In several short years, artificial intelligence (AI) moved from the research labs to mainstream usage. With the rapid advancement in technology capabilities and commoditized access to model development, AI adoption is no longer a yes or no decision — it's more of a how. And this appears to be a major stumbling block.

Over 60% of CIOs feel they're expected to know more about AI than they currently do, according to a Salesforce survey. Three of five CIOs also think stakeholder expectations for their AI expertise are unrealistic. At the same time, most businesses are also struggling to start or scale AI adoption due to skills shortages, data complexity, and ethical concerns.

To overcome these barriers, many organizations are turning to artificial intelligence outsourcing partnerships – an approach that offers greater predictability and confidence with ML and AI deployment.

Why Outsource AI/ML Development?

Since 2015, the global demand for AI skills has increased globally by 9 times, according to a Citi report. With high demand and limited supply, hiring times are prolonged. The average time-to-fill for AI-related roles is 55 days, according to the same report. Although some vacancies remain unfilled for up to 84 days. With prolonged hiring and small teams, project timelines constantly get delayed.

AI development is a complex, interactive process, involving multiple model training and validation rounds. Several industry and scientific surveys suggest that only 10% to 20% of developed AI/ML models are successfully deployed to production i.e., made available to the end users.

Although, that doesn’t mean each productized AI model becomes a value driver. We’ve already seen a slew of AI mishaps. After 3 years in the making, McDonalds shelved its AI-powered drive-through ordering due to poor customer feedback. Zillow also had to cut back on algorithmic home valuations after losing $381 million to errors in price predictions.

For many businesses, AI development remains unchartered territory due to limited experience with effective data management, algorithm selection, and validation, plus model explainability. By partnering with an AI and machine learning development services vendor, you gain access to:

Missing AI skills and competencies

Tested technology frameworks

Lean model development processes (MLOps)

Proven methods for model validation and explainability

Model observability and security

Speed and efficiency in project delivery

More predictable path to ROI

Given the benefits, it’s not surprising that many leaders are choosing this route. AstraZeneca just signed a deal with a Canadian-based AI development company to accelerate drug discovery. Logitech partnered with Svitla Systems to develop an enhanced video conferencing firmware for video processing with neural networks and computer vision.

By seamlessly integrating into your team, AI and machine learning development partners allow businesses to remain competitive in the era of rapid technological advancements without the overhead of building massive internal engineering departments.

Challenges of ML and AI Development Outsourcing

Software development outsourcing in general is a complex process, as it requires a strong alignment between the partners, proactive expectation management, and knowledge exchange. With these elements in place, co-sourcing alliances have a high chance of success.

When it comes to ML and AI outsourcing partners should aim to foster an effective service delivery framework that accounts for the following challenges:

Knowledge Transfer

The main cause of AI project failures is “misunderstanding or miscommunication among stakeholders about the problems needed to be solved using AI”, a research report by RAND found.

ML and AI have a number of proven use cases in healthcare, finance, and travel industries among others – and even more yet untapped opportunities. However, the technology’s flexibility is a double-edged sword: Amidst the myriad options, it may be hard to figure out the optimal use case and then communicate it to an external team.

To formalize and communicate your vision effectively, think of the specific business or user problems you want to solve first. Think in terms of specific outcomes. Let’s say you want to improve medical code billing with AI. List specific processes and use cases the new system should enable e.g., automated code assignment, claim error detection, and revenue forecasting. Decide on the success criteria of such features e.g., 99% automatic code assignment for all standard queries; mean claim error rate of less than 5%, etc. Then work with your partner on determining the feasibility of each use case and defining the optimal AI approach (e.g., supervised machine learning vs deep learning vs generative AI).

Typically, an AI/machine learning outsourcing vendor will guide you through this stage during the discovery — a structured, pre-engagement process, designed to clarify project requirements and develop a roadmap for subsequent machine learning app development.

Data Privacy Concerns

Data is the backbone of AI and ML model development. Yet, it’s also in short supply either due to regulatory limitations or internal concerns over sharing corporate insights with a third-party vendor.

The optimal approach to minimizing regulatory and security risks is to apply appropriate data minimization and anonymization techniques to all model training data shared with other parties. Common privacy-preserving techniques for AI model training include data masking, homomorphic encryption (HE), differential privacy, secure multiparty computation (SMPC), and federated learning.

Another approach is to train AI models on synthetic data — computer-generated mock data that closely mimics real-world data structures. Synthetic data sets eliminate any privacy risks without compromising algorithm efficiency. In fact, scientists have found that synthetic datasets help build better models in industries like healthcare or finance, where original data is in limited supply.

Integration Issues

Potential incompatibility with existing systems is another major concern over externalizing machine learning software development. Indeed, integration problems can emerge when the vendor is given limited context about your existing technology portfolio.

Again, such issues can be mitigated through software requirements specification at the early stage of the project. By providing your partner with comprehensive details about your IT landscape, you can determine the necessary integration requirements at the model design stage. An experienced partner will also advise you on the necessary adjustments to cloud infrastructure or data management processes to ensure a smoother roll-out and effective model performance in production.

Moreover, many AI solutions can be served as serverless application programming interfaces (APIs) using cloud services providers. This approach abstracts the complexities of infrastructure management. Instead of worrying about resource provisioning, scaling, and maintenance, your teams can focus on further model interaction and fine-tuning.

How to Ensure Successful Cooperation With an AI Development Services Provider

Assuming you’ve already found the right artificial intelligence outsourcing vendor, here’s how to structure the collaboration process for maximum efficiency.

1. Focus on Building Trust

Trust is the bedrock of any successful partnership, but it’s not something that comes as a “default”.

Research shows that trust among teams is based on a combination of different surface-level cues (initial impressions and certain biases), emotional reactions (e.g., feelings of disappointment due to unmet expectations), and deeper-level schemas and cues (observations, developed over time). The latter are the most important components as they’re used to assess the three most important dimensions of trustworthiness: ability, benevolence, and integrity.

There are several ways to speed up the process of building trust between internal stakeholders and external IT outsourcing services providers.

Define the key objectives. Establish the main project milestones, deadlines, and success criteria. Having clarity and alignment in vision reduces misunderstanding, which reduces trust. Additionally, it facilitates the development of a shared belief that others are pursuing the same goal with good intentions.
Establish two-way feedback mechanisms to collect feedback from internal stakeholders and your vendor. When problems arise, tackle them head-on. Open discussions about challenges demonstrate a willingness to work together to find solutions, cultivating trust.
Focus on the outcomes, not approaches. Some companies fall into the trap of focusing too much on how the service should be delivered, rather than the outcomes. However, by being too prescriptive with your recommendations, you risk missing out on the extra value your partner can bring (e.g., a more agile process, new insights for technology roadmap planning, etc.). Consider adapting to the provider's model and best practices to capture more value from the partnership.

Remember: You already have many agreed-upon processes and milestones formalized in legal documents like master service agreement (MSA) and scope of work (SoW). Hence, your goal is to give them a “headspace” to show their best abilities and effectively deliver on the said work without much friction.

2. Foster a Collaborative Partnership

Successful IT outsourcing partnerships are no longer transactional (i.e., driven solely by cost reduction), but rather outcome-oriented. Businesses are increasingly turning to

IT managed services providers to increase the pace of digital transformations (62%) and gain access to new capabilities (56%), according to Deloitte.

Such collaborative models provide access to the missing competencies and the ability to unlock new revenue streams through co-innovation. Yet, the collaborative nature of IT partnership also calls for a greater integration in the value chain. If your partner doesn’t understand the “bigger picture” – your business direction and key priorities – they won’t be able to align well with your strategy.

Sharing regular updates on your goals and being candid about more complex strategic decisions, help your partner better understand their role in the outcome and adjust their performance accordingly.

3. Establish Effective Knowledge Sharing Processes

A certain communication gap is inevitable in IT outsourcing. Your vendor will lack the institutional knowledge of in-house teams and will need time to understand your IT landscape, existing processes, and communication preferences. Your goal, however, is to proactively bridge the chasm in communication rather than allow it to widen further due to limited knowledge sharing.

To ensure effective knowledge transfer processes:

Create shared repositories. When you outsource machine learning and AI model development, effective access to data is key. Provide access to pre-approved datasets, set up shared code repositories and model registers to keep track of ongoing experiments and ensure proper version control.
Maintain clear documentation. Detailed records are essential for effective experiment recreation and reverse-engineering issues with model performance. Encourage everyone on the team to keep up-to-date notebooks and create shared technical documentation.
Schedule regular collaborative sessions like sprint reviews and project retrospectives, where everyone can candidly discuss successes, collaborate on solutions to recent mishaps, and get in sync on the next steps.

Beyond that, incentivize knowledge sharing through less formal initiatives — Slack discussion channels, group workshops, and shared corporate Wiki pages. Doing so helps enhance alignment, collaboration, and trust between distributed teams and drive more innovative thinking.

4. Select the Right Metrics to Measure Vendor Performance

As mentioned already, the ROI of AI investments can be elusive, unless backed by the right measures. Our recommendation is to track both technical model metrics and business KPIs.

Technical AI/ML model metrics like ‘accuracy’, ‘recall’, or ‘error rate’ can be directly influenced by your AI development partner, thus creating good measures for mutual accountability. On the other hand, business metrics like ‘adoption rates’, ‘productivity gains’, or ‘percentage of workflow automation’ are in the shared zone of responsibility. Your partner can influence them, but also require certain efforts on your side (e.g., staff upskilling to accelerate adoption).

Still, tracking and sharing these metrics with your partner is important as it provides them with extra valuable context for advisory. For example, if the adoption rates stall at some point, the vendor can leverage user feedback to create new user stories and prioritize improvements in the product’s UI or functionality.

Conclusion

First movers have a market advantage. A Bain survey found that 87% of companies are already developing, piloting, or deploying generative AI in some capacity. Done right, a strategic partnership with an artificial intelligence outsourcing partner like Svitla Systems can help you accelerate time to market for productized AI/ML models.

By combining domain expertise with engineering process excellence, we helped global businesses launch AI-powered products for healthcare, recruitment, hospitality, and sports entertainment.

Guide to AI Development Outsourcing

Why Outsource AI/ML Development?