI enhancements to their IP products. There were two reasons for this strategy.
- First, interviewees prioritized getting AI solutions into their customers’ operations and encouraging their use. They were confident that their customers would be highly satisfied with their AI deliverables and that those customers would begin to think about other ways they could use the technology in their organization, generating additional products for the partner and further cementing their market share growth and leadership.
- Second, their AI work has a built-in profitability boost as a result of using Microsoft Azure AI services internally to develop solutions and software products. Interviewees unanimously agreed that they recognized substantial productivity improvements for their engineering and developer teams because those professionals leveraged Azure AI services to do their own work. The AI CTO at a systems integrator said: “We know, as a general rule with AI, we can release software to market 40% faster. At the individual developer or architect level, we measure productivity gains in a percentage range from, say, the mid-30s to the low-50s. Interestingly, it seems to be the more experienced engineers and architects that get the bigger impact.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite partner:
- The partner runs 30 proofs of concept (PoCs) in Year 1, an increase from 15 in the period preceding Year 1. This increases to 45 in Year 2 and 90 in Year 3.
- PoCs convert to production AI projects at a rate of 35%, so each year the partner completes AI engagements equal to 35% of the previous year’s PoCs.
- Of the total AI projects undertaken in Year 1, those commissioned by new customers make up 40% in Year 1, 50% in Year 2, and 60% in Year 3.
- The average revenue for an AI project starts out at $550,000 and grows to $750,000 over the three years as the partner becomes more skilled in identifying opportunities and applying AI solutions.
- The partner’s average services gross margin is 35%.
- Because the partner is using AI in its own operations, it experiences a margin improvement due to overall employee productivity of 30% in Year 1. This increases to 35% in Year 2 and 40% in Year 3 as developers and administrative employees become more familiar with the capabilities of AI in their work.
Results. The three-year total PV gross profit (discounted at 10%) of new AI customer acquisition is $5.2 million.
New Customer Acquisition
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| A1 | Proofs of concept (PoCs) in progress | Composite | 30 | 45 | 90 | |
| A2 | PoCs converted to projects | Y1: Composite; Y2 and Y3: 35%*A1PY | 9 | 11 | 16 | |
| A3 | Percentage sold to new customers | Composite | 40% | 50% | 60% | |
| A4 | AI projects sold to new customers | A2*(1-C3) | 4 | 6 | 10 | |
| A5 | Average AI project revenue | Interviews | $550,000 | $650,000 | $750,000 | |
| A6 | Total new customer AI project revenue | A4*A5 | $2,2000,000 | $3,900,000 | $7,500,000 | |
| A7 | Average project gross margin | Interviews | 35% | 35% | 35% | |
| A8 | Margin improvement due to increased AI productivity | Interviews | 30% | 35% | 40% | |
| At | New customer acquisition | (A6*A7)+ (A6*A7*A8) | $1,001,000 | $1,842,750 | $3,675,000 | |
| Three-year total: $6,518,750 | Three-year present value: $5,194,016 | |||||
Increased IP Revenue And Margin
Evidence and data. Executives at ISVs (as well as at SIs selling IP products) described several ways in which investing in their AI practice benefited their bottom line. To the extent that they were incorporating AI features and capabilities into their existing software offerings, they reported improvements in their customer churn rate. By proactively providing their subscribers with new and useful features at little to no cost, they greatly enhanced customer satisfaction; they also found they were less vulnerable to their competitors’ attempts to penetrate their accounts.
- A consultant on product strategy and design at a decision management software provider confirmed: “Our retention rate was already high, but this will keep competitors at bay. We have seen increased competition over the past 10 years, so having AI incorporated into the product helps reinforce our value and decreases the chance that customers will talk to a competitor.”
- The head of technology at a trade finance software company reported: “We saw major improvements in our product functionality for customers. TAT [turnaround time] was 45 minutes and is now under 30; document ID capture ratio was 70% and is now 98%; the algorithm used to take 40 seconds and now takes 15.”
In addition, partners reported they were able to develop and release new products specifically designed to showcase their AI capabilities and provide incremental value to their customers. The result was a new, fast-growing revenue stream from the uptake of these new products.
Finally, similar to their experience with AI-based services projects, the interviewees noted considerable increases in their (already healthy) gross margins on IP. These increases resulted from the providers’ use of Microsoft Azure AI in their own practices, where they found that it improved their work process — from enabling faster coding to reducing time for reviews and eliminating administrative/managerial tasks. They were also able to bring these products to market faster than they had in the past, allowing them to recognize revenue earlier and beat their competitors to market.
The leader of the Microsoft business group at a systems integrator enthused: “We are looking at about 30% to 35% productivity improvement, faster time to build code. And then also as it moves up a layer, we are seeing about 40% to even 45% productivity improvement from code reviews, code approval, and generally about an hour per day for all the managerial tasks.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite partner:
- The partner’s revenue from SaaS products based on its own IP is $8 million before the period of the analysis begins. This grows at a rate of 20% per year to $9.6 million in Year 1, $11.5 million in Year 2, and $13.8 million in Year 3.
- The average gross margin on its IP products is 65%.
- The partner improves these products by including new features and capabilities based on AI, thus increasing customer satisfaction and reducing the customer churn rate. Year 1 sees a 2% reduction in turnover, which rises to 3.5% in Year 2 and 5% in Year 3, thus delivering incremental revenue for the partner.
- In addition, the partner creates new AI-based software products that generate $960,000 in revenue in Year 1 and double each of the remaining years of the analysis.
- Finally, the partner’s use of AI in its own software development processes improves its margins by 30% to 40% over the three years of the analysis.
Results. The three-year total PV gross profit (discounted at 10%) of increased IP revenue and margin is $5.7 million.
Increased IP Revenue And Margin
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| B1 | Existing customer IP revenues | Composite | $9,600,000 | $11,520,000 | $13,824,000 | |
| B2 | Improvement in turnover rate | Interviews | 2.0% | 3.5% | 5.0% | |
| B3 | Incremental IP revenue from improved customer retention | B1*B2 | $192,000 | $403,200 | $691,200 | |
| B4 | New AI-related software products | Interviews | $960,000 | $1,920,000 | $3,840,000 | |
| B5 | Average IP gross margin | Composite | 65% | 65% | 65% | |
| B6 | Margin improvement due to increased productivity with AI | Interviews | 30% | 35% | 40% | |
| Bt | Increased IP revenue and margin | ((B3+B4)*B5)+ ((B3+B4)*B5*B6) | $973,440 | $2,038,608 | $4,123,392 | |
| Three-year total: $7,135,440 | Three-year present value: $5,667,711 | |||||
New AI Projects With Current Customers
Evidence and data. Most interviewees told Forrester that their early wins in the AI space were primarily with existing customers. While new customer penetration grew quickly, current customers provided a fertile source of new business early on. Because the partners had contacts and relationships in place at those customers and because they had a good grasp of the kinds of business problems that these customers were prioritizing, partners were in a unique position to proactively propose AI solutions or to respond quickly and credibly to AI-related questions posed by senior management at their current customers.
These incumbent partners were also in a strong position to sell additional AI projects to other business units within the customer organization or to propose new downstream projects related to the initial AI projects.
- The AI CTO at a systems integrator told Forrester, “We’ve seen clients repurpose their classic AI investments to now focus on generative AI because they believe the return on investment is going to come far quicker for them.”
- The head of global services at another systems integrator added: “Right now for all of the projects, there are first, second, third phases, and we just see more work coming from them. I think it’s going to take three to five years for people to understand all the ways they can use it and build it into their systems.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite partner:
- As described in Benefit A, the composite partner runs 30 proofs of concept (PoCs) in Year 1, an increase from 15 in the period preceding Year 1. This increases to 45 in Year 2 and 90 in Year 3.
- PoCs convert to production AI projects at a rate of 35%, so each year the partner completes AI engagements equal to 35% of the previous year’s PoCs.
- Of total AI projects undertaken in Year 1, those commissioned by current customers make up 60% in Year 1, 50% in Year 2, and 40% in Year 3.
- The average revenue for an AI project starts at $550,000 and rises to $750,000 by Year 3.
- The partner’s average services gross margin is 35%.
- Because the partner is using AI in its own operations, it experiences a margin improvement due to overall employee productivity of 30% in Year 1, 35% in Year 2, and 40% in Year 3.
Results. The three-year, risk-adjusted total PV gross profit (discounted at 10%) of new AI projects with current customers is $4.3 million.
New AI Projects With Current Customers
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| C1 | Proofs of concept (PoCs) in progress | Composite | 30 | 45 | 90 | |
| C2 | PoCs converted to projects | Y1: Composite; Y2 and Y3: 35%*C1PY | 9 | 11 | 16 | |
| C3 | Percentage sold to current customers | Composite | 60% | 50% | 40% | |
| C4 | Incremental AI projects from current customers | Composite | 5 | 6 | 6 | |
| C5 | Average AI project revenue | Interviews | $550,000 | $650,000 | $750,000 | |
| C6 | Total current customer AI project revenue | C4*C5 | $2,750,000 | $3,900,000 | $4,500,000 | |
| C7 | Average project gross margin | Interviews | 35% | 35% | 35% | |
| C8 | Margin improvement due to AI productivity gains | Interviews | 30% | 35% | 40% | |
| Ct | New AI projects with current customers | (C6*C7)+ (C6*C7*C8) | $1,251,250 | $1,842,750 | $2,205,000 | |
| Three-year total: $5,299,000 | Three-year present value: $4,317,083 | |||||
Azure Innovate Incentive Program
Evidence and data. The composite partner’s total investment in its AI business is significantly offset by Microsoft’s incentive programs, particularly Azure Innovate, which provide the composite partner with direct incentives for each of its current and new customer AI projects outlined in the Revenue section as well as for new repeatable IP products that boost Azure AI usage. Interviewees described other types of support, including help upskilling employees with certification programs and technical support as well as enablement assistance. They were able to quantify only the incentive funding portion.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite partner:
- The partner brings in a total of nine new AI projects in Year 1, 11 in Year 2, and 16 in Year 3. They each fall into the “small” category under the Azure Innovate program, so each qualifies for a payment of $15,000 to support and reimburse the partner for their selling efforts to bring in that piece of business.
- The partner also develops and launches three new IP products, two of which fall into the “small” category for software launches and qualify for the $15,000 incentive payment. The third falls into the “medium” category and qualifies for a $20,000 incentive payment.
Results. The three-year total PV (discounted at 10%) of these Azure Innovate payments is $480,000.
Azure Innovate Incentive Program
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| D1 | Total AI projects sold | A2 | 9 | 11 | 16 | |
| D2 | Incentive payments per project | Composite | $15,000 | $15,000 | $15,000 | |
| D3 | Total new IP products launched | Composite | 1 | 1 | 1 | |
| D4 | Incentive payments per IP product | Composite | $15,000 | $15,000 | $20,000 | |
| Dt | Azure Innovate incentive program | D1*D2*D3*D4 | $150,000 | $180,000 | $260,000 | |
| Three-year total: $590,000 | Three-year present value: $480,466 | |||||
Key Outcomes
Interviewees mentioned the following additional benefits that their partner organizations experienced but were not able to quantify:
- Improved engineering productivity on traditional projects from using AI internally. The composite partner not only deploys AI for its customers’ benefit but also uses the technology in its own business processes. As AI becomes widespread and ingrained in the composite partner’s workstreams, it improves productivity even among employees working on more traditional, non-AI-infused projects, and this translates to better margins across the board.
- The opportunity to reposition the organization as an industry leader and client advisor. AI products and solutions are not typically generated in response to technical questions and issues; they address improving a firm’s basic way of doing business and often respond to questions posed by C-suite executives. Focusing on AI removed interviewees’ organizations from purely tactical discussions with technical professionals to more strategic discussions with business leaders. As the chief architect at one systems integrator recalled: “I don’t get into a conversation these days about SLAs or technology. It’s all about ‘How are you going to reduce my emissions?’ or ‘How are you going to improve my time to market?’ or ‘How am I going to be able to engage with my employees through this?’ Customers are much more seeing us in an advisor role rather than a task outsourcing role. And that’s good.”
Flexibility
The value of flexibility is unique to each partner. There are multiple scenarios in which a Microsoft Azure AI Services partner might implement an AI practice and later realize additional opportunities. In particular, interviewees whose organizations are seeing success in AI told Forrester that those AI projects often lead to larger, more traditional modernization projects. They found this to be true for AI projects undertaken both for current and new customers.
- The head of global services at a systems integrator explained: “We believe building AI-driven accelerators to deliver projects faster is a competitive advantage for us, and we believe that if we can help our clients, let’s say, migrate to the cloud infrastructure where we’re also experts, we have a better chance of helping them manage that infrastructure and providing more value to them.”
- The chief architect at another systems integrator supported that assessment, declaring: “There will be a massive amount of work to be done on data modernization. In fact, there’s probably a lot of failed projects at the moment because of the quality of data. Then you need to refigure your privacy and security standards and, again, that’s going to drive an awful lot of work. Rethinking the application modernization space will be important and provide a lot of downstream work.”
- The leader of the Microsoft business unit at another systems integrator agreed: “Once you start working on an AI project, it opens up a number of different avenues from both downstream and upstream work that needs to happen before you can execute on that particular AI mandate. This spans data security, data governance, risk and compliance, change management, consulting and advisory. Whether you are doing a CRM transformation or a supply chain transformation, the entire ecosystem needs to be looked at.”
- Finally, the leader of the Azure AI practice at a fourth systems integrator reinforced that this area of flexibility applies to new customers, not just those with which the partner has an ongoing relationship. “[New customer projects] are important because they give us an opportunity to grow these accounts into large-scale accounts where we not only do AI, but the entire spectrum of our services can be positioned there.”
Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Appendix A).
Quantified investment data as applied to the composite
Total Incremental Investment
| Ref. | Investment | Initial | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|---|
| Etr | Incremental research and development | $318,750 | $1,106,250 | $1,275,000 | $1,443,750 | $4,143,750 | $3,462,862 |
| Ftr | Incremental marketing spend | $0 | $610,200 | $759,240 | $826,560 | $2,196,000 | $1,803,205 |
| Gtr | Employee upskilling | $31,380 | $280,841 | $196,000 | $206,400 | $714,621 | $603,745 |
| Total costs | $350,130 | $1,997,291 | $2,230,240 | $2,476,710 | $7,054,371 | $5,869,812 | |
Incremental Research And Development
Evidence and data. The first investment most interviewees cited was in the area of research and development. Whether they were talking about services or software products, their ability to sell AI and leverage their expertise to improve their market share and margins depended on their investment in creating the solutions and products their customers needed. This internal development work — not captured in project gross margins as it was a prerequisite for generating revenue — included the costs of human resources and cloud compute resources. The humans were the highly skilled (and expensive) architects and developers who had been trained to make the most of Microsoft Azure AI Services; the cloud resources represented the compute capacity required to experiment with and run the large models behind the solutions and IP that the partners were creating.
As the leader of the Microsoft business unit at one systems integrator explained: “We have gone beyond the AI asset studios into advanced AI labs, where we are investing ahead of the curve in terms of research and development of AI models for industry-specific solutions. We are investing heavily in doing AI model research with the data scientists and two or three dozen PhDs.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite partner:
- The organization experiences an increase of approximately 15% of what its total Azure spend would have been without the internal AI development work.
- It commits 15% of its AI architect and developer workforce’s time to creating innovative AI products and solutions.
- The average fully burdened salary of those AI experts is $168,750.
Results. The three-year, total PV of incremental research and development investment (discounted at 10%) is approximately $3.5 million.
Incremental Research And Development
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|---|
| E1 | Expanded compute capacity | Composite | $150,000 | $600,000 | $600,000 | $600,000 | |
| E2 | Engineering FTEs dedicated to R&D | Composite | 1.0 | 3.0 | 4.0 | 5.0 | |
| E3 | Average AI engineer fully burdened salary | TEI standard | $168,750 | $168,750 | $168,750 | $168,750 | |
| Et | Incremental research and development | E1+(E2*E3) | $318,750 | $1,106,250 | $1,275,000 | $1,443,750 | |
| Three-year total: $4,143,750 | Three-year present value: $3,462,862 | ||||||
Incremental Marketing Spend
Evidence and data. Interviewees agreed that their organizations spent ahead of the curve on marketing to promote the value of their AI practices. These activities included sponsoring and attending industry events, publishing thought leadership papers and other content, digital advertising, running introductory workshops, and other awareness-building and lead-generating tactics.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite partner:
- The organization traditionally spends approximately 2.5% of revenue on marketing activities before it begins its commitment to build its AI business.
- In the first year of its AI push, the partner spends 10% of the incremental income generated by AI.
- The firm continues strong spending on marketing activity to establish and maintain its market leadership position over the next several years, although marketing investment drops to 7.5% in Year 2 and 5% in Year 3 in relation to revenue growth.
Results. The three-year total PV in incremental marketing investment is approximately $1.8 million.
Incremental Marketing Spend
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|---|
| F1 | Total incremental AI revenue | At+Bt+Ct | $0 | $6,102,000 | $10,123,200 | $16,531,200 | |
| F2 | Average marketing spend rate | Interviews | 2.5% | 10.0% | 7.5% | 5.0% | |
| Ft | Incremental marketing spend | F1*F2 | $0 | $610,200 | $759,240 | $826,560 | |
| Three-year total: $2,196,000 | Three-year present value: $1,803,205 | ||||||
Employee Upskilling
Evidence and data. Finally, interviewees described their initiatives to prepare their workforces for the AI opportunity they were pursuing. These efforts focused on core AI sales and delivery teams, including salespeople, system architects, and software developers/engineers. As with the other investment areas, the partner organizations spent ahead of the curve, constantly looking to train more people than were actually on the AI team so that ample talent would be available to feed their future growth.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite partner:
- The partner has 17 employees in its core AI team: eight in sales, seven AI engineers, and two senior architects. The team grows proportionally to 42 in the first year, 68 in the second, and 96 in the final year of the analysis, including those actively working on AI sales and delivery as well those being trained in advance of new AI revenue.
- The salespeople earn an average of $101 per hour, the AI engineers $81 per hour, and the senior architects $97 per hour on a fully burdened basis.
- Each employee on the AI team receives 80 hours of training relevant to their position when they join the team. The original 17 team members begin their training in the few months preceding the first year of the analysis and complete it during Year 1.
Results. The three-year total PV investment in employee upskilling is approximately $476,000.
Employee Upskilling
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|---|
| G1 | Total employees involved in AI sales and delivery | Composite | 17 | 42 | 68 | 96 | |
| G2 | AI sales employees | Composite | 8 | 22 | 36 | 50 | |
| G3 | AI engineering employees | Composite | 7 | 16 | 24 | 36 | |
| G4 | AI architects | Composite | 2 | 4 | 8 | 10 | |
| G5 | Average fully burdened sales hourly wage | TEI standard | $101 | $101 | $101 | $101 | |
| G6 | Average fully burdened engineer hourly wage | TEI standard | $81 | $81 | $81 | $81 | |
| G7 | Average fully burdened architect hourly wage | TEI standard | $97 | $97 | $97 | $97 | |
| G8 | Average hours of training/upskilling | Interviews | 20 | 72 | 80 | 80 | |
| Gt | Employee upskilling | ((G2*G5)+(G3*G6)+(G4*G7))*G8 | $31,380 | $280,841 | $196,000 | $206,400 | |
| Three-year total: $714,621 | Three-year present value: $603,745 | ||||||
Consolidated Three-Year Metrics
Cash Flow Chart (Risk-Adjusted)
Total costs Total benefits Cumulative net benefits-
The financial results calculated in the Revenue Streams and Investments sections can be used to determine the ROI, NPV, and payback period for the composite organization’s investment. Forrester assumes a yearly discount rate of 10% for this analysis.
Cash Flow Analysis
| Initial | Year 1 | Year 2 | Year 3 | Total | Present Value | |
|---|---|---|---|---|---|---|
| Incremental investments | ($350,130) | ($1,997,291) | ($2,230,240) | ($2,476,710) | ($7,054,371) | ($5,869,812) |
| Incremental profits | $0 | $3,375,690 | $5,904,108 | $10,263,392 | $19,543,190 | $15,659,276 |
| Net incremental profit | ($350,130) | $1,378,399 | $3,673,868 | $7,786,682 | $12,488,819 | $9,789,464 |
| ROI | 167% | |||||
| Payback | <6 months | |||||
Appendix A: Total Economic Impact
Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s technology decision-making processes and assists vendors in communicating the value proposition of their products and services to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of IT initiatives to both senior management and other key business stakeholders.
Total Economic Impact Approach
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Benefits represent the value delivered to the business by the product. The TEI methodology places equal weight on the measure of benefits and the measure of costs, allowing for a full examination of the effect of the technology on the entire organization.
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Costs consider all expenses necessary to deliver the proposed value, or benefits, of the product. The cost category within TEI captures incremental costs over the existing environment for ongoing costs associated with the solution.
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Flexibility represents the strategic value that can be obtained for some future additional investment building on top of the initial investment already made. Having the ability to capture that benefit has a PV that can be estimated.
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Risks measure the uncertainty of benefit and cost estimates given: 1) the likelihood that estimates will meet original projections and 2) the likelihood that estimates will be tracked over time. TEI risk factors are based on “triangular distribution.”
The initial investment column contains costs incurred at “time 0” or at the beginning of Year 1 that are not discounted. All other cash flows are discounted using the discount rate at the end of the year. PV calculations are calculated for each total cost and benefit estimate. NPV calculations in the summary tables are the sum of the initial investment and the discounted cash flows in each year. Sums and present value calculations of the Total Benefits, Total Costs, and Cash Flow tables may not exactly add up, as some rounding may occur.
Appendix B: Supplemental Material
Related Forrester Research
The Forrester Wave: AI Services, Q2 2024, Forrester Research, Inc., May 13, 2024.
The AI Services Landscape, Forrester Research, Inc., January 4, 2024.
Predictions 2024: Software Development, Forrester Research, Inc., October 30, 2023.
Appendix C: Endnotes
1 Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s technology decision-making processes and assists vendors in communicating the value proposition of their products and services to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of IT initiatives to both senior management and other key business stakeholders.
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