We help startups and growing companies decide where AI improves margins and growth, what it will cost, and how it changes their business model and valuation. Every company needs an AI strategy aligned to its business strategy; the framework below is how we build one.
We build AI as well as advise on it: our AI Finance & Accounting Automation replaces the bookkeeper and staff accountant in the companies we serve. See AI Finance & Accounting Automation.
AI is not only for large enterprises. A clear strategy gives smaller companies a roadmap: where AI lifts profitability and growth, what foundations it needs, and how to start small and scale.
Blockchain
We also advise on blockchain as a business enabler: tokenomics and token design anchored in valuation and runway, white papers and business-model validation for token-based startups, and core-process uses such as supply chain for transaction-intensive companies.
AI strategy development framework
I. Develop Vision to Harness AI to Achieve Company’s Strategic Objectives
Perform a deep assessment of the company’s current market position, operational challenges, and long-term strategic objectives to pinpoint where AI can deliver the most value.
Sample Vision: “To lead our SaaS target segment by harnessing AI to deliver unmatched value for our customers. Through deeper insights into customer needs, we will continuously innovate our offerings to stay ahead of industry demands and deliver the best value in the fastest way possible. We will also leverage AI to become price leaders by using advanced AI and no-code solutions to accelerate our time to market and cut IT development costs”.
II. Understand the Business Case for AI
Identify specific areas where AI capabilities will need to be introduced. Some key areas where AI can immediately have an impact include:
Profitability Improvement
Process Automation
Process Redesign & Optimization
IT Development Acceleration and Cost Reduction
Revenue Growth:
Product Innovation
Advanced Pricing Strategies
Enhanced Cross-Selling
Improved Customer Acquisition
Improved Customer Support and Retention
Enhanced Decision-Making
III. Lay the Foundation for AI Adoption
To successfully implement AI, SMEs must first build a solid foundation. This involves careful planning, preparation, and alignment across various aspects of the business. The initiatives presented below are not exhaustive.
Step 1: Data Readiness
Conduct a Data Audit
Clean and Organize Data
Establish Data Collection Processes
Step 2: Cultural Shift
Educate Leadership and Staff
Communicate the Vision
Foster a Data-Driven Culture
Step 3: Technology Stack Assessment
Evaluate Existing Infrastructure
Select Appropriate AI Tools
Implement a Scalable Infrastructure
Step 4: Skill Development
Assess Current Skills
Upskill Existing Employees:
Hire or Partner for AI Expertise
Establish an AI Champion or Team
IV. Implement AI Solutions
Define Clear Objectives for AI Capabilities Introduction
Build or Buy AI Solutions
Implement Pilot Projects
Scale Up
Continuous Improvement, Monitoring, and Adaptability
V. Overcome Common Barriers to AI Adoption
Cost Constraints
Data Privacy and Security
Lack of Expertise
VI. Measure Impact of AI vis-à-vis the Vision
Key Metrics
Case Studies
VII. Adapt Company Vision to Evolving AI Developments
AI is constantly evolving, and to stay competitive, companies must refine their AI vision in line with the latest advancements. This isn’t a one-time setup—it’s an ongoing process that directly fuels growth and profitability.
Regularly Reassess the AI Vision
Invest in Continuous Learning
Monitor Industry Trends
Implement a Scalable AI Infrastructure
Build Strategic Partnerships
Gather and Analyze Customer Feedback Continuously
Incorporate AI into Strategic Planning
AI Insights for Founders and CEOs
Analysis of how AI is changing strategy, valuation, fundraising and finance:
Contract Options
Time Commitment
On demand.
Fixed number of hours/month.
Full-time.
Location
Remote.
On-site.