Defining the pillars of AI strategy
Artificial Intelligence in the Enterprise
In today's rapidly evolving digital landscape, generative artificial intelligence (AI) has emerged as a powerful tool for businesses aiming to harness the power of data and drive innovation.
As more organizations recognize the potential of AI, developing a value-driven AI strategy is becoming increasingly important to ensure its successful integration and alignment with overarching business objectives.
AI has the potential to transform the way we work, making us more productive, efficient, and creative. The future of AI in the workplace is not about replacing humans with machines, but about augmenting human intelligence and enabling people to achieve more than they could on their own.
In this article, we will explore the four key pillars that form the foundation of a value-driven AI strategy:
AI Vision
AI Values
AI Risks
AI Adoption
Building a value-driven generative AI strategy for businesses involves focusing on these key pillars to bring about digital transformation. By understanding and addressing each of these components, businesses can create a comprehensive roadmap for implementing AI technologies that deliver tangible benefits and propel them towards a high-growth future.
Here are actionable items to implement each of the four pillars in building a value-driven AI strategy for businesses:
1. AI Vision:
Identify strategic opportunities for generative AI and other forms of AI that align with the business objectives.
Learn from mature organizations that have already deployed AI techniques to understand successful implementation strategies.
Consider how generative AI can transform existing economic and social frameworks, automate repetitive tasks, and provide new insights to gain a competitive advantage.
Senior leadership must clearly state how AI objectives link to enterprise goals. Clearly stating AI goals is essential for promoting and facilitating the adoption of AI throughout an organization. It is important to remove any organizational barriers to capturing value. This will also assist in identifying the most appropriate use cases, those that will provide a clear return on investment and foster further innovation.
Example Goal: AI automation to increase productivity
How Generative AI enables that goal: AI and automation increase productivity by shifting people away from managing mundane tasks.
Use cases: Knowledge management and training, content generation, code generation
Banking giant JPMorgan Chase, uses AI to automate the process of reviewing legal documents, saving thousands of hours of work.
Amazon uses AI to automate its warehouse operations, using robots to move and sort packages.
This is what Jeff Bezos, the founder and former CEO of Amazon has to say about AI:
“Machine learning and AI is a horizontal enabling layer. It will empower and improve every business, every government organization, every philanthropy — basically there’s no institution in the world that cannot be improved with machine learning.”
2. AI Value:
Set clear goals, benefits, and success metrics for generative AI implementation to link it to business impact.
Assess how generative AI can drive shareholder value by creating new opportunities, increasing revenue, improving customer engagement, reducing costs, and enhancing productivity.
Consider establishing an AI ethics committee to ensure only ethical AI practices are developed and implemented.
If there are any concerns in the organization that could hinder the ability to create value; say metrics which adequately deliver credibility for project maturity; consider collaborating with data and analytics officers to discuss what would be best to measure for success. Or for a concern of not having formal structures of accountability, consider a RACI (responsible, accountable, consulted, informed) matrix for AI strategy development and execution.
IBM is committed to harnessing the power of generative AI by establishing clear objectives, outlining the benefits, and marking success metrics. The organization employs AI as a catalyst for innovation, enhancing customer experiences while constantly seeking fresh avenues for growth and revenue generation. To maintain ethical standards, IBM has formed an AI ethics committee overseeing the responsible development and deployment of these technologies.
Google recognizes the potential of generative AI in delivering value to shareholders through various means, such as unveiling new opportunities for increased revenue, strengthened customer relationships, cost reduction, and productivity enhancement. They effectively utilize AI for refining search algorithms, introducing novel products and services, and streamlining their business processes. Google shares IBM's dedication to ethical AI integration and has also established an AI ethics committee to uphold these principles.
Microsoft mirrors this commitment by instituting an AI ethics committee designed to guarantee that their AI operations are ethically sound in both development and execution. Acknowledging the innovations brought about by AI, Microsoft concentrates on utilizing it to enrich customer engagement by identifying new possibilities for growth and revenue expansion. Like IBM, they too have set definite goals, weighed the advantages, and devised success measurements for their implementation of generative AI.
3. AI Risks:
Conduct a comprehensive assessment of the risks associated with adopting generative AI.
Identify potential pitfalls related to technology adoption and the unforeseen consequences of implementing AI solutions into business processes.
Develop necessary mitigation strategies to address these risks and ensure successful implementation.
Generative AI has risk. There have been reports of hallucinations and biased results. In 2018, a self-driving car operated by Uber struck and killed a pedestrian in Arizona. While this is a different type of AI, this tragic event underscores the importance of conducting comprehensive assessments and tests, as well as developing necessary mitigation strategies to address potential risks associated with AI technology. Failure to do so can result in serious consequences, as demonstrated by this incident. It is crucial for companies to take a proactive approach to risk management when implementing AI solutions, in order to ensure the safety and well-being of all stakeholders. By doing so, we can unlock the full potential of AI and drive innovation, while also protecting our customers and our reputation.
Key types of risk: What to do
Regulatory: Facilitate cooperation between AI experts and legal, risk and security staff to assess the feasibility and risks of use cases.
Reputation: Strengthen your AI security by recognizing the potential threats from both harmful and harmless actors within your organization. Enhance the protection of your enterprise security controls, data quality and AI model performance. Use external resources to help safeguard your AI systems.
Competency: Integrate your AI and cloud strategies and consider using cloud as a platform for AI. Launch a startup accelerator program to minimize technical debt and innovate step by step.
4. AI Adoption:
Prioritize initiatives based on their potential to drive desired business impact.
Plan and strategize the adoption of specific AI initiatives to align with the overall AI strategy.
Ensure that the selected initiatives are fully integrated into the organization's processes and systems.
Use simple criteria such as technical feasibility and business value factors to rank, evaluate, and score the feasibility and value of each project, making it easier to compare. Usually, executives want to go for initiatives that have high value (and also high risk, i.e., low feasibility) but avoid projects that are so infeasible that they are impossible. A use case that has a very high business value and a high feasibility is either a breakthrough, or a missed opportunity by the market.
By prioritizing initiatives based on their potential to drive desired business impact, planning and strategizing the adoption of specific AI initiatives to align with the overall AI strategy, and ensuring that the selected initiatives are fully integrated into the organization’s processes and systems, companies can unlock the full potential of AI and drive innovation, while also protecting their customers and their reputation.
Keeping these four pillars in mind and following a rigorous approach from developing a business-driven vision to planning specific initiatives for adoption, companies can successfully build an AI strategy that drives value for their enterprise.
“The popularity of many new AI techniques will have a profound impact on business and society,” said Arun Chandrasekaran, Distinguished VP Analyst at Gartner. “The massive pretraining and scale of AI foundation models, viral adoption of conversational agents and the proliferation of generative AI applications are heralding a new wave of workforce productivity and machine creativity.”
More reading:
https://emtemp.gcom.cloud/ngw/globalassets/en/information-technology/documents/gen-ai-planning-workbook.pdf
https://www.gartner.com/en/newsroom/press-releases/2023-08-16-gartner-places-generative-ai-on-the-peak-of-inflated-expectations-on-the-2023-hype-cycle-for-emerging-technologies