AI and ML have become a part of many businesses and industries. With the increasing demand for AI/ML solutions, more and more companies are seeking to partner with development companies to develop and implement these complicated technologies.
Though it is necessary to find the right partner, making a poor investment can be very detrimental, as it can only waste your time and money and have substandard results.
In this article, we answer key questions and assess critical factors that a vendor’s AI/ML development services should satisfy to avoid making the wrong choice.
The goal is to provide business leaders, entrepreneurs, and technology decision makers with the knowledge to properly vet partners and choose ones that align with their financial, value, budget, and capability needs.
Specifying Project Goals and Requirements
Defining the scope of work and the actual requirements from the vendor is the first step. This includes:
Purpose and objectives
What is the business trying to solve with the AI/ML solution? How will it create value? What key metrics define success? This sets out what is expected from both parties.
Data requirements
What and how much do I need in terms of qualifications: data types, formats, sizes, sources, etc.? This affects the choice of algorithm and requirements on the infrastructure. Provide sample data to share for evaluation.
Functionality needs
Specify must-have features versus nice-to-haves. Prioritize critical functions.
Timelines
What are those proof of concept (POCs), prototype, pilot and production deployment deadlines? Be realistic about timing.
Team involvement
Will internal staff be assigned to the project? If so, specify the process and timeline. Define roles and responsibilities.
Budget estimate
Give a ballpark budget or cost constraints upfront so vendors can assess feasibility.
When vendors are asked to propose solutions on the basis of these requirements, they are able to understand the goals and propose appropriate solutions. Compare proposals to evaluate fit.
Vetting Technical Expertise
Once basic project alignment is established, technical competency has to be rigorously evaluated to succeed. Key questions to ask include:
- How have you built such AI/ML solutions before? Get 3-5 detailed use cases and results.
- What AI/ML techniques are you foreseeing using? Neural networks, NLP, computer vision, reinforcement learning, etc., are options, and you can seek evidence that they know what is the most suitable method for the project.
- How do you use such things as tools, libraries, etc.? Look for proven, state-of-the-art technologies.
- What is your model development and deployment process? Ensure it aligns with industry best practices.
- How do you evaluate model performance to minimize bias? These metrics and the bias mitigation procedures should be robust.
- How many do you have in engineering and data science full-time? More support comes with more resources.
- How do you leverage other experts? If you are using your application in specialized domains such as healthcare, you will need subject matter experts.
Request to see previous work that proves relevant experience. Discuss project specifics and test knowledge. It looks at breadth over all of AI/ML as well as depth in priority specialty areas that are on the critical path for success.
Evaluating Delivery Capability
Beyond technical skills, evaluating a vendor’s ability to deliver functioning solutions is key. Explore questions like:
- How long is your development process, and what process do you have? The requirement gathering, data preparation, prototyping, user testing, deployment, etc., are to be sought in a structured form.
- What methods are used to handle modifying requirements while on a project? It is advantageous to have an agile, adaptable process.
- How do you ensure model performance over time? Ongoing monitoring, maintenance, and model retraining should be discussed.
- What team will be assigned to our project? Meet key players to assess their experience.
- Where are your delivery centers located? Discuss time zone alignment and travel needs.
- Can we meet past clients? Speaking to references offers credibility.
- What post-deployment support do you provide? Ongoing maintenance, updates, and enhancements should be offered.
Vendors should showcase the capacity to take projects from conception to completion smoothly. Request examples like project plans, timelines, deliverables, and results. Assess for fit.
Evaluating Data Privacy and Security Posture
Data privacy and model transparency are very important for many companies today, especially for the regulated industries, such as financial services and healthcare. Key aspects to explore include:
- What data governance policies and access controls do you have? Strict protocols should protect sensitive data.
- Do you meet the regulations such as HIPAA and GDPR? Seek external validations as appropriate.
- Is data access by geography possible, or can we limit it? Global projects may have data residency restrictions.
- What information do you give about your model? They also bring transparency in the form of fact sheets covering intended use, data, performance, etc.
- How do you monitor models for bias and fairness? Explicit audits help identify issues.
- How is our IP protected? Ensure adequate protocols protect proprietary algorithms.
Vet security and regulatory compliance rigorously for personally identifiable, confidential, or restricted data. It also requires transparency into models, metrics and processes as well.
Assessing Cultural Fit and Communication
AI/ML projects involve complex problem-solving. However, strong collaboration between client and vendor is difficult when the partnership is across different cultures, languages and time zones. Areas to evaluate fit include:
- Is communication proactive and consistent? Language barriers can impede projects. Verify proficiency and clarity.
- Are your style and values aligned with your working style? Take cultural dimensions that must exist, such as power distance, uncertainty avoidance, etc.
- How do you conduct collaboration? Coordination is increased by such things as daily standups, status reports, project management platforms, etc.
- How flexible are you to changing priorities? Seek evidence of agility and adaptability.
- How do you resolve conflicts or mismatches? Clear escalation protocols indicate maturity.
Preferential, normative and the need for communication and work culture should be open for conversation. Assess firsthand, if you can, but visit the offices. Ensure alignment for productive teaming.
Validating Commercial Terms and Pricing
With technical qualifications met, the last step is closing the contract. Key considerations include:
- What pricing models do you offer? Options include fixed bid, time and materials, contingency fee, etc.
- Are the statements of work very detailed? Make sure about deliverables, timelines and their specifics on pricing.
- What are payment terms and invoicing cadence? Link payments to milestones achieved.
- Is source code escrow available? This provides access to code if a vendor goes out of business.
- What are included: service levels and warranties? Uptime guarantees and performance metrics provide reliability.
- What insurance coverage is carried? Get sufficient professional liability, errors and omissions, and cyber liability policies.
- Who owns the IP generated? Agree to commercially negotiate rights for using its algorithms, models, and tools.
Low cost doesn’t mean high value – know the full TCO consequences. Engage legal counsel to review contracts to reduce liability, IP, indemnities, termination rights, etc.
Moving Forward with an Aligned Partnership
In choosing an AI/ML solutions partner, you synthesize findings across all evaluation dimensions, from the ability to understand to fit the culture and negotiate contracts. The best strategic alignment, technical excellence, delivery track record, transparency, communication, and cost-effectiveness help move forward with the vendor.
The more complex the project, the more time spent doing thorough due diligence upfront is well spent. An informed decision is defined by defining key parameters, asking probing questions, reviewing past work, and validating responses. This is how it leads to the shared vision, complementary skills and trust needed to drive AI/ML impact technically, operationally, and economically.
The next frontier of business value is AI/ML applied intelligently and ethically. The guide for picking the right partner is a blueprint and a guide to leaders starting to realize this potential today for the future opportunities of tomorrow.
Conclusion
With AI/ML embedded in business success, it is a strategic imperative to hire the right development partner. This 3500-word guide discusses:
- Defining project requirements and expectations upfront
- Evaluating technical expertise across AI/ML capabilities
- Assessing the ability to deliver functioning solutions
- Validating data privacy, security, and transparency
- Determining cultural alignment and communication fit
- Negotiating favorable commercial terms and pricing
One can ask probing questions, thoroughly review capabilities and validate by references to make an informed choice. By choosing the right partner, competencies are aligned to needs for maximized value. Today’s diligence with AI/ML is advancing exponentially, so with good diligence today, we can thrive tomorrow.
