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Author: Sameer Dhanrajani, CEO at AIQRATE advisory & consulting | AI advisor, business builder | Author | Speaker |Columnist| President at 3AI | Ex-CSO: Fractal, Global Business Leader: Cognizant, Country Head: Fidelity National Financial

At the enterprise level, AI assumes enormous power and potential , it can disrupt, innovate, enhance, and in many cases totally transform businesses . Multiple reports predicts a 300% increase in AI investment in 2020-2022 and estimates that the AI market amongst several exponential technologies will be the highest . There are solid instances that the AI investment can pay off—if CEO’s can adopt the right strategy. Organizations that deploy AI strategically enjoy advantages ranging from cost reductions and higher productivity to top-line benefits such as increasing revenue and profits, enhanced customer experiences, and working-capital optimization. Multiple surveys also shows that the companies winning at AI are also more likely to enjoy broader businesses.

So How to make your Enterprise AI Ready?

72 % of the organizations say they are getting significant impact from AI. But these enterprises have taken clear, practical steps to get the results they want. Here are five of their strategic orientation to embark on the process to make AI Enterprise Ready :

Core AI A-team assimilation with diversified skill sets

Through 2022, organization using cognitive ergonomics and system design in new AI projects will achieve long term success four times more often than others    

With massive  investments in AI startups in 2021 alone, and the exponential efficiencies created by AI, this evolution will happen quicker than many business leaders are prepared for. If you aren’t sure where to start, don’t worry – you’re not alone. The good news is that you still have options:

Process Based Focus Rather than Function Based

One critical element differentiates AI success from AI failure: strategy. AI cannot be implemented piecemeal. It must be part of the organization’s overall business plan, along with aligned resources, structures, and processes. How a company prepares its corporate culture for this transformation is vital to its long-term success. That includes preparing talent by having senior management that understands the benefits of AI; fostering the right skills, talent, and training; managing change; and creating an environment with processes that welcome innovation before, during, and after the transition.

The challenge of AI isn’t just the automation of processes—it’s about the up-front process design and governance you put in to manage the automated enterprise. The ability to trace the reasoning path AI use to make decisions is important. This visibility is crucial in banking &  financial services, where auditors and regulators require firms to understand the source of a machine’s decision.

Evangelize AI amongst senior management

One of the biggest challenges to enterprise transformation is resistance to change. Surveys have found that senior management is the inertia led to AI implementation. C-suite executives may not have warmed up to it either. There is such a lack of understanding about the benefits which AI can bring that the C-suite or board members simply don’t want to invest in it, nor do they understand that failing to do so will adversely affect their top & bottom line and even cause them to go out of business. Regulatory uncertainty about AI, rough experiences with previous technological innovation, and a defensive posture to better protect shareholders, not stakeholders, may be contributing factors.

Pursuing AI without senior management support is difficult. Here the numbers again speak for themselves. The majority of leading AI companies (68%) strongly agree that their senior management understands the benefits AI offers. By contrast, only 7% of laggard firms agree with this view. Curiously, though, the leading group still cites the lack of senior management vision as one of the top two barriers to the adoption of AI.

The Dawn of System-of-Intelligence Apps & Platforms

Analysts report predicts that an Intelligence stack will gain rapid adoption in enterprises as IT departments shift from system-of-record to system-of-intelligence apps, platforms, and priorities. The future of enterprise software is being defined by increasingly intelligent applications today, and this will accelerate in the future.

By 2022, AI platform services will cannibalize revenues for 30% of market leading companies    

It will be commonplace for enterprise apps to have machine learning algorithms that can provide predictive insights across a broad base of scenarios encompassing a company’s entire value chain. The potential exists for enterprise apps to change selling and buying behaviour, tailoring specific responses based on real-time data to optimize discounting, pricing, proposal and quoting decisions.

The Process of Supporting Innovation

Besides developing capabilities among employees, an organization’s culture and processes must also support new approaches and technologies. Innovation waves take a lot longer because of the human element. You can’t just put posters on the walls and say, ‘Hey, we have become an AI-enabled company, so let’s change the culture.’ The way it works is to identify and drive visible examples of adoption. Algorithmic trading, image recognition/tagging, and patient data processing are predicted to the top AI uses cases by 2025. It is forecasted that predictive maintenance and content distribution on social media will be the fourth and fifth highest revenue producing AI uses cases over the next eight years.

In the End, it’s about Transforming Enterprise

AI is part of a much bigger process of re-engineering enterprises. That is the major difference between the automation attempts of yesteryear and today’s AI: AI is completely integrated into the fabric of business, allowing private and public-sector organizations to transform themselves and society in profound ways. Enterprises that will deploy AI at full scale will reap tangible benefits at both strategic & operational levels.

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