Working together

Ranging in timeframe to 2 days through to 4 weeks. Roadmap creation can take on a number directions depending on the needs of leadership.

Our approach is to create a strategic framework that aligns AI initiatives with business objectives, identifies potential AI projects, and outlines the steps needed to achieve desired outcomes.

Short Term Roadmap

This roadmap can help organizations navigate the complex AI ecosystem, making sense of the myriad of technologies, platforms, and methodologies available. It also aids in identifying and prioritizing AI projects that can deliver the most value, ensuring resources are allocated effectively. Moreover, it facilitates the development of repeatable building blocks, which are essential for scaling AI outcomes across an organization. These building blocks can be reused and adapted for different projects, accelerating the AI implementation process and reducing costs.

Medium Term Areas to investigate

Developing Applied AI systems is complex and requires deeper research into the building blocks required medium term to achieve longer range outcomes.  While there can be a lot of hype around AI, it's important as a business leader to understand the impact and effectively put in place strategies to meet the market at the right time. In many cases these medium areas to investigate can lead to new revenue lines, shifts in business strategy, and the ability to respond to competitors.

Competitive Disruptor Analysis

Understanding how your own business can be disrupted is critical when understanding and evaluating investments. Internal teams can do a phenomenal job of understanding these challenges but can sometimes be resource constrained or aren't as exposed to different industry approaches or novel technologies. Data Kinetic works closely with teams to uncover areas that could be disrupted and provide detailed analysis of the underpinning technology involved.

Typically a 4-6 week engagement with a series of workshops and evaluation sessions, companies are able to rapidly develop growth strategies and enable a robust framework for capturing value and avoiding the pitfalls of the rapidly moving Generative AI space.

Identification of Growth Opportunities: The identification of new growth opportunities through the use of generative AI. This could include the creation of new products, services, or business models that were not previously possible or considered.

Development of a Generative AI Strategy: The development of a comprehensive generative AI strategy that aligns with the organization's overall business strategy and objectives. This would outline how generative AI can be used to drive growth and innovation in the organization.

Training and Skill Development: The development of a training program to upskill employees in the use of generative AI. This would ensure that the organization has the necessary skills and expertise to effectively use generative AI for growth.

Performance Measurement: The establishment of metrics and KPIs to measure the performance and impact of generative AI initiatives. This would enable the organization to track the success of these initiatives and make data-driven decisions.

Innovation Culture: The fostering of an innovation culture that encourages experimentation and the use of generative AI. This could involve changes to organizational culture, processes, and structures to support the use of generative AI.

Scalable AI Solutions: The development of scalable AI solutions that can be easily expanded or adapted as the organization grows or as its needs change.

Typically ranging from 2-8 weeks depending on organizational and industry complexity, AI governance is a key value pillar to ensure safe, predictable adoption of AI technology. Outcomes include:

Establishment of AI Governance Framework: The development of a robust AI governance framework that outlines the roles, responsibilities, and processes for decision-making related to AI initiatives. This framework would ensure that AI systems are used responsibly and ethically, and that they align with the organization's overall strategy and objectives.

Development of AI Policies and Guidelines: The creation of clear policies and guidelines for AI use, including ethical considerations, data privacy, and security protocols. These policies would provide guidance on how to handle various AI-related scenarios and would ensure compliance with legal and regulatory requirements.

Risk Management Strategy: The formulation of a risk management strategy that identifies potential risks associated with AI use, including ethical, legal, and technical risks, and outlines measures to mitigate these risks.

AI Transparency and Explainability: The development of strategies to ensure the transparency and explainability of AI systems. This would involve creating guidelines for documenting AI systems and their decision-making processes, which would increase trust in AI among stakeholders.

Training and Education Plan: The creation of a training and education plan to upskill employees and stakeholders on AI technologies, their use, and their implications. This would ensure that all relevant parties are equipped to handle AI responsibly and effectively.

Measurement and Monitoring Mechanisms: The establishment of mechanisms to measure and monitor the performance and impact of AI systems. This would involve defining key performance indicators (KPIs) and setting up regular audits and reviews of AI systems.

AI Ethics Committee: The formation of an AI ethics committee or similar body to oversee AI governance and ensure adherence to established policies and guidelines.

Data Management Strategy: The development of a data management strategy that outlines how data is collected, stored, processed, and used in AI systems, ensuring data quality and integrity, and compliance with data privacy regulations.