Are you worried that outsourcing your next artificial intelligence (AI) development may be a costly mistake?
Maybe your previous outsourcing experience led to wasted resources and nearly derailed your business. Or perhaps you’ve heard cautionary stories of outsourcing disasters that gave you second thoughts.
The good news is outsourcing remains an effective way of cutting costs, buying back time, and gaining a competitive edge, especially in this fast-growing AI era. As AI adoption grows, businesses continue to reach out to expert AI development partners to scale their projects and keep up with technological advancements.
However, outsourcing could wreak more havoc than expected without the right strategies in place. In this article, we’ll share proven strategies that helped businesses like Sage, nZero, and Acxiom outsource successfully without wasting money.
How to Outsource AI Development Successfully
-
Assess Your Data Readiness
Before starting any AI project, make sure your company’s data is actually ready to use. Many businesses jump into AI development only to discover their data is scattered across systems, inconsistent, or simply insufficient. At minimum, you need organized data storage like data warehouses, working ETL pipelines to extract and transform your data, proper orchestration systems, and enough computing power to handle AI workloads. You also need clear governance policies covering the data lifecycle, access control, ownership, sources of truth, and quality standards.
Skipping this assessment can severely impair both the development of your AI solution and your ability to use your data resources effectively. Companies that address these fundamental data issues before proceeding with AI projects typically see much higher success rates and lower overall costs. While this preparation might add time at the beginning, it prevents much longer and costlier delays later. Getting your data infrastructure in order first might be the difference between an AI project that delivers real value and one that never gets off the ground.
-
Set Your Project Scope & Quality Standards
Before deciding which AI solution companies to choose, you must identify which aspects of the AI project need to be outsourced to external professionals. This is because AI processes can be diversified into areas, such as:
- Data Infrastructure and orchestration,
- Machine learning,
- Robotic process automation,
- Data processing,
- Computer visualization,
- Strategic decision-making,
- Custom model development, and
- the full AI operations management.
So, you should ask yourself which of those specifications are needed. It may even be an AI innovation audit for your business, where AI companies like GAP review your processes to determine where automation and innovation could be impactful.
Once you’ve defined the aspect of AI expertise your internal tech team lacks expertise in, design your project scope. When setting your project scope, consider specific problems the project addresses, performance metrics to track, features, AI technology required, deliverables, quality standards required, specific compliance, delivery time, and stakeholders’ involvement notes.
Here’s an example of the AI project scope for Acxiom, one of GAP’s clients.
Profile & Challenge
When it comes to prospect and customer intelligence, data is everything. Acxiom — a global leader in customer intelligence — analyzes customer and business information used for targeted advertising campaigns. Their data catalog, which contains more than 500 million active consumers worldwide and about 1,500 data points per person, focuses on consumer insights to help organizations understand, predict and reach the customers they want. With more than 12,000 global attributes for audience prospecting and segmentation, Acxiom gives clients the power to simplify the process of identifying ideal prospects, retaining customers and growing loyal members for their business.
Now, there’s a demand for customer intelligence via AI for better marketing performance. An example of this is the Acxiom Health solution, the latest evolution in curated, data-driven healthcare and pharmaceutical audiences.
Read More: Acxiom Success Story
Setting a clear project scope and internal standards helps avoid scope creep and allows the external team to stay aligned with your expectations.
-
Research & Choose a Reliable AI Development Vendor
Having specified your need, you’re a few steps away from meeting the right AI consulting partner. Rather than seeking AI development vendors who are generalists, search for those with expertise in your specific needs—this could be data analysis, machine learning, chatbot development, and others. You can get the best option by asking friends for referrals, checking AI partner communities, and using the internet, whether Google, Facebook, LinkedIn, etc.
However, before you contact any artificial intelligence development companies, conduct thorough background checks that satisfy your curiosity. Start with their website and review their portfolio for customers’ stories similar to your project as closely as possible.
Review their industry knowledge and technical team expertise across verticals such as data science, AI engineering, machine learning, data processing, software development, etc.
If you feel satisfied with their expertise but can’t find case studies on their website, you may contact the support team or a representative and make a request.
-
Communicate Your Expectations
According to Rand’s research, communication issues remain one of the major underlying causes of AI project failures. Companies like nZero and Sage, which have successful outsourcing stories, understood this principle and ensured full transparency with external vendors.
Share your organizational visions and objectives with your vendor without missing details. While stating performance metrics for AI projects can be elusive, ensure you set technical model metrics that align with your business KPIs. Metrics could be error rate, accuracy, precision, time to market, customer satisfaction, adoption rate, and return on investment.
At GAP, we have an internal onboarding framework that supports us in aligning our technical processes with business expectations. We expect businesses to brief us on their business KPIs. Nevertheless, we still ask the right questions to ensure a seamless and results-driven collaboration. Below is an example of how we build client profiles.
Profile & Challenge
NZero aims to help organizations achieve sustainability and carbon neutrality by providing consultation services and real-time, accurate data on carbon emissions and energy consumption. Their platform offers comprehensive tracking and analysis tools that enable businesses to reduce emissions, improve energy efficiency, and progress toward net-zero carbon goals.
The company offers a comprehensive decarbonization platform for sustainability leaders in government and business that seek to measure, analyze, report and act on sustainability initiatives across. NZeroOS is able to track carbon emissions via automated data collection and analysis, advanced building modeling, and the use of machine learning technologies to drive climate action. NZeroOS utilizes detailed datasets from energy providers and public sources to feed machine learning models, which in turn generate predictions, simulations and tailored recommendations. The next step in this innovation is to use data processing to support AI models updates
Read More: nZero Success Story
Ensure that communication is timely, accurate, and open. Also, build a transparency structure to ensure you know any unanticipated problems early on, allowing each party to take immediate action. For continuous transparency as the project proceeds, set up communication channels such as Slack, Jira, Trello, or Asana to track tasks and timelines, be clear with roles and responsibilities, declare project leadership, and provide feedback sessions with clear documentation.
A clear expectation prevents you from crises such as
- misaligned cultural fitness and business goals,
- time zone differences,
- missed deadlines,
- poor quality delivery,
- damaged relationships, and
- scope creep.
-
Set a Realistic Budget
Since AI solutions in businesses are at an early adoption stage, many decision-makers struggle with setting the right budget. While there’s no fixed price, AI project pricing can range from $10,000 to $200,000, depending on the project’s complexity and the service provider. AI can be affordable and costly, but clear communication about your project details can help avoid overspending.
Understanding your vendor’s service model is one of the first things to consider when setting a budget. Are they offering AI consulting and managed services, or are they augmenting your IT staff? Also, consider your project scope specifications, such as the model complexities, model type, data requirement, configuration, maintenance costs, and other expectations.
Below is the breakdown of AI development rates by Clutch.
- Hourly AI Development Rates: Most AI development companies listed on Clutch charge between $24–$49/hour for their expertise.
- The Cost of AI Development Based on the Service:
- Chatbots: $25–$49/hour
- Cognitive Computing: $25–$49/hour
- Machine Learning: $25–$49/hour
- Natural Language Processing (NLP): $25–$49/hour
- Robotics: $25–$49/hour
Source: Clutch
Based on the analysis of AI development companies on Clutch, common factors that impact the cost of hiring include
- Project complexity & scope
- Customization
- Number of employees
- Technology
- Timeline constraints
- Location
Although outsourcing AI development could be more cost-effective than in-house methods, you must consider these key factors for realistic budgeting.
-
Sign a Contract
Legalizing every agreed-upon detail is the next phase once you’ve chosen an AI outsourcing company. This is where you seal the deal to legally outline expectations, responsibilities, deliverables, timelines, and payment terms between the parties involved.
Signing a contract goes beyond formal documentation. It protects your intellectual properties with a nondisclosure agreement in both parties’ interests and minimizes risks. With it, you can specify what happens during delays, non-delivery, or contract breaches. Likewise, both parties can agree on a dispute resolution arrangement to avoid costly legal fights.
Regardless of your level of trust in any external vendor, we do not recommend ignoring the need for a contractual agreement.
-
Plan for Long-Term Engagement
As mentioned earlier, AI adoption is at its earliest stage, indicating that businesses may need technical support even after delivering the AI project. Therefore, think long-term and plan for it right from the interview process. Discuss post-launch support and maintenance upfront. Also, develop a roadmap for future enhancements and updates. Highlight key areas that are likely to need enhancements, such as model performance, data, user experience, security, and scalability.
This allows you to prepare financially while knowing you will have full assistance in case of a bug fix or other issues. Similarly, it ensures that issues are resolved promptly and efficiently.
-
Give Importance to Cybersecurity
Right from the project planning, ingrain security and privacy concerns into your priority checklist. Evaluate their security measures in handling projects with sensitive data. Ensure they have robust data protection practices, including compliance with relevant regulations like GDPR and industry-specific ones.
Depending on the sensitivity of your data and the specific needs your AI project addresses, critical infrastructure decisions must be made very early in the project’s lifecycle. Specifically, you’ll need to decide whether to host models locally (either on-premises or as account-bound resources within a cloud environment) versus using vendor-hosted solutions. This hosting decision significantly impacts your security posture, compliance capabilities, and overall control of your data and AI assets.
Ensure the vendor’s security procedures align with your existing cybersecurity strategy. Develop an AI security strategy and governance framework to avoid AI model poisoning and other backdoor attacks. Evaluate their ethical AI usage concerns. You should also understand their privacy-preserving techniques for AI model training. Popular methods include federated learning, strong multi-factor authentication, data masking, homomorphic encryption (HE), differential privacy, and secure multiparty computation (SMPC).
Your security strategy can make or mar your entire AI development process. Therefore, collaborating with AI development outsourcing companies with extensive technical experience that extends beyond your immediate AI requirements is vital. GAP’s software and data engineering services assure security and compliance while enabling innovation with emerging technologies.
-
Trust Your Gut
Each step highlighted above is a strategic effort to boost your confidence and trust in the AI expert. However, If you don’t feel comfortable partnering with them at the end of the exercise, consider another option on your list.
The goal is to build a partnership that aligns with your business goal, and you deserve one with mutual understanding and collaborative efforts. Do you feel a strong connection with the AI vendor? What’s your perception of their expertise, collaboration, passion, and effort in your project? Listening to your instinct is part of the due process that you shouldn’t ignore. Ask relevant questions as follow-up until your curiosity is satisfied.
Ready to Collaborate With a Strategic AI Development Partner?
If you need to outsource your AI development, think GAP.
We are not just experts in machine learning & AI consulting services; we are business partners who support you with the technical expertise required for business innovations. Our expertise in AI services has supported businesses across different industries such as restaurants, hospitality, real estate, construction, health care, consulting, media, non–profit, software, education, and logistics design innovative solutions.
We design AI/ML algorithms and generate systems that give your products and business a competitive advantage. Our technical support includes
- Data engineering
- Data science
- Machine learning and AI
- Large language models
- Business intelligence
- Data as a product
- Data quality engineering
- IoT & connected devices
- And more.
Outsourcing can be cost-effective when done with a strategic partner. Talk to us today, and let’s kickstart your AI project.
