This is Part 2, continued from Part 1, where we first discussed three foundational issues related to AI and digital transformation. An AI transformation journey starts with the problem, not the technology, and business outcomes are both the starting point and the destination for AI transformation.
Before an organization begins to take the steps outlined below, it should first perform an initial AI feasibility assessment, as outlined in our previous article on AI consulting. After aligning AI initiatives with business objectives, mission, vision, and values, while keeping a continuous pulse on the needs of the end users, develop a clear AI strategy for AI investments and deployment in line with company’s business goals to achieve specific results.
Then consider the following 6 steps for AI transformation.
Analyze your business processes and identify the most important areas where AI can optimize efficiency, improve decision-making, and enhance customer experience.
Then you can determine the data required to train AI models. Focus your resources on your top priority opportunities, and establish a roadmap for implementation.
A skilled AI team is crucial for project execution, data management, and ongoing maintenance. Assemble a team with expertise in data science, machine learning, and AI engineering. Consider establishing a dedicated AI unit or integrating AI expertise within existing structures. “Professionals who are proficient in machine learning and artificial intelligence deployments should lead the AI functions.” https://research.aimultiple.com/ai-transformation/
Data security and privacy: Vigilant attention must be sustained with rules, procedures, and gatekeeping mechanisms to safeguard sensitive customer information.
To build AI solutions faster, consider forming an ad hoc partnership between an external AI team, with more AI technical expertise, and your internal teams who have deep domain knowledge.
Together, the AI engineers can define a measurable AI objective that creates business value.
With small, achievable, and measurable pilot projects, you can test and change strategies and approaches to AI transformation. The first few attainable projects should be examples for creating business value, and provide ways to learn, gain insights, refine, and prove the feasibility of AI solutions. This is also the best way to develop trust and momentum on the part of the entire organization for future AI transformation.
The pilot project can use off-the-shelf AI/ML tools, or customized solutions such as SLM (small language model), or a process mining tool for improving process efficiency or customer experience, or by automating data extraction and processing of document data… There are many low hanging fruits for organizations to pilot with.
“[f]rom early proofs of concept to production and demonstrate incremental business value… Pilots should be highly impactful on the organization and the business, as well as meaningfully benefit from AI being applied to it. Regardless of whether they are successful or not, they can help influence your future direction. Learning from them helps you adjust your approach before scaling to full production.” https://docs.aws.amazon.com/whitepapers/latest/aws-caf-for-ai/your-ai-transformation-journey.html
“Involve users from the outset and prioritize solutions by potential impact and feasibility to set the stage for success. Design implementation milestones, focusing on targeted user needs and allowing for periodic progress evaluations.” “This practice, far from being top-down, can empower everyday users to become changemakers, utilizing the most appropriate tools to maintain a competitive edge. The result is not resistance to change but an organizational culture where change becomes routine.” https://www.forbes.com/sites/forbesbusinesscouncil/2024/02/05/ai-transformation-a-three-step-blueprint-for-organizations/
The continuous, virtuous cycle feedback loop from end-users to an organization is based on a human-centered commitment to serving ever evolving customer needs, by constantly and proactively pivoting and improving business solutions. As AI speeds up efficiency, it is all the more important for companies to change from old practices of self-centered “profitization” to customer centric management practices.
Scaling pilot initiatives, both in technical capabilities and the scope of customer services, will lead to sustained improvement of customer value.
“AI adoption is a cross-functional effort, much more so than this is the case for other technologies. Aligning internally on the goals set in the envision phase is critical. Doing so helps you create strategies for improving your cloud and AI readiness at large, ensure stakeholder alignment and future buy-in, and facilitate relevant organizational change management activities.”
“While you iterate through these cycles, recognize the limits of what you can achieve in a single cycle. It is important to be ambitious and aim high, but trying to do everything in the same cycle can lead to discouragement in the organization. This is why pairing a larger picture with many pragmatic and actionable steps and measurable KPIs on these smaller steps is crucial. Every step then brings the organization closer to its goal. Do not try to do everything at once. Rather, evolve the foundational capabilities and improve your AI readiness as you progress through your AI transformation journey.” https://docs.aws.amazon.com/whitepapers/latest/aws-caf-for-ai/your-ai-transformation-journey.html
Foundational capabilities for AI and Gen AI usually refer to operations, security, platform, governance, people, and business.
With ears constantly monitoring stakeholder concerns and challenges, hands on building internal AI foundational capabilities, and eyes open to identify cross-organizational dependencies, an organization can scale AI transformation to achieve goals as described by
https://landing.ai/case-studies/ai-transformation-playbook/:
AI is a continuously iterative process. Companies can optimize value from AI transformation by constantly improving strategy, monitoring results, and refining models. Each AI project brings better insight about where AI can create the most value. As your company builds a track record by scaling AI technology, shifting company culture and the mindsets of leaders, managers, and employees is essential for successful implementation.
“AI initiatives should not be limited to investments in technology alone. Process and people aspects of transformation should not be underestimated.” “Deploying Artificial Intelligence across business operations requires restructuring of the entire technology strategy and infrastructure in the organization”, https://research.aimultiple.com/ai-transformation/
“AI is unlocking new potential for every enterprise.” “The results can enable a competitive edge for the business.“ https://h2o.ai/insights/ai-transformation/
AI Transformation is a continuous ongoing journey. Learning, adapting, and exploring AI’s impact is not a one-time event. Forward-thinking leaders can reap the long-term benefits by embracing both AI and human aspects of this transformation, to position their organizations in:
Enhancing customer experience: AI can make timely and reliable predictions of customer needs, and provide personalized interactions and support.
Improving decision-making: AI can help with better-informed decision making through analyzing data and identifying patterns and trends.
Increasing efficiency and productivity: AI can automate tasks, optimize processes, and free up humans for more strategic, innovative, and creative work.
The Wull experts and consultants are here to help you gain a significant edge over competitors with every aspect of your AI transformation, to empower you to innovate fast and create unique value propositions. “Think + Design + Do = Wull”.
Please contact us for more information or consultation. Thank you.
Sources
https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
https://wull.com/ai-consulting-in-ai-evolution-stephen-wullschleger/
https://h2o.ai/insights/ai-transformation/
https://landing.ai/case-studies/ai-transformation-playbook/
https://research.aimultiple.com/ai-transformation/
https://docs.aws.amazon.com/whitepapers/latest/aws-caf-for-ai/your-ai-transformation-journey.html
https://provectus.com/case-studies/transforming-insurance-underwriting-with-generative-ai/
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