In today’s rapidly evolving technological landscape, businesses seek innovative ways to harness the transformative power of artificial intelligence (AI). The rise of generative AI (GenAI) has opened new opportunities across industries, offering unprecedented ways to drive growth, improve productivity, and create competitive advantages. This blog explores how businesses can develop an AI strategy that balances value creation, risk management, and sustainable adoption.

From Vision to Action: Building an AI Strategy

A successful AI strategy begins with a clear vision and progresses through four key pillars:

1. AI Vision: Defining Opportunities and Goals: Businesses must identify how AI can align with their strategic goals and ambitions. GenAI, in particular, is revolutionizing industries by:
   • Increasing Revenue: AI accelerates product development, particularly in sectors like pharmaceuticals, healthcare, and manufacturing. For instance, AstraZeneca has utilized AI to streamline its drug discovery process, leading to quicker time-to-market for new medications. AI-driven quality control systems have helped Siemens reduce defects and improve manufacturing efficiency.
   • Enhancing Customer Engagement: Generative AI enables direct-to-consumer business models, disrupting traditional intermediaries and allowing organizations to connect more effectively with their customers. For example, Netflix’s AI-powered recommendation engine has enhanced customer satisfaction by offering personalized viewing experiences, contributing to increased user engagement and retention.
   • Reducing Costs and Improving Productivity: AI can streamline operations by automating processes, summarizing content, generating code, and optimizing chatbot performance. Shopify’s AI-driven chatbot, for example, has enabled its customer service team to handle a higher volume of inquiries, reducing response times and increasing customer satisfaction.
2. Measuring Success: Successful AI strategies emphasize metrics that reflect business impact. Mature organizations focus on:
   • Business Growth: Measures like cross-selling potential, price increases, and demand estimation. For instance, Amazon’s AI-powered demand forecasting has helped the company better manage inventory, reduce stockouts, and optimize pricing strategies.
   • Customer Success: Metrics include retention rates, customer satisfaction, and wallet share. Marriott’s AI-driven customer retention programs have contributed to increased repeat bookings and improved satisfaction ratings in the hospitality sector.
   • Cost Efficiency: Factors like production costs, inventory reduction, and employee productivity. For instance, Google’s AI-driven data centers have reduced energy consumption by 30%, leading to significant cost savings over time.

Removing Barriers to Value Realization

To achieve AI’s full potential, organizations must overcome several barriers:

• Process Transformation: AI adoption may require rethinking business processes, structures, and workflows. Upskilling employees and adapting to new ways of working can help organizations capture AI’s value. For instance, McKinsey reports that organizations effectively integrating AI into daily operations see an average 5-10% increase in productivity.
• Organizational Structure: AI-driven transformation may demand new roles, skills, and systems, including responsible AI deployment. A case study from Deloitte shows how an insurance company successfully incorporated an AI ethics officer to oversee the deployment of AI-powered underwriting tools, ensuring transparent and fair decision-making.
• Workforce Evolution: AI can create new jobs and opportunities, reshaping how organizations structure their teams. A Gartner report predicts that by 2033, AI solutions will contribute to over half a billion new human jobs worldwide, demonstrating AI’s potential to transform workforces.

Managing AI Risks

To sustain AI initiatives, businesses must navigate several risks:

• Regulatory: AI may lead to lawsuits over copyright, data protection, or intellectual property concerns. Staying compliant with evolving regulations is essential. For instance, a European Union directive mandates that AI solutions comply with strict guidelines to protect consumer data, prompting companies to adapt their AI strategies accordingly.
• Reputational: Transparency in AI operations is crucial. A lack of clarity in algorithms can lead to harmful outputs, making robust safeguards necessary. In the financial sector, JPMorgan Chase’s AI-powered fraud detection system has faced criticism for its lack of transparency, highlighting the need for precise, explainable AI algorithms.
• Competencies: AI demands specialized skills, requiring businesses to upskill existing talent or hire new employees with the necessary expertise. IBM’s partnership with several universities to offer AI and machine learning courses exemplifies efforts to address this competency gap, preparing the workforce for AI-driven roles.

AI Adoption: Prioritizing Use Cases

Successful AI adoption involves selecting use cases that deliver the highest business impact and feasibility:

• Business Impact: Clearly define the problem, the technology’s primary consumers, and how its success will be measured. Ensure alignment between use cases and business goals. For example, Spotify’s AI-powered recommendation system has effectively targeted user preferences, increasing engagement and driving higher subscription rates.
• Feasibility: Evaluate technical, internal, and external factors. Ensure technology options, culture, leadership, skills, and regulations support the chosen use case. For instance, Toyota’s AI-powered predictive maintenance system has successfully integrated into its manufacturing operations, reducing downtime and improving production efficiency.

Building an impactful AI strategy involves balancing business goals, risk management, and adoption feasibility. By developing an approach rooted in vision, value, risk mitigation, and feasibility, organizations can leverage AI to drive sustainable growth and long-term success. Visit Gartner’s website for more resources and guides on building a comprehensive AI strategy​​.