Enterprise AI Designed to Maximize ROI
Learn the practical steps for becoming an AI-powered enterprise. This guide covers building a data foundation, low-risk approach to AI implementation, augmenting people with AI, focusing on responsible AI, connecting AI to business value, and more. Implementing artificial intelligence doesn't have to be elusive for your business - follow these proven best practices to drive competitive advantage.
For many companies, artificial intelligence feels like an elusive, futuristic technology only accessible to tech giants like Google and Amazon. But AI is becoming essential for organizations of all sizes looking to gain a competitive edge. The question is, how can companies actually start using AI in impactful, responsible ways?
At Dynamo, we've worked with numerous clients exploring enterprise AI solutions. Through these experiences, we've developed perspectives on the practical steps organizations should take when embarking on an AI journey.
Focus on the Data Foundation
The hype around cool AI applications like chatbots and computer vision can be tempting for companies. But those thinking of AI strictly in terms of specific use cases are getting ahead of themselves. The elementary step is getting your data house in order.
AI algorithms are only as good as the data they learn from. So organizations need reliable pipelines for aggregating quality data across all their systems and sources. This includes structuring and labeling data for AI readiness.
Many companies discover they still have work to do on data governance, quality, and infrastructure. But investing in your data foundations will pay dividends down the road when deploying impactful AI.
Take an Iterative Approach
It's important to start small with AI experiments, gather learning and then build on success incrementally. Companies commonly make the mistake of wanting to "boil the ocean" from the beginning.
But AI projects have risk. Only by iteratively piloting solutions on focused issues can you effectively mitigate risks and demonstrate value. With each successful iteration, you expand the scope.
This agile approach prevents organizations from overcommitting resources to large-scale AI projects with unclear returns. And it helps build internal buy-in.
Focus on Augmenting People
Too often companies fixate on using AI to replace human activities and jobs altogether. But the biggest value comes from AI amplifying what your employees are capable of.
The goal should be integrating AI seamlessly into existing workflows to help employees perform efficiently and effectively. This human-centered mindset will maximize adoption and benefits.
For example, an insurance company could deploy AI to analyze documents and surface the most relevant information to claims processors. This augmentation makes employees' jobs easier while upholding quality.
Leverage AI Expertise
While intuition tells companies to build internal AI skills and tools, most organizations continue relying on outside expertise to kickstart progress. The market is flooded with qualified AI vendors, consultants, and open source tools to get your initiatives moving.
Attempting to build an extensive internal AI platform from scratch can be time-consuming and risky. Augmenting your team with outside talent and technology will accelerate your enterprise AI adoption.
This blend of internal capability building with external partnerships represents the most effective strategy for harnessing AI's benefits. Companies can transfer knowledge from external experts to their employees in the process.
Focus on Responsible AI
With growing scrutiny of AI's societal impacts, organizations must make ethics a priority. Responsible AI includes:
Mitigating algorithmic bias through diverse data sources and testing
Increasing transparency into how AI systems operate
Ensuring accountability through governance processes
Safeguarding privacy and securing data
Prioritizing responsibility will minimize risks and build trust with all stakeholders. And designing equitable, ethical AI systems can become a competitive differentiator that attracts talent.
Connect AI to Business Value
AI for AI's sake won't drive real impact. You need to deliberately link AI projects to business KPIs and priorities. This forces focus on using AI to move the needle on what matters most.
Connecting the dots between technology capabilities and business value is a best practice that extends beyond AI. But it's especially important with emerging technologies carrying substantial cost and complexity.
Relentlessly focusing AI efforts on driving measurable improvements provides the best chance of proving its value within your organization.
AI's mass-scale impact on business is an inevitability. But realizing its full potential requires forethought and discipline. Companies must build capabilities, data assets, and processes purposefully suited for AI integration.
Following the steps outlined here will accelerate responsible adoption of enterprise AI and unlock its benefits for your organization and employees. If you take the right approach, you can turn AI from a buzzword into a driver of real competitive advantage.