What does it take for your company to experience lasting benefits from artificial intelligence (AI)? Are you hearing about the AI hype but need more tools to lead durable, scalable change? Leaders in every industry and business function are exploring the changes to their role and their team due to AI technology. Many companies have launched AI pilot projects to experiment and identify AI use cases that can be scaled across an enterprise. However, according to a Gartner survey, 49% of leaders who are highly involved in AI struggle to estimate and demonstrate the value of AI. To realize the growing benefits of AI, you need to develop a thoughtful AI strategy that integrates with your overall business strategy.
Before you can develop an AI strategy for your business, you need a foundational understanding of AI technology—how it works, its capabilities, and its limitations. If you haven’t read the Leader’s Guide to Building AI Acumen and Maximizing Business Impact, we recommend starting here and then returning to this blog.
With a foundational understanding of AI, it is now time to begin building your AI strategy. Here are the four building blocks of a winning AI strategy.
- Your overall business strategy
- Your core business processes that drive your business strategy
- Use case identification & technical feasibility
- Prioritization for selecting which uses cases to tackle now or later
Let’s break each of these components down further.
# The Four Building Blocks of a Winning AI Strategy
# 1. Your Overall Business Strategy
Your AI strategy should be in service of your overall business strategy. In many ways, starting with your business strategy will make things less complicated—you already have part of the puzzle. Think of your priority focus areas for the fiscal year documented in your strategic plan, objectives and key results, or big bets. Perhaps these are supply chain efficiencies, workforce retention, and satisfaction, or higher customer personalization and loyalty. Are you accountable to an enterprise-wide strategic objective? Or do you lead a single department? Either way, write down the priority workstreams for this year. The more specific you are, the easier the next step will be.
Related Content: AI in Action: How Top Businesses Shape Their AI Strategy
# 2. Core Business Processes
The next building block to your AI strategy is a strong understanding of your core business processes. These are the processes that are central to driving the priorities in your business strategy. For instance, if a strategic goal is “Efficient & Cost-Effective Contract Management,” core business processes could include 1) contract drafting and negotiation, 2) review, and 3) approval and signing. Each of these processes can be further broken down into discrete steps. Document the steps that are repetitive, objective, and time-consuming. These steps may be ripe with AI opportunities.
Don’t forget to consider the processes from different vantage points, such as the perspective of the employee, vendor, and customer. A consumer-facing strategic goal could be “Personalized Marketing at Scale.” This goal includes internal business processes like 1) data gathering and integration, 2) segmentation and audience targeting, 3) content creation, and 4) experimentation. It also includes consumer-facing processes like 1) awareness, 2) engagement, 3) purchase, and 4) loyalty and retention. What are the steps and sub-steps to these processes?
# 3. AI Use Case Identification & Technical Feasibility
Once you have your core business processes mapped, it is time to identify potential AI use cases. This means you are identifying the overlap between pain points in your business processes and AI’s capabilities. Clearly define the problem you want to solve with your AI use case and the anticipated impact on the business’s strategic goals. For instance, implementing an HR chatbot could handle 90% of employee requests for frequently asked, low-risk questions, thereby contributing to overall strategic goals related to workforce retention and satisfaction.
At this point, it is critical to involve engineers who are versed in AI capabilities to check for the technical feasibility of potential use cases. This will prevent spending valuable time and resources on efforts that are not possible with today’s technology. Testing for technical feasibility involves confirming the availability, quality, and structure of your data for the use case, assessing your current technological infrastructure to support AI workloads, selecting a suitable AI model, and evaluating the scalability of the AI solution in a real-world environment. Additionally, review your processes and highlight the phases that involve data analysis, pattern recognition, automation, and real-time responses, as these requirements most align with AI’s capabilities. Examples of AI use cases for the contract management process previously mentioned could include:
- Analyze past negotiation data to predict counterpart behavior and suggest negotiation strategies (data analysis)
- Compare contracts with contract templates and current legal requirements and identify potential risks or deviations from standard terms (pattern recognition)
- Automate the creation of contract templates and use AI to suggest optimal clauses based on historical data and legal standards (automation)
- Monitor contract execution to detect missed deadlines or expiration dates (real-time response)
# 4. Prioritizing AI Use Cases
When you have a list of potential use cases that have been vetted for technical feasibility, it is time to prioritize the work. Andrew Ng—founder of DeepLearning.AI, co-founder of Coursera, and leading expert in AI and machine learning—recommends prioritizing projects which:
- Show traction in 6-12 months. This means adding measurable value in 6 months and, ideally, a 200% return on investment in 1 year.
- Have a clearly defined and measurable objective that creates business value.
- Allow for a new or external AI team (which may or may not have deep domain knowledge about your business) to partner with your internal team.
Source: landing.ai AI Transformation Playbook
Further, Ng prioritizes projects that align with the “Virtuous circle of AI” positive feedback loop. These are projects that accumulate good data more quickly. The theory is that a project that strengthens a product attracts more users, which, in turn, gathers more data. This data is poured back into product improvements, which attracts more users and continues the virtuous cycle of gathering more data. Data is the foundation of AI. The better the data, the stronger the AI. If your company can harness the power of the virtuous cycle of AI, the higher the possibility you’ll create a defensible moat against competition.
McKinsey offers another prioritization framework in the form of a 2x2 grid. They advise mapping your AI use cases by technical feasibility and business impact. Variables for technical feasibility include data and knowledge readiness, solution readiness, ability to scale, and reusability. Business impact variables include value creation, strategic alignment, improved employee experience, and business readiness. AI use cases with high feasibility and high impact are to be prioritized. Those with lower feasibility yet high impact should also be carefully considered as they will likely require higher investment.
No matter which framework you choose, focus on projects that drive measurable business value, are technically feasible, and leverage data for continuous improvement.
# AI Strategy Enablers
Once you have prioritized use cases that have been vetted for technical feasibility and align with your business strategy—you have your AI strategy. However, simply having an AI strategy does not secure the strategy’s success. There are surrounding business dynamics that enable the success of an AI strategy. Without proactive attention to these dynamics, you will struggle to gain momentum and change that does occur may not have staying power.
Review the checklist of the top 8 AI strategy enablers below:
Leadership and Governance
- Garner executive sponsorship via an AI Steering Committee
- Establish a governance and portfolio review process for new and existing use cases
- Establish cross-functional working groups
Technical and Data Management
- Validate technical infrastructure and expertise
- Plan for maintenance, integration, and data management
- Review, clean, and restructure data
Training and Change Management
- Build AI literacy and training
- Consider user readiness and change management
An AI strategy is an integrated, cross-functional effort that requires the entire business to have a thorough understanding of the technology and how to effectively interact with it in order to realize its full potential.
# Developing an AI Strategy For Your Business
Developing an AI strategy that aligns with your business strategy is essential for realizing the lasting benefits of AI. It requires a deep understanding of your core business processes, identifying viable AI use cases, and prioritizing projects based on technical feasibility and business impact. Remember, success in AI is not just about the technology; it also involves ensuring data readiness, fostering AI literacy, and managing change effectively. By focusing on these enablers and continuously iterating, your company can harness AI's full potential, driving innovation and maintaining a competitive edge in the market.
No matter where you are in your AI journey, our experts can help.
