5 Ways to Scale Enterprise AI for Maximum Value

Artificial Intelligence (AI) is redefining the enterprise ecosystem. Alongside the emerging patterns and data infrastructure, the enterprise world has begun to experiment with the capabilities of AI. This has led more businesses to extrapolate technologies to a scale. 

According to Gartner’s report, in 2021, AI augmentation will create $2.9 trillion of business value. Worker productivity increases by 6.2 billion hours that includes learning, decision making, and new experiences.

Businesses have adopted AI anticipating a larger shift to increase profitability and keep projects inflow. Moreover, development platforms, data storage, AI-powered digital infrastructure is advancing rapidly and have become more affordable. To toss out some advantages of AI, The use of automation and AI tools has reduced mistakes while providing personalization and convenience for the enterprise to transform and succeed.

One of the biggest misconceptions among enterprise leaders is viewing AI for immediate returns. Understanding the consequences while having the cutting edge technology and talents as needed, the business leaders should articulate the problem they are facing, the impact, and value associated with the business problems before deploying AI for the enterprise. Why is it so?

Artificial intelligence can revolutionize every aspect of an enterprise, leveling up a unique possibility of progress and lasting shift to create relative balance. Being used in a wide range of industries, it is providing new opportunities and does constitute challenges.  

"There's no one thing that defines AI. It's more like a tapestry of modern intelligent technologies knit together in a strategic fashion that can then uplift and create a knowledge-based that is automated - where you can extrapolate finding from there."

Says, John Fremont, Founder & Chief Strategy Officer of Hypergiant

It is important to understand the parameters that will define the individual and collective success from the adoption of AI. The primary focus of AI-driven business value is customer experience. Optimizing the entire customer journey can help you overcome the struggle that lies in the business problems.

To drive the expected results and add value, it's crucial to see the technology that aligns with company culture, structure, to scale up the business. We are sharing five ways to leverage the use of artificial intelligence to scale enterprise for maximum value.

1. Rethinking the Paradigm of Business

The results of AI depends on the accuracy of the business decisions. When the value of AI is never realized, it can cause catastrophic consequences for business operations. AI can deliver a bigger impact when it's developed by cross-functional teams with skills and varied perspectives. 

A diverse team is more likely to recognize the enterprise priorities, operational changes, and new applications that may be required to maintain the workflows. Development should be shaping the mindsets and data-driven decisions are helping in ways of working to support a broad AI adoption. Bringing dramatic changes within the organization.

With the adoption of AI, leaders often make decisions based on their judgments and intuition with algorithms, these recommendations help them to arrive at better answers. But for this approach to work, the decision is made by employees at all levels,  have to trust the algorithms, and feel empowered to decide or make plans supported by data. AI has shifted the complex manual method in the decision making processes. 

2. Aligning with Strategy

The counterpart of the intelligent software is implemented to handle a routine process with precision and to help professionals to focus more on the human aspects of the business process. 

The human factor is the most important in the context of AI. There are certain human talents where machines are currently unmatched. Those main highlights are interpersonal communication, empathy, and creativity, which has to lead to the creation of new value, new jobs, and a higher level of responsibilities. 

In the early implementation progress, surveying the end-users, and observing their habits, studying the workflow to analyze and fix problems as needed. Experimenting with AI can help in reframing strategies to reduce the fear of failure. 

Aligning AI to the company's cultural values might seem like a struggle to assemble interdisciplinary teams. But through combining the expertise and skills, relationship managers could improve the customer's experiences thus increase the flow of revenue and profit. 

3. Organizing AI for Scale

The return on investment is a pivotal aspect that enables the enterprise's decision to invest in AI and the success of the new technology implementation. AI is crucial when it comes to short term gains but it can be well organized for the future to make the investment more significant with the business roadmap. 

Goals should be more narrowed in the terms of process, milestones, talents, training, and other key factors including the enterprise's vision for AI. According to the Harvard Business Review surveys, nearly 90% of the companies that had engaged in successful scaling practices for integration as for technology. 

Moreover, the automation processes don't need human intervention, it is easy to identify frauds, data processing, and support in analyzing functions that demand human involvement. prioritizing on the long term vision and considering other initiatives, AI-supported customer service will pay off delivering maximum value.

4. Educate the Workforce

In a rapidly growing space, there is a high level of requirement for technical innovation. Building the capabilities, they can monitor the changes and face competitive challenges in terms of data analytics maturity and business complexity. By getting the diverse perspectives to the frontline, companies can build, deploy, and monitor new AI capabilities to extract value faster. 

To ensure the efficiency of in-house skills, companies need to educate all its people by launching internal AI training to oversee the distribution of AI initiatives. Fundamental training can improve decision making and improve business scenarios and underlying workflow changes.

5. Reinforce the Change

Harnessing AI to deliver business value is the biggest challenge faced by the business. Even though AI can deliver desired data for decision making, with relative customization AI tools are the best place to start when it comes to solving technical problems. 

Intelligent automation and predictive reports are more likely to be with clear accountability across the business strategy. For the same reason, AI projects must be led by the business units and they are responsible for the success of AI transformation. Providing training, rewarding practice for the employees can keep them inspired in the long run, and help them to be adaptive to changes.

AI can be implemented throughout the enterprise, organizational hierarchies should encourage collaboration between agile teams and even bigger thinking to derive the maximum value. Expanding AI capabilities enables the management to solve discrete problems in operational and business models. The business now can leverage greater advantage in their analytics journey.