Artificial intelligence (AI) is a transformative force. The automation of tasks that traditionally relied on human intelligence has far-reaching implications, creating new opportunities for innovation and enabling businesses to reinvent their operations. By giving machines the growing capacity to learn, reason and make decisions, AI is impacting nearly every industry, from manufacturing to hospitality, healthcare and academia. Without an AI strategy, organizations risk missing out on the benefits AI can offer.
An AI strategy helps organizations address the complex challenges associated with AI implementation and define its objectives. Whether it’s deeper data analysis, optimization of business processes or improved customer experiences, having a well-defined purpose and plan will ensure that the adoption of AI aligns with the broader business goals. This alignment is essential for extracting meaningful value from AI and maximizing its impact. A successful AI strategy will also provide a roadmap for addressing challenges, building necessary capabilities and ensuring a strategic and responsible application of AI into the fabric of the organization.
Organizations that make efforts to understand AI now and harness its power will thrive in the future. A robust AI strategy will enable these organizations to navigate the complexities of integrating AI, adapt quickly to technological advancements and optimize their processes, operational efficiency and overall growth.
What is an AI strategy?
An artificial intelligence strategy is simply a plan for integrating AI into an organization so that it aligns with and supports the broader goals of the business. A successful AI strategy should act as a roadmap for this plan. Depending on the organization’s goals, the AI strategy might outline the steps to effectively use AI to extract deeper insights from data, enhance efficiency, build a better supply chain or ecosystem and/or improve talent and customer experiences.
A well-formulated AI strategy should also help guide tech infrastructure, ensuring the business is equipped with the hardware, software and other resources needed for effective AI implementation. And since technology evolves so rapidly, the strategy should allow the organization to adapt to new technologies and shifts in the industry. Ethical considerations such as bias, transparency and regulatory concerns should also be addressed to support responsible deployment.
As artificial intelligence continues to impact almost every industry, a well-crafted AI strategy is imperative. It can help organizations unlock their potential, gain a competitive advantage and achieve sustainable success in the ever-changing digital era.
Read more about IBM’s AI Ethics governance framework
Benefits of a successful AI strategy
Building an AI strategy offers many benefits to organizations venturing into artificial intelligence integration. An AI strategy allows organizations to purposefully harness AI capabilities and align AI initiatives with overall business objectives. The AI strategy becomes the compass for meaningful contributions to the organization’s success. It empowers stakeholders to choose projects that will offer the biggest improvement in important processes such as productivity and decision-making as well as the bottom line.
More specifically, an AI strategy outlines the steps that will enable AI projects to smoothly transform ideas into impactful solutions. This calls for the organization to also make important decisions regarding data, talent and technology: A well-crafted strategy will provide a clear plan for managing, analyzing and leveraging data for AI initiatives. It will also determine the talent the organization needs to develop, attract or retain with relevant skills in data science, machine learning (ML) and AI development. It will also guide the procurement of the necessary hardware, software and cloud computing resources to ensure effective AI implementation.
In essence, a successful AI strategy is indispensable, acting as support for business objectives, facilitating prioritization, optimizing talent and technology choices and ensuring an organized integration of AI that will support organizational success.
Steps for building a successful AI strategy
The following steps are commonly used to help craft an effective artificial intelligence strategy:
Explore the technology
Gain an understanding of various AI technologies, including generative AI, machine learning (ML), natural language processing, computer vision, etc. Research AI use cases to know where and how these technologies are being applied in relevant industries. List issues AI can address and the benefits to be gained. Note the departments that use it, their methods and any roadblocks.
Assess and discover
Understand the organization, its priorities and capabilities. Review the size and strength of the IT department, which will implement and manage AI systems. Interview department heads to identify potential issues AI could help solve.
Define clear objectives
What problems does the organization need to solve? What metrics need to be improved? Don’t assume AI is always the answer, choose business objectives that are important for the business and that AI has a track record of addressing successfully.
Identify potential partners and vendors
Find companies in the AI and ML space that have worked within your industry. Create a list of potential tools, vendors and partnerships, evaluating their experience, reputation, pricing, etc. Prioritize procurement based on the phases and timeline of the AI integration project.
Build a roadmap
Create a roadmap that prioritizes early successes that will bring value to the business. Choose projects based on identified practical needs. Determine the tools and support needed and organize them based on what’s most crucial for the project, specifically:
Data: Make a data strategy by determining if new or existing data or datasets will be required to effectively fuel the AI solution. Establish a data governance framework to manage data effectively.
Algorithms: Algorithms are the rules or instructions that enable machines to learn, analyze data and make decisions. A model represents what was learned by a machine learning algorithm. Determine who will deploy algorithms and design, develop and validate models, as expertise is needed to effectively manage these tasks.
Infrastructure: Determine where your AI systems will be hosted and how they will be scaled. Consider whether to deploy on your own infrastructure or on third-party platforms.
Talent and outsourcing: Assess the readiness of and skills gaps within the organization to implement AI initiatives. Determine if a talent pipeline exists to fill roles such as data scientists and developers or if skills can be developed internally through training. Also assess if certain tasks, such as deployment and operations, should be outsourced.
Present the AI strategy
Present the AI strategy to stakeholders, ensuring it aligns with business objectives. Attain buy-in for the proposed roadmap. Clearly communicate the benefits, costs and expected results. Secure the necessary budget to implement the strategy.
Begin training and encourage learning
Start upskilling ai teams or hiring individuals with the right AI expertise. Encourage teams to stay updated on the cutting-edge AI advancements and to explore innovative problem-solving methods.
Establish ethical guidelines
Understand the ethical implications of the organization’s responsible use of AI. Commit to ethical AI initiatives, inclusive governance models and actionable guidelines. Regularly monitor AI models for potential biases and implement fairness and transparency practices to address ethical concerns.
Assess and adapt
Keep up with the fast-paced developments of new products and AI technologies. Adapt the organization’s AI strategy based on new insights and emerging opportunities.
Following these steps will enable the creation of a powerful guide for integrating AI into the organization. This will allow the business to take better advantage of opportunities in the dynamic world of artificial intelligence.
Common roadblocks to building a successful AI strategy
Several issues can get in the way of building and implementing a successful AI strategy. Their potential to impede the process should be assessed early—and issues dealt with accordingly—to effectively move forward.
Insufficient data
How and where is your data, really? AI models rely heavily on robust datasets, so insufficient access to relevant and high-quality data can undermine the strategy and the effectiveness of AI applications.
Lack of AI knowledge
A lack of awareness about AI’s capabilities and potential applications may lead to skepticism, resistance or misinformed decision-making. This will drain any value from the strategy and block the successful integration of AI into the organization’s processes.
Misalignment of strategy
If the AI initiatives are not closely tied to the organization’s goals, priorities, and vision, it may result in wasted efforts, lack of support from leadership and an inability to demonstrate meaningful value.
Scarcity of talent
Professionals are needed to effectively develop, implement and manage AI initiatives. A shortage of AI talent, such as data scientists or ML experts, or resistance from current employees to upskill, could impact the viability of the strategy.
AI strategy and IBM
Recent developments within artificial intelligence (AI) have demonstrated the scale and power of this technology on business and society. However, businesses need to determine how to structure and govern these systems responsibly to avoid bias and errors as the scalability of AI technology can have costly effects to both business and society. As your organization uses different datasets to apply machine learning and automation to workflows, it’s important to have the right guardrails in place to ensure data quality, compliance, and transparency within your AI systems.
IBM can help you put AI into action now by focusing on the areas of your business where AI can deliver real benefits quickly and ethically. Our rich portfolio of business-grade AI products and analytics solutions are designed to reduce the hurdles of AI adoption, establish the right data foundation, while optimizing for outcomes and responsible use.
Global enterprises rely on IBM Consulting™ as a partner for their AI transformation journeys. As a leading AI consulting firm, we enhance the impact of AI development and cloud technologies in business transformation by working across our own IBM watsonx technology and an open ecosystem of partners to deliver any AI model, on any cloud, guided by ethics and trust.
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