Author
Wilan Wong
Engaging consulting services on an AI/ML project brings several advantages, especially in terms of expertise, speed, and cost-effectiveness. Consultants provide specialised skills that are often hard to build in-house quickly, including the latest in AI algorithms, data engineering, and learnings from implementing industry-specific use cases. They can help accelerate project timelines, avoiding pitfalls or common mistakes. When attempting something new, they can bring fresh perspectives and proven methodologies that are more out-of-the-box thinking than your comfort zone of solutions. It can, however, be hard to choose the right services that suit your company. This is where a scorecard can help.
Factors to consider when choosing an AI/ML consulting partner
Selecting a consulting vendor to enable and augment your strategic goals with AI requires a nuanced approach. The complexities of this field demands a vendor with specific expertise and capabilities. Here are some criteria that should be considered when assessing and choosing a consulting partner:
Criteria | What to Assess | How to Rate |
1. Domain Expertise | When evaluating potential vendors, ensure they possess specialised expertise in AI, ML, and data analytics relevant to your industry. Vendors with a proven track record in your sector will understand the specific challenges and opportunities associated with your data.
For instance, a vendor experienced in retail data analytics will be well-versed in understanding what data is required to fulfil retail use cases such as dynamic pricing, demand forecasting, inventory optimisation. They would be able to see the trade-offs and potential pitfalls of different models and approaches early, significantly increasing the likelihood of a successful model deployment into production - which still remains a significant barrier for most companies. | 1-2: Little to no AI or industry experience 3-4: Can be strong in one or the other and has relevant delivery experience 5: Strong in both, with proven delivery |
2. Innovation | AI and ML projects often require innovative approaches to problem-solving. It’s important to bring creative solutions to the table, leveraging the latest in algorithms and methodologies, not just routine implementations. They will be proactive when a problem presents itself, coming up with solutions based on experience. And most importantly, they are not afraid to challenge the status quo when it makes sense. You’ve called in the experts, let them guide you on both the new and what needs to be changed. | 1-2: Problems and solutions derived from existing landscape, not challenged 3-4: Problems defined collaboratively, alternatives brought to the table 5: Experience based, drives the problem definition to challenge understanding and considers best practices pragmatically for solutions |
3. Technical Expertise | AI and ML projects require a high level of technical proficiency. Knowing how to design and implement the most appropriate solutions for the business problems is crucial to not get carried away with hype, boil the ocean or create solutions that would not be maintainable. This requires staying on top of the latest technology to place what tools to use for the most appropriate job. It is also important to translate the technical skills into business language and be able to demonstrate how the technical decisions drive value. The easier it's understood, the easier it's adopted. And finally, proposed solutions should balance both the speed to the specific project while being strategic in terms of the enterprise architecture so decisions are not tactical and potentially aligned to future direction. | 1-2: Little consideration to relevance of tech, stays in comfort zone of what they know and little translation of how it works 3-4: Is able to define rationale technical decisions and translate effectively to business, may not be strategic in nature 5: Stays on top of latest tech, proposes fit for purpose solutions, easy to understand, and makes strategic decisions |
4. Test and Learn Culture | A test-and-learn culture is essential for a consulting company delivering AI services as it fosters innovation, adaptability, and continuous improvement. In AI projects, there are often unknowns and variables that can't be fully anticipated upfront, so iterative test and learn helps refine models, optimise algorithms, and adjust strategies based on real-world outcomes. This culture encourages business experimentation with trials to collect data, allowing teams to quickly identify what works and what doesn’t, and it empowers them to deliver solutions that are both effective and tailored to requirements. | 1-2: Little to no consideration for how to trial proposed solutions 3-4: Can design, conduct and manage trials for proposed solutions 5: Considers multiple simulations and how to trial before landing on solution, leverages a business experimentation framework |
5. Project Delivery and Change Management | Effective project management is essential, especially in complex AI and data projects. Assess the vendor’s ability to manage projects from inception to completion, ensuring timely and on-budget delivery. They should have robust methodologies for managing data-centric projects, including clear milestones, risk management strategies, and effective communication channels to keep all stakeholders informed and aligned. For AI/ML projects, the vendor should have experience and capability to assist with change management. As generally these projects disrupt, modify or augment existing business processes - there needs to be effective change management in place for these new insights and improvements to make their way through these processes. In managing the project, the vendor needs to be able to sometimes navigate and prioritise competing needs from different business units to seek opportunities where there is mutual benefit (where possible). | 1-2: May have some rigid project management frameworks/templates, no change management considerations 3-4: Adaptable end-to-end delivery frameworks, including change management planning 5: Adaptable delivery framework that caters for change, communication strategies and managing competing priorities in the business. |
6. Transparency and Integrity | Transparency about methodologies, data handling practices, and pricing is vital. Choose a vendor that provides clear documentation of their processes and is upfront about potential challenges and costs. Integrity in how they manage your data, including adherence to privacy regulations and ethical standards, is critical to maintaining trust and avoiding potential issues. | 1-2: Little to no visibility of pricing, security practices and product documentation 3-4: Well documented practices, pricing easily accessible 5: Certified data and security standards, clear and well understood documentation and pricing |
7. Cultural Fit and Collaboration | Cultural fit is important for ensuring smooth collaboration, especially in projects involving AI and data where iterative testing among many teams and adjustments are common. There will need to be continuous engagement between teams to deliver the most effective outcome. This can involve engaging with teams outside yours to get the quickest and most effective outcome, especially if it is for requirements outside the remit of your team.
This means that the vendor needs to work well with other internal teams, understanding your company’s culture and workflow. This good cultural fit facilitates better communication, faster problem resolution, and a more effective partnership. | 1-2: Work within themselves, little interactivity with immediate team, no broader engagement 3-4: Works well with your team, communicates and shares knowledge openly 5: Easy to approach and works well with broader teams to get best organisational outcomes |
8. Cost and Value | While project cost is an important factor, the value delivered and being proposed by the vendor should be the primary consideration. Evaluate the potential return on investment by considering the long-term benefits of the AI/ML solutions and data strategies they propose. Remember, an initially lower-cost option may result in higher overall costs if it leads to suboptimal solutions. Think about the on-going costs associated with the solution. Vendors must take every effort through the design and development phases to keep the productionised operational costs to a minimum. | 1-2: No benefits defined up front, no evaluation strategies, little consideration to minimising ongoing 3-4: Defined benefits, with assessment criteria, considers but may not optimise ongoing 5: Clearly defined and agreed benefits realisation strategy, including operational costs and long term financial and non-financial value |
9. Strategic and Tactical Balance - Managing Competing Priorities | AI and data projects often require balancing immediate tactical needs with long-term strategic goals. This balance needs to be considered carefully, as generally there is a tendency to overemphasise on the importance of short-term goals as they appear to be more alarming due to the “Mere Urgency Effect”.
The right consulting vendor should help you navigate this balance by offering solutions that meet your immediate needs while laying the foundation for future growth where possible. In proposing this balance, there should be activities to ensure and confirm the actual priority of time sensitive tasks to ensure that they are critical for them to be prioritised over high value medium-term strategic initiatives. Thus the vendor should provide strategies that address both short-term objectives and long-term scalability. | 1-2: Mainly tactically driven. Little consideration to strategic objectives. 3-4: Considers broader objectives, can balance with immediate need with overall goal 5: Understands and aligns planning to your business’ strategic goals, is able to help define drawbacks of tactical and communicate this clearly to all stakeholders |
What jahan.ai offers
jahan.ai is a product and consulting services firm that specialises in building AI/ML solutions for the retail and manufacturing industry. With deep-rooted expertise and a unique approach, jahan.ai helps businesses navigate complex challenges, driving innovation and delivering tangible results. We have a team of specialists that combine real-world experience, industry insights and an unwavering commitment to quality, making us the go-to-partner for organisations looking to thrive in today’s competitive retail landscape.
What we offer
At jahan.ai, our services are designed to address the unique needs of retail companies. Here’s what sets us apart:
- Strong Domain Expertise in Retail: Our extensive experience in the retail sector allows us to understand the challenges and opportunities businesses face. We’ve successfully tackled these issues for large-scale companies and can do the same for you.
- Proven Solutions Across Companies: We don’t just solve problems—we create scalable solutions that work across different organisations. This ability to adapt proven strategies accelerates our consulting solution delivery, helping clients achieve success faster and cost efficiently.
- Innovative, Results-Driven Culture: Continuous innovation is at the heart of our culture. We stay ahead of the curve by keeping up with the latest trends and separating the hype from reality. Our laser focus is always on delivering measurable outcomes for our clients, ensuring that every project has a tangible impact on their business.
- Commitment to Quality: Being a nimble startup, we ensure that each jahan.ai member brings a strong and diverse set of experiences and expertise, ensuring high-quality results every time.
- Secure Platforms and Data Compliance: In today’s digital world, security and compliance are paramount. At jahan.ai we have built a secure ISO27001 and SOC2 compliant data and AI platform. We can utilise this same knowledge regarding processes and technical architecture to keep your data safe. We prioritise security at every step, ensuring your operations continuously meet industry standards and regulations.
Why jahan.ai is Uniquely Positioned
What truly sets jahan.ai apart is our ability to bring product-building expertise into consulting. This holistic approach enables us to view client challenges from all angles and craft solutions that are both innovative and practical.
- Product Expertise for Holistic Solutions: Our deep product knowledge gives us a unique advantage. We understand the full lifecycle of a product, from concept to execution, and this insight allows us to offer well-rounded solutions that align with your broader business goals.
- Tailored Solutions: While some firms might offer one-size-fits-all answers, we pride ourselves on creating custom solutions when they make sense for your specific needs. This personalised approach ensures that our strategies are not just effective, but also aligned with your long-term objectives.
Contact us on info@jahan.ai to learn more about our consulting services offering to see how we can help, or hop onto our services page in our website at https://jahan.ai/services
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At jahan.ai, we build end-to-end AI twins for retail and supply chain businesses. We take the concept of a digital twin, a virtual replica of business processes, further with advanced AI, which not only mirrors these processes but also automatically optimises them. In this way, we support businesses in driving efficiency, going beyond their potential.