4 Key Strategies for Turning AI Vendor Changes or Failed AI Projects into Opportunities
In the fast-paced world of artificial intelligence, the journey to success is often seen as a path paved with failures. But what if the stumbling block isn’t an internal hiccup, but an external force? What if the very foundations of our organisations are shaken by the changes or failures of third-party vendors, to the point of dissolution? In this dynamic realm, where constant evolution is the norm, setbacks like AI vendor changes or sudden dissolutions are not uncommon. As organisations find themselves at a crossroads, the true measure of resilience lies in the ability to turn these external challenges into strategic opportunities.
Faced with such disruptions, a natural question may arise: “Should I stop working with external vendors altogether?” While this is a valid consideration, it is equally essential to explore strategies that cannot only mitigate the impact of these challenges but also transform them into avenues for success.
1. Embrace a Culture of Continuous Improvement
Foster a culture of continuous improvement in the face of AI setbacks. Extract valuable insights from encountered challenges, learning from failures to drive ongoing innovation within your organisation. Thrive amidst the complexities of the AI landscape by swiftly adapting to changing circumstances. Real-world examples demonstrate how setbacks can become launching pads for success through agile methodologies.
2. Evaluate Support Models for Sustainable AI Solutions
Re-evaluate your support models when confronted with AI setbacks. Conduct a comprehensive assessment to compare the advantages of in-house versus vendor support for your AI solutions. Consider factors such as control, customisation, expertise, flexibility, responsiveness, and risk management. Align the distinct advantages of each support model with your organisation’s specific needs for sustainable AI solutions contributing to long-term success.
| Criteria | In-House Support | Vendor Support |
|---|---|---|
| Control & Customisation | Greater control and adaptability, enabling organisations to implement changes promptly | Relies on vendor expertise and customisation options |
| Costs | Higher initial costs for recruiting, training, and maintaining an in-house team | Lower initial costs but requires ongoing operational expenses |
| Expertise | Relies on internal expertise and might require specialised training for new project needs | Access to vendor’s specialised skills |
| Resources & Scalability | Require continuous enhancement in resource allocation to align with organisation’s goals & unique need, may face resource constraints, making it challenging to scale operations quickly | Access to vendor’s resource, might need more than one vendor to fully align with organisation’s goals and unique needs for different lines of business; scalability varies among vendors and may need to replace vendor after the contract term ends or due to sudden changes. |
| Flexibility | Flexible customisation based on organisational needs but might be time & cost consuming | Limited flexibility based on vendor offerings |
| Responsiveness | Immediate response and issue resolution | Depending on vendor responsiveness, may face delays |
| Risk Management | Direct control over security and data handling, lack of expertise may require longer resolve time | Vendor’s responsibility, less infrastructure on-site to be handled, but might have potential security concerns |
This comparative analysis provides organisations with a nuanced understanding of the pros and cons associated with both in-house and vendor support models. It emphasizes the importance of aligning these considerations with the organisation’s specific goals, operational requirements, and long-term scalability needs. While in-house support offers greater control and customisation, it comes with higher costs, resources allocation challenges and lack of expertise. The recent findings from McKinsey in 2022 and 2023 suggest that hiring for AI-related roles remains a challenge which could potentially delay your projects. On the other hand, vendor support provides access to specialised skills and cost advantages but requires careful consideration of scalability and potential limitations in flexibility. Making an informed decision involves weighing these factors against the unique context and priorities of the organisation.
3. Diversify Vendor Relationships for Resilience
Advocate for the strategic diversification of vendor relationships to enhance your organisation’s resilience. Maintain multiple vendor partnerships to mitigate risks associated with sudden changes in the vendor landscape. Optimise your vendor ecosystems to align organisational goals with external partnerships. This approach positions your organisation to navigate challenges effectively and emerge stronger in the rapidly evolving AI environment.
4. Joint Model: Integrating External Expertise for Long-Term Operational Sustainability
An immediate establishment of an internal AI team might be challenging in the short term especially after vendor sudden changes or AI project failure. Rather than merely relying on external vendors, consider a joint model that leverages vendor expertise while building an internal Centre of Excellence for data and AI. This strategic move allows your organisation to gradually handle routine operations internally while continuing to benefit from external experts for review, verification, and new initiatives. This dynamic approach not only safeguards organisational confidentiality and deep knowledge during day-to-day operations but also aligns with industry best practices of safeguarding trade secrets within the organisational boundaries. This approach ensures both immediate operational efficacy and a steadfast journey toward internal AI competence.
Seizing the Strategic Opportunities Within Setbacks
Turning AI vendor changes or failed projects into strategic opportunities is not only possible but essential for your organisational growth and resilience. Embrace a culture of continuous improvement, diversify vendor relationships, foster innovation, optimise internal operations, and aim for long-term operational sustainability.
In the journey of navigating AI challenges, KewMann brings 10 years long expertise in the AI and data business at the Asia level, serving mission-critical industries like the public sector, healthcare, banking, and financial services, among others. Our key members of the team have also been engaged by ABS to train regional bankers and have authored two comprehensive books on big data and AI. Navigating setbacks with resilience, we view them not as failures but as stepping stones towards strategic advantages in the ever-evolving landscape of artificial intelligence.
