Our prior blog article outlines the benefits of becoming an AI Enabled Enterprise. An Enterprise AI Strategy is the key enabler for achieving that goal. Leveraging AI is non-negotiable and is critical for staying competitive.
If you think you have an AI Strategy because you’ve launched your own AI Chat Assistant, or happen to have a couple of AI solutions, think again. Leading firms are preparing for widespread AI adoption that will radically transform their organization. They forecast a world where using AI is second nature and thousands of AI Agents operate throughout their firm. An AI Strategy is an essential enabler and is critical for minimizing risks.
Why is an AI Strategy Important?
You can throw a rock in the air and hit an opportunity to use AI. Olmstead’s AI Use Case Map has identified over 100 use cases that impact every single department. The opportunity to leverage AI is significant, but wide successful adoption does not happen by accident.
Cultivating AI adoption across the enterprise requires intentional leadership. Firms need to strike a balance between cultivating an innovative AI culture where AI adoption is encouraged and ensuring that the firm is protected from risks. An Enterprise AI Strategy has the following goals:
- Fostering an AI Culture: Raising AI Literacy of the organization, upskilling talent, championing AI adoption, navigating around obstacles, and sharing success stories. Staff need to feel empowered to leverage AI to do their jobs better.
- Stewarding AI Adoption: Ensuring that the organization is safely adopting AI by establishing lightweight AI Governance processes, Guardrails for AI solutions, and Oversight for ensuring accountability, monitoring compliance, and dealing with exceptions.
- Delivering Use Cases with Tangible Business Value: Delivering practical AI solutions that offer business benefits such as Productivity gains, Enhanced Decision Making, and Innovation.
- Rationalizing AI Capabilities: Understanding available and potentially available AI capabilities so that funding can be optimized. Vendors adding AI capabilities to their products need to be vetted since there are often charge extra charges.
Firms need to understand how AI is being used throughout their organization because they will need to disclose its use to regulators, customers, and prospects. They will also need to explain what controls are in place and how risks like data privacy and hallucinations are being mitigated. An articulated AI Strategy allows for organizational alignment and prevents unchecked flourishing AI adoption creating risks for the organization.
Anatomy of an AI Strategy
An AI Strategy is a comprehensive plan unique to an organization that outlines how it will leverage AI to achieve its business objectives. It guides the adoption, implementation, and management of AI technologies within your company. It needs to align AI initiatives with your broader business goals and ensure that AI is creating business value.
Here are the key components of an effective AI strategy:
- Alignment with Business Objectives: An AI strategy must directly support and enhance your company’s overall strategic goals. This could include improving the customer experience, improving deal analysis, or developing new products and services.
- AI Vision and Use Cases: Clarify the vision for how AI will transform your organization. Identify specific use cases where AI can deliver measurable value. Firms typically start with an initial set of pragmatic narrow use cases that can demonstrate ROI and build momentum. Ultimately, AI use cases become an opportunity pipeline to be managed.
- AI Culture: Outline how the firm will foster a culture of learning and innovation around AI. This includes upskilling talent, coaching, and sharing success stories to improve AI Literacy. This is critical for attracting and retaining top talent.
- AI Stewardship: Define an Operating Model that includes Governance (roles, responsibilities, processes), Guardrails (ethics, policies, standards), and Oversight (compliance, escalation, overcoming obstacles, metrics) that enables AI adoption to flourish while avoiding danger and bureaucracy. AI adoption will suffer when staff needs to ask permission first, so governance should feel like a Yield sign, not a Stop sign.
- Curated AI Capabilities: Maintain an inventory of AI Capabilities, including both vendor and proprietary solutions. This may include introducing new platforms to fill gaps. Solutions are constantly evolving, so regular review is required for optimization.
- Metrics: Declare how AI success will be measured. This can include cost savings, throughput rates, automation levels, customer satisfaction scores, and more.
- Roadmap: Develop a phased roadmap that outlines the steps needed to implement the AI strategy. This includes timelines, milestones, and resource allocations. Organizational Change Management is critical for smooth adoption across the organization.
An AI Strategy is only as good as its data. An AI Strategy and Data Strategy are deeply interconnected and interdependent. The Data Strategy is a foundation upon which the AI Strategy is built. If you have issues with data accessibility, data quality, or data governance, then those weaknesses should be addressed before they risk the AI Strategy.
Implementing an AI Strategy
Success with AI ultimately comes down to implementing use cases to create business value. Firms are chock full of routine tasks that can be automated with AI, and that is a great place to start. Here are some examples from our industry:
AI-Assisted Investment Research
Replaced an antiquated Investment Research process that gathered earnings transcripts, expert calls, sell-side research, news, and financial filings, then extracted key data points, summarized views, and made predictions. The new AI process reduced report preparation time by 60-85%. Additionally, a more efficient process enables you to innovate by doing deeper research and/or doing research across more investment opportunities, which enhances decision making. According to Financial Times research, investment firms using AI-augmented analysis outperformed market benchmarks by an average of 2.7% in risk-adjusted returns, attributed partly to more comprehensive and timely informational advantages.
Meeting Lifecycle
Meetings such as sales meetings and client meetings can take hours. The lifecycle includes preparing materials beforehand, having the meeting and taking notes, and then summarizing outcomes and next steps from the meeting. An hourlong meeting often requires an additional hour in prep and recap. AI can automate the meeting prep, transcribe the meeting, summarize the meeting recap, and update the CRM. This automation can save an hour, creating 12.5% capacity. Additionally, it can lead to more effective meetings.
Hyper-Personalization
Clients want a personalized experience. AI enables firms to deliver a level of personalized service that was previously impossible without a massive increase in headcount. A leading wealth manager’s AI reporting system generates quarterly client summaries that automatically highlight relevant performance factors based on each client’s goals and risk tolerance, increasing client satisfaction scores by 28%. The client service role shifts from reacting to client needs to proactively anticipating and addressing them, leading to greater wallet share and higher client satisfaction and retention. Superior client experience builds deeper trust and loyalty in a competitive market.
DDQs and RFPs
Responding to Due Diligence Questionnaires (DDQs) and Requests for Proposals (RFPs) can be time consuming, particularly due to lack of standards. AI helps teams quickly draft responses to DDQs and RFPs by leveraging prior responses, relevant fund and regulatory documents, and enterprise data. Scaling with AI avoids missing out on growth opportunities due to human capacity constraints.
AI-Assisted Data Exploration
Not every business user knows SQL, but AI can easily translate natural language queries into SQL to answer questions. This saves time, reduces errors, and unlocks the value of your data platform. The ability to quickly respond to ad hoc questions and changing business needs is immeasurable.
Olmstead’s AI Strategy Framework
In summary, an AI strategy is about purposefully harnessing AI capabilities, mitigating risks, and aligning them with your overall business objectives to drive competitive advantage and long-term success. It is more than just technology; it is about people, processes, data, and a clear vision for how AI will contribute to your company’s future.
It can be overwhelming to construct an AI Strategy for your organization. Olmstead’s AI Strategy Framework and AI Use Case Map are accelerators for building a tailored AI Strategy for your organization. It is comprehensive yet lightweight and is ultimately customized to fit your needs. Contact us today to learn more.