1 Introduction
The contribution of this study is to make an inventory of the current AI outsourcing practicesby focusing on formal and relational governance. This paper is organised as follows. First, our theoretical background addresses the concepts of governance for AI outsourcing. Subsequently, we introduce a research framework, based on the outsourcing governance framework of Lioliou et al. (2014). The research method is presented in Sect. 3, and the data analysis and results are described in Sect. 4. Discussions and conclusions are presented in Sect. 5 and 6, respectively.How are formal and relational governance used in artificial intelligence outsourcing?
2 Theoretical background
2.1 Artificial intelligence
2.2 IS governance
Author | Journal | Method | Contractual attributes | Relational attributes | |
---|---|---|---|---|---|
IS Governance | Miranda and Kavan (2005) | Journal of Information Technology | Literature study | Contract, assets, social capital | Commitment, Organizational governance |
Goo et al. (2009) | MIS Quarterly | Quantitative | Service level agreement elements | Relational norms, Conflict resolution, Trust, Commitment | |
Lacity et al. (2009) | Journal of Strategic Information Systems | Qualitative | Contract detail, type and duration | Trust, Reciprocity | |
Li et al. (2010) | Strategic Management Journal | Qualitative | Contract | Trust, Brokered access, Shared goal | |
Tiwana (2010) | Journal of Management Information Systems | Quantitative | Formal control (output, behavior) | Informal control (clan control) | |
Srivastava and Thompson (2012) | Journal of Management Information Systems | Quantitative | Contract performance, control mechanisms | Trust, Shared understanding | |
Chua et al. (2012) | MIS Quarterly | Qualitative | Formal control (output, behavior) | Clan control | |
Rai et al. (2012) | Journal of Management Information Systems | Quantitative | Contract, goals, flexibility | Trust, Information exchange, Conflict resolution | |
Huber et al. (2013) | Journal of Management Information Systems | Qualitative | No specific attributes | No specific attributes | |
Lioliou et al. (2014) | Information Systems Journal | Qualitative, case study | Contract, SLA, KPI, measurement | Psychological contract, Trust, Commitment, Dependency, Communciation | |
Oshri et al. (2015) | Journal of Strategic Information Systems | Quantitative | Contract, pricing mechanisms | Relationship quality, Conflict resolution, Trust, Commitment | |
Lacity et al. (2016) | Journal of Information Technology | Qualitative and Quantitative | Contract details and type, control mechanisms | Communication, Trust, Knowledge, Commitment, Cooperation | |
Chang et al. (2017) | MIS Quarterly | Quantitative | Clauses, risks, dependency, compensation | No specific attributes | |
Bui et al. (2019) | Journal of Strategic Information Systems | Qualitative | Detailed contract, cost reduction, portfolio governance | Innovation | |
Oshri et al. (2015) | Journal of Management Information Systems | Quantitative | Plans, goals (outcome), monitoring | Communication, Joint decision processes (authority) | |
Lioliou and Willcocks (2019) | Palgrave Macmillan, Cham | Quantitative | Flexibility, service levels, disputes | Psychological contract, Trust, Commitment, Communication | |
Kotlarsky et al. (2020) | MIS Quarterly | Qualitative and Quantitative | Formal control, contract, SLA, pricing | Trust, Relational norms, Commitment, Communication, Psychological contract | |
Kranz (2021) | Journal of Strategic Information Systems | Quantitative | Profit sharing, long-term orientation, contractual flexibility | Goodwil trust, Norms of reciprocity, Social interaction ties |
2.2.1 Formal IS governance
2.2.2 Relational IS governance
2.3 AI outsourcing governance research framework
3 Research method
Expert | Type of supplier | Company | Function interviewee | Interview time (min) |
---|---|---|---|---|
1 | Strategy advisor | BCG | Managing Director and Partner | 30 |
2 | BCG | Managing Director and Partner | 30 | |
3 | McKinsey | Client Partner | 30 | |
4 | Accounting and Consulting firm (Big 4) | EY | Client Partner | 60 |
5 | EY | AI subject matter expert | 60 | |
6 | KPMG | Senior Manager | 60 | |
7 | KPMG | AI subject matter expert | 60 | |
8 | Technology supplier | Accenture | Client Partner | 60 |
9 | Accenture | AI subject matter expert | 60 | |
10 | Atos | Client Partner | 60 | |
11 | Atos | AI subject matter expert | 60 | |
12 | HCL | Senior Vice President | 60 | |
13 | HCL | AI subject matter expert | 60 | |
14 | TCS | Vice President & Global Head | 45 | |
15 | TCS | Chief Technology Officer | 45 | |
16 | Market research advisor | Gartner | Client Partner | 60 |
17 | Gartner | AI subject matter expert | 60 | |
18 | Horses for Sources | Senior Vice President | 90 |
3.1 Data collection
3.2 Data analysis
4 Findings
We clustered our findings by means of the governance of AI outsourcing and its embedded elements and have added interview quotes to illustrate our findings.How are formal and relational governance used in Artificial Intelligence Outsourcing?
4.1 Client—supplier AI outsourcing arrangement
The typical set of profiles of clients involved in AI is 50% business stakeholders and 50% technology experts. However we see more and more clients initiating AI initiatives from a business side (source: Market research advisory firm—Gartner—Client Partner).Clients’ IT and innovation teams often lack the knowledge of what's really important for their business, how processes work, what the right procedures are, and where failures may occur. But it's not that they already have that within their background. And we don't pretend that all our data scientists know this already, but we do have people specialised in industries and if we work together with them, we can already bring in the right teams (source: Technology supplier—Accenture—Client Partner).
What’s really important is to train AI, like algorithms. By setting up a minimum viable product (MVP) and train an algorithm during sprints step by step you can increase its functionality. So we create a kind of a jumpstart and by exploring different use cases in parallel we show our competitive edge at the same time (source: Technology supplier—TCS—CTO).
Interviews consistently show that the agile approach is a good practice in the current emerging market maturity for AI.Start small initiatives, and focus on continuous improvement. What is important is to monitor the availability of capabilities closely from the start. The ability to scale up is an important success factor (source: Market research advisory firm—Gartner—AI SME).
4.2 Formal governance of AI outsourcing
4.2.1 Contract
Similar to software development, if a supplier includes their IP in AI, they grant a perpetual licence to protect their interest. Interestingly, suppliers also offer the opportunity to clients to co-develop AI services. In these circumstances, both the client and supplier draft specific contractual clauses on how to deal with IP-related topics.Essentially we are bounded by the contract that we agreed with. If we build an AI algorithm, then the client wants us to sign that we do not build the same algorithm for the other clients. So, IP is transferred to the client (source: Technology supplier—Accenture—AI SME).
Suppliers that are involved in co-IP development argue that contractual clauses are rapidly evolving. Our interviews show that standard T&Cs are used in AI-related contracts. Some suppliers argue that existing framework contracts are used to contract AI and therefore standard T&Cs are applied, but specific AI solution provisions are embedded in the underpinning AI solution Statements of Work.By co-creating IP, we are able to monetarise our client’s assets that affect their balance sheet positively. In case we co-develop IP with a client we may come to an agreement that this IP can be used in other client engagements. If that’s the case, the original client receives a royalty fee (source: Technology supplier—HCL—SVP).
Interestingly, none of the interviewees argued that decreased liability caps were applied in contracts for AI. Apparently, the suppliers do not recognise the additional risks of provisioning AI or have instead incorporated the additional risks into their fees.Typically, clauses are related to a responsible user, as we're building a product and leave it behind. The responsible user clauses are dealing with client obligations on using the product. This is not limited to adjusting the source code but also includes training the algorithm (source: Strategic advisor—BCG—MD & Partner 1).
4.2.2 Services—SLAs and KPIs
None of the interviewees has yet to experience penalty clauses for missed service levels in providing AI. Therefore, there are no observations related to KPIs, which are typically used in contracts, only to measure the service level but also to include a penalty for missing the agreed-upon service level.We see a shift towards xLAs (experience-driven service levels). Based on the insights that we gain during AI tests we can predict how AI will perform in practice. That means that we are willing to apply a service level driven by experience (source: Technology supplier—TCS—VP & Global Head).
4.2.3 Pricing mechanisms
Technology suppliers that offer ‘AI as a Service’ apply a consumption-driven model. Depending on the number of client users and types of AI solutions, the degree of consumption affects the price directly. We find that two technology suppliers offer outcome-based pricing agreements (e.g. HCL and TCS). After co-developing and implementing an AI solution, performance is measured for a certain period; based on the performance and learnings, an agreement is made for the desired outcome. These suppliers applied a bonus/malus mechanism based on the outcome provided.Considering our AI support the financial model is based on a fixed fee type of model. If the degree of uncertainty in a project is difficult to calculate, we use a time and materials model. We do not use or see risk and reward types of financial models in practice (source: Accounting and Consulting firm—KPMG—Senior Manager).
The interviews reveal that strategy advisors apply the identification of relevant use cases for their AI, which is a similar principle as for their other offerings—potential benefit / investment ratio.AI as an outcome-based model is part of our service portfolio. By collaborating with our clients in co-creating AI we are transparent in how AI works in daily life. In specifying a desired outcome, we agree on a bonus/malus principle. An example is the development of a proactive application maintenance solution, which we have based on AI. The focus is on cost reduction and improved quality of services (source: Technology supplier—HCL—SVP).
We will not touch anything which is less than 10X, and on average we typically pitch for opportunities between 50 to 100X, both in terms of benefit/investment ratio (source: Strategic advisor—McKinsey—Client Partner).
4.3 Relational governance of AI outsourcing
4.3.1 Trust
Trust is also related to ethics. All suppliers explicitly address this aspect in delivering AI. Continuously monitoring ethical compliance is perceived as important.Importantly, we developed criteria to assure the quality of our work. In case we develop AI for clients, we also apply our internal AI quality framework. We share and discuss this quality framework with our clients and provide full transparency as an extra guarantee to inform them proactively. Next, the client accepts our product and becomes responsible for the use of the AI solution, such as an algorithm (source: Accounting & Consulting firm—KPMG—AI SME).
In addition, we noticed that providing AI assurance, including ethical compliance, is emerging.Of course, everyone agrees that ethics is important, but how do you really implement it? Actually, when you look at certain elements that are relevant for ethics and fairness, and then testing your output among different groups that isn't rocket science. But you can also make it transparent. That will contribute to trust (source: Accounting & Consulting firm—EY—AI SME).
Going forward, clients might ask for, or even due to regulatory compliance might be in need of, an AI assurance certification (source: Market research advisory firm—HfS—SVP).Business leaders are increasingly very closely involved in ensuring ethical compliance (Technology supplier—TCS—VP & Global Head).
I think we are five to ten years away from people thinking about it systematically and proactively. However, in all of our engagements we explicitly integrate ethical compliance in our engagements (source: Strategic advisor—BCG—MD and Partner 2).
We have our set of ethical principles and a set of actionable policies. Any engagement team has to answer 10 yes/no questions to flag risks, potentially followed by a full assessment of 50 questions. We use this to assess the ethical risk profile. During the engagement, the high-risk cases are closely monitored by a committee. These senior subject matter experts are also guiding engagement teams (source: Strategic advisor—BCG—MD and Partner 1).
4.3.2 Commitment
We found no differences in the initiation of AI solution engagements across the different types of suppliers. All initiated AI solution engagements are with both existing clients and with new clients.AI may have a serious effect within a firm or towards their customers that may be impactful. Therefore, it is important to ensure that executive management is aware of the consequences. That’s why we focus on C-level commitment first (source: Strategic advisor—McKinsey—Client Partner).
4.3.3 Dependencies
Interviewees state that data quality (e.g. correctness, completeness, compliance) is essential and can be seen as the foundation for a profound AI solution. If an AI solution is functioning incorrectly or even not at all, data quality may cause a serious dependency. As data is primarily a clients’ responsibility, the reputation of the supplier may be at risk.By co-creating IP we are able to monetarize our client’s assets that affect their balance sheet positively. In case we co-develop IP with a client we may come to an agreement that this IP can be used in other client engagements. If that’s the case, the original client receives a royalty fee (source: Technology supplier—HCL—AI SME).
Compliance is also an important topic if proprietary software is used for providing the AI services.It does start with the data you know because AI will use available data in which the quality is key. Dependent on the client’s data quality, we select a relevant type of AI technology and then start to co-develop. Not just apply the same AI technology because you're used to it, but the client’s specific situation will influence the choice of a technology (source: Accounting & Consulting firm—EY—AI SME).
In order to ensure good data quality, data cleansing can also be added to the engagement.We are mailing using open source products. Therefore, we didn't have to go down to too many legal issues or other kind of compliance implications, rather than technical standardization (source: Accounting & Consulting firm—Atos—AI SME).
In turn, clients’ data quality inconsistencies may affect AI governance negatively.Ensuring the data quality at all levels is often a significant part of our engagements. The data needs to be of high quality to enable a long-term training. For instance, six to eighteen months’ time for algorithm training data sets is not unusual (source: Accounting & Consulting firm—EY—Client Partner).
We find that educating client staff in AI reduces the dependency and improves IS governance.Data scientists and data engineers are in high demand. We see a lot of emphasis on training, also as part of our engagements (source: Technology supplier—Accenture—AI SME).
4.3.4 Communication
I think we quite often make AI something that is really far away and is a kind of a black box. But actually, it's a human team that is behind it. And the more you communicate and get involved with the client team that in the end is using it, the more aware you become of AI functionalities (source: Technology supplier—Atos—Client Partner).
4.3.5 Psychological contract
We find that a psychological contract enables transparency with regard to the obligations between clients and suppliers, which supports governance of AI outsourcing.If my customers get worried that our algorithm is not providing the right results for them anymore, then all the attention will go to fixing it now, and then the question of who is responsible for that is no longer relevant anymore because we just need to fix it. We make it happen (source: Technology supplier—Atos—Client Partner).
4.3.6 Summary
Attributes/codes | Sub-codes | Events identified | Expert | Supplier |
---|---|---|---|---|
Client-supplier AI arrangement | Gradual approach (sprints) | 12 | 8,9,10,11,12,13,14,15 | Accenture, Atos, HCL, TCS |
AI Proof of Concept | 6 | 8, 10,11, 14,15 | Accenture, Atos, TCS | |
Multidisciplinaire teams | 7 | 9,11,13,14,15 | Accenture, Atos, HCL, TCS | |
Data strategy, platform and quality | 10 | 8,9,10,11,13,18,7,15 | Accenture, Atos, HCL, HfS, KPMG, TCS | |
Contract | Intellectual Property | 7 | 8,10,12,14,15 | Accenture, Atos, HCL, TCS |
Services | xLAs | 4 | 12,18,14 | HCL, HfS, TCS |
Pricing mechanisms | Fixed price model | 4 | 8,10,4,6 | Accenture, Atos, EY, KPMG |
Outcome based model | 3 | 12,18,14 | HCL, HfS, TCS | |
Consumption driven model | 3 | 12.14 | HCL, TCS | |
Risk/reward model | 3 | 8,10,3 | Accenture, Atos, McKinsey | |
Time&Material model | 4 | 8,10,6 | Accenture, Atos, KPMG | |
Trust | AI quality framework | 5 | 8,4,6,12 | Accenture, EY, KPMG, HCL |
Ethical compliance | 5 | 10,12,14 | Atos, HCL, TCS | |
AI assurance | 6 | 10,4,3,6 | Atos, EY, McKinsey, KPMG | |
Commitment | AI co-development with clients (projects) | 8 | 9,11,13,7,15 | Accenture, Atos, HCL, KPMG, TCS |
Executive involvement | 7 | 8,10,3,14,15 | Accenture, Atos, McKinsey, TCS | |
Dependency | Co-development of IP (client = owner) | 8 | 8,10,13,7,14 | Accenture, Atos, HCL, KPMG, TCS |
Education and training | 8 | 8,9,11,5,13,3 | Accenture, Atos, EY, HCL, McKinsey | |
Communication | Business involvement | 6 | 8,10,11,12,6,14 | Accenture, Atos, HCL, KPMG, TCS |
Psychological contract | Client relationship | 6 | 8,9,10,12,18,3 | Accenture, Atos, HCL, HfS, McKinsey |