With all the hype around artificial intelligence, it’s tempting for businesses to jump on the AI bandwagon. But implementing AI just for the sake of it rarely delivers value. The key is identifying high-impact use cases where AI can solve real problems for your customers and business.
In this post, I’ll walk through a strategic process for finding your company’s most promising AI opportunities. Follow these steps and you’ll avoid AI for AI’s sake, while unlocking real competitive advantage.
Audit Your Customer Pain Points
Start by understanding your customers’ biggest unmet needs. Review support tickets, social media, app reviews, and satisfaction surveys. Look for repetitive issues and complaints. Talk directly to customers about their challenges. Also analyze metrics like churn rates for red flags.
Compile a list of major customer pain points and rank them by frequency and severity. These form your target list for potential AI applications.
Analyze Your Competitive Landscape
Conduct an objective assessment of what your competitors are doing with AI. Search their websites, apps, and marketing materials for language or features indicating AI usage. Sign up for their products and pay attention to your experience. Search published interviews with their data scientists. Subscribe to their engineering blogs for product update announcements.
Identify use cases where competitors are already using AI to differentiate their offerings. Your priority list of pain points are great AI opportunity candidates if competitors aren’t addressing them effectively. You can leapfrog laggards.
Brainstorm Creative Solutions
For each high-priority customer problem, lead your team in brainstorming sessions to ideate AI solutions. Encourage creativity and stretch thinking here. AI can transform user experiences in unconventional ways you may not consider initially.
Catalog all ideas first without critique. Then analyze each solution against criteria like technical feasibility, implementation cost, projected business impact, and fit with your overall strategy. Identify the 3-5 most promising AI applications for prototyping.
Start Small, Think Big
The best approach is iteratively launching AI products that solve focused use cases really well, then expanding from there. Aim your first release at improving a specific workflow or micro-scenario, rather than reinventing your entire business.
For example, an e-commerce site could start by using AI to generate more relevant product recommendations for each user. Get this right, prove value and scalability, then move onto harder challenges like personalized pricing or predictive inventory planning.
Think big on how an AI assistant could eventually coordinate across your full system – but start small on developing its first capability. Master focused use cases, then expand.
Choose Problems That Play to AI Strengths
Play to the inherent strengths of AI and machine learning. Good candidates have some combination of:
- Repetitive workflows ripe for automation.
- Complex decision making based on many variables.
- Tasks requiring constant personalization.
- Data-intensive challenges.
- Subjectivity and ambiguity.
Conversely, avoid use cases with sky-high stakes where errors are unacceptable, or problems lacking objective right/wrong feedback needed for AI training.
Lean Into Your Domain Expertise
Look within your organization for “pockets of AI opportunity” where you’ve already amassed advantages. Long-tenured team members, proprietary datasets, institutional knowledge etc. are hard for competitors to replicate quickly.
Don’t neglect all the tribial gained over years of experience in your specialty. For example, AI built atop a decade of patient data will outperform models trained on generic medical datasets.
Complement Humans, Don’t Replace Them
The most successful AI applications enhance human capabilities rather than attempt to replicate them outright. Use AI for tedious tasks while reserving skilled teams for higher judgement roles.
For example, an insurance firm could automate claims filing while human agents focus on complex approvals. Or AI schedules mundane meetings so salespeople have more time selling.
When evaluating use cases, ensure AI augments humans strategically rather than replace them wholesale.
Prioritize Enterprise Buy-In
Technical viability alone won’t get AI projects funded – they require executive buy-in. Make a compelling business case for how your AI applications will improve customer experience, reduce costs, increase revenue, boost competitiveness etc.
Propose hypotheses for how AI will move key business metrics, along with an implementation roadmap and resource requirements. Earning leadership support is crucial to securing budgets.
The Power of AI Use Cases
Adopting AI for one-off projects with unclear returns is playing with fire. But building AI solutions around your most valuable use cases – where you possess data advantages and alignment to customer needs – can transform your business for the better.
Take the time to be thoughtful about where AI can make the biggest impact. Follow the steps here to identify your company’s shortlist of high-potential AI applications. Then start small, prove value, and scale. You’ll be surprised how fast strategic AI investments compound capabilities and competitive differentiation.
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