The AI Strategy for Your business from Deloitte’s State of AI in the Enterprise, 4th Edition report, discusses strategists’ improved decision-making.
Step 1: Improving Organizations’ Approach to AI Transformation
A key approach for AI transformation is establishing an all-encompassing strategy led by top management.
Our State of AI in the Enterprise, 4th Edition survey supports this: Transformers are more likely to have a comprehensive strategy and to communicate a vision for AI compared to Starters. However, only 40% of respondents fully agreed that their company has one. Despite 66% considering AI essential for success, only 38% feel their AI use sets them apart from competitors.
Step 2: Understanding the Different Types of AI and Their Applications
In the realm of artificial intelligence, it is crucial to understand the different types of AI and their applications: this knowledge will form the backbone of any effective AI strategy. These machine intelligence classifications are commonly divided into three categories: Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Superintelligence (ASI).
Step 3: Leaders Often Neglect AI Strategy for Your Business
Leaders often underestimate the need for a holistic AI strategy, instead focusing on individual use cases. This approach, especially when led by IT or data sciences instead of business leaders, limits the potential benefits of AI, such as improving competitiveness, product launching, market speed, and supply chain security.
Effective AI strategies start with the core business strategy and involve collaboration with leaders across all business divisions. It should align with the key performance indicators (KPIs) to enhance competitive advantage. Jeff Bezos is a prime example of this approach who integrated AI and machine learning (ML) across Amazon, resulting in remarkable innovation and growth.
Business leaders should identify opportunities within their divisions to apply AI. These plans should then be aligned with the main business strategy. Integration of AI throughout the enterprise is crucial for efficiency and value creation.
Step 4: Evaluating the Risks and Challenges of Implementing an AI Strategy
As organizations delve into the intricacies of implementing an AI strategy, it becomes evident that this process is fraught with risks and challenges: both anticipated and unforeseen. Understanding these potential obstacles is crucial, as they can significantly impact the success of the AI initiative. The potential risks and challenges can range from technical complexities and data privacy issues to lack of skilled personnel and resistance to change.
One of the most significant challenges is the technical complexity of AI technologies. AI systems can be intricate and challenging to manage, especially for organizations that lack technical expertise:
Artificial intelligence, by its very nature, hinges on advanced algorithms, statistical techniques, machine learning principles, and complex data structures: all components that require a certain level of technical acumen to comprehend and harness. This is particularly true for businesses without an established technical team or those venturing into AI technologies for the first time.
Given these challenges, it becomes imperative for organizations to develop a comprehensive AI strategy. But how can such a strategy be constructed effectively? How can it be ensured that the AI strategy aligns perfectly with the broader organizational goals, while maximizing the potential of AI technologies?
1. Understand the AI Landscape
Foremost, a successful AI strategy requires a deep understanding of the AI landscape: both in terms of general AI technologies and the specific AI applications relevant to the organization’s industry. This should include staying abreast of the latest advancements and trends in AI, and comprehending how these could potentially be integrated into the business. Understanding the AI landscape is fundamental to making informed decisions about AI investments and initiatives.
2. Establish Clear Objectives
Once the landscape is understood, the organization must establish clear objectives for its AI strategy. These objectives should be in line with the broader business goals and should detail the specific outcomes the organization hopes to achieve with its AI initiatives. Objectives could range from increasing operational efficiency, to enhancing customer experience, to driving innovation: all depending on the nature and needs of the business.
3. Build a Skilled Team
No AI strategy can be successful without a team of skilled individuals who can implement it. This involves not just hiring AI specialists, but also training existing staff on AI capabilities and applications. Building a strong AI team is crucial to managing the technical complexities of AI and ensuring that the organization’s AI initiatives are successful.
4. Invest in the Right Infrastructure
Implementing AI initiatives requires significant infrastructure investment. This includes the necessary hardware for running AI algorithms, the software for managing AI data, and the cloud services for storing and processing that data. Investing in the right infrastructure is key to the successful execution of an AI strategy.
Step 5: Balance your goals: Over indexing on efficiency can lead to missed opportunities
efficiency and value-creation targets. Rajeev Ronanki, SVP and Chief Digital Officer at Anthem, says that with digital transformation, efficiency is crucial. However, exploring new business opportunities and capabilities that AI provides is also important.
Our survey shows that organizations with less success tend to focus more on efficiency and cost reduction. On the other hand, successful organizations emphasize growth-oriented goals such as customer satisfaction, creating new products, and entering new markets. These organizations are adept at identifying and seizing opportunities often overlooked by others focused purely on efficiency or maintaining the status quo.
Impact and foundation building must go hand in hand,” suggests Najat Khan, PhD, Chief Data Science Officer and Global Head of Strategy & Operations for Janssen Research & Development. The competitive edge is achieved by AI-driven companies when the CEO and senior management utilize data, analytics, and AI to form short and long term strategic plans aligned with their corporate strategy.
Choose diverse and impactful questions to show early value and gain momentum for long-term success.
Najat Khan, PhD, Chief Data Science Officer, Janssen Research & Development.
Step 6: Communicating Vision: Signaling Transformation to Increase Market Value
Top CEOs often serve as the main AI communicator. Our data shows that clear vision communication can lead to better outcomes. Effective leaders use their platform to outline their plans and the necessary sacrifices.
Clear communication is crucial for keeping the organization focused and aligned. Leaders can also create value by influencing market and investor perceptions. Publicly communicating the company’s vision can boost success, showing markets and potential talent that the company is committed to a bold future. Without strong communication, a good strategy might not overcome the inertia of the status quo.
Step 7: Measuring the Success of Your AI Strategy and Making Adjustments
Assessing and adjusting an AI strategy is crucial for strategic development. It helps organizations evaluate their AI efforts, pinpoint areas for improvement, and make informed decisions to enhance their AI skills. This requires careful planning, understanding of key metrics, and adaptability to changing business and tech scenarios.
The first step is to set clear, measurable goals that reflect the organization’s overall aims. These goals should consider the advantages of AI, such as better decision-making, improved operational efficiency, better customer service, or new product development.
Step 8: Leveraging Data for AI Insights and Decision-Making
The implementation of an effective AI strategy is heavily dependent on the appropriate use of data. Notably, the role of data in AI cannot be underestimated: it serves as the foundation upon which AI models are built, trained, and optimized. Data powers insights, drives decision-making, and ultimately shapes the trajectory of the AI journey.
However, it’s not just about having vast quantities of data, but rather, about having the right kind of data. Organizations need to ensure that their data is accurate, reliable, and relevant to their specific needs and objectives. In essence, data quality trumps data quantity.
Step 9: Refine and Evolve Your AI Strategy
Finally, developing an enterprisewide AI strategy that’s set up to fuel a differentiating core business strategy is not a one-and-done exercise. Organizations should develop dynamic ways of assessing their strategy to ensure it remains responsive to ever-changing market and technology developments. As the organization’s core business strategy and AI capabilities mature over time, leaders should continually sharpen their goals, moving beyond staying competitive to
increasingly using AI and ML as competitive differentiators.
Before selecting any AI technologies or tools, it is important to identify the specific business problems that the AI strategy aims to solve. This will help to determine the type of AI technology or tool that is best suited for the task at hand. For example, if the goal is to improve customer service, a chatbot or virtual assistant may be the best option.
| Step | Summary |
|---|---|
| 1 | Establish an all-encompassing AI strategy led by top management. |
| 2 | Understand different types of AI (ANI, AGI, ASI) and their applications. |
| 3 | Avoid focusing solely on individual use cases and integrate AI with core business strategy. |
| 4 | Evaluate risks and challenges of AI implementation, including technical complexity and data privacy issues. |
| 5 | Balance efficiency with exploring growth-oriented goals and opportunities. |
| 6 | Communicate the AI vision clearly to align the organization and create market value. |
| 7 | Continuously measure and adjust the AI strategy based on key metrics and changing circumstances. |
| 8 | Emphasize the role of accurate and relevant data as the foundation for AI models and insights. |
| 9 | Evolve the AI strategy over time to remain responsive to market and technology developments and leverage AI as a competitive differentiator. |