AI maturity: Build a strategy to scale
- Posted on September 15, 2020
- Estimated reading time 3 minutes
The time is ripe for organizations to look at artificial intelligence (AI) with fresh eyes and rethink how AI can help their business in a post-COVID-19 world. While many businesses want to accelerate their usage of AI to help them emerge stronger, some will fall short of their goal.
Organizations that do not have a clear AI strategy or roadmap are stuck in time with only limited proofs of concepts, siloed adoption, and minimal return on investment. This is also clear from our recent AI Maturity research which reveals that 95% of organizations that are currently implementing AI, agree that AI strategy is the most critical area to scale.
Advanced AI strategies deliver highest payback
AI strategy is the ability to set and communicate a vision which is linked to an organization’s business objectives. Organizations that are most advanced in their AI strategy and maturity cite a payback of five times return on investment of what they have invested, much higher than the average of three times. Strategy should be the starting point to achieve the greatest return on the power of AI.
We find more often that AI strategies lag well behind where AI should be at an enterprise level to affect the rate of scale and deliver the greatest impact for most organizations. Why is this important? Three out of four C-suite executives believe if they don’t scale AI in the next five years, they risk going out of business entirely.
To scale AI initiatives, there is a need to develop an effective AI strategy that focuses on four critical areas: (1) prioritize your investments, (2) define your operating model, (3) develop the right talent and culture, and (4) ensure digital ethics is part of the data culture throughout the enterprise.
Four strategies to rethink AI maturity
1. Prioritize your investments
Planning a high-performing AI strategy is not just about deploying a single use case across the value chain. It is about shoring up AI and analytics resources for the enterprise in priority domains, which will drive innovation for your customers, within your operational processes and with your employee base.
An effective AI strategy should align and validate technical investments to accelerate your organizational goals around, improving customer experience, optimizing costs, building reliance, creating talent agility and reinventing products or services.
Once you align your AI strategy to business objectives, you can begin to identify the use cases that will influence your priorities. These could be about developing smarter products and services or making business functions more intelligent or automating repetitive or mundane tasks to free people up for more value-adding activities.
2. Define your operating model to deliver AI at scale
In our research, we explored some of the ways organizations are looking to mature AI and we found an emerging focus on creating a right-sized Center of Excellence (COE). Just over one-quarter of organizations are using this as a lever. COEs are a key element of a scalable AI strategy and a centralized ability to accelerate over time and drive measurable return on investment.
3. Develop the right talent and culture
There is an often quoted phrase that culture eats strategy for breakfast. I have worked with multiple clients where culture created a gap between AI strategy and effective deployment. AI needs to sit between employee skills and business processes to drive the greatest upside for the business. In fact, 80% of respondents agree that business culture and change management are make-or-break items for AI’s long-term success.
Creating an AI-driven culture is critical to enabling new experiences, efficiencies, and innovations. But to achieve those results, the people element needs to be central to your AI strategy. Recruiting and training users in AI enables a robust use of technologies and techniques deeply embedded within business processes that harness the power of AI at the user level. This is when the true value of AI begins to accelerate.
4. Ensure digital ethics and adopt strong data governance practices
AI brings unprecedented opportunities to businesses, but also incredible responsibility. Digital ethics is the practice of designing, developing, implementing, and operating digital technologies with good intention to do no harm, and, as a result, allows companies to engender trust and scale AI with confidence.
For example, we helped one client improve its ability to support fact-based decision making by developing a roadmap for creating a governance model with detailed recommendations. The solution helped DNV GL establish guidelines on how to scale, work and develop for Microsoft Power BI, while ensuring it was secure and user friendly.
Without question, a mature AI strategy will address data governance and ethics in AI. With ethical AI, you can shape key objectives and establish your governance strategy, creating systems that enable AI and help drive business results.
A mature AI strategy at scale delivers limitless possibilities
The outcome of a well-defined AI strategy holds extensive possibilities. From supply chains and improved customer engagement to making sure hospitals and providers can effectively track and manage personal protective equipment, clients are seeing how a mature AI strategy can drive results at scale. I’m convinced that as clients mature their AI strategy, they will be able to improve operational resiliency, find greater value throughout the enterprise, and be able to compete and emerge from this pandemic even stronger.
Find out where your organization’s AI maturity lands and how Avanade can help you succeed. We invite you to engage with us for an assessment to be benchmarked against our 1,700 respondents globally.