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What’s Your Route to Enterprise AI Adoption?

Artificial intelligence (AI) is currently an inherent piece of our everyday lives. We don’t consider anything but seeing personalized product recommendations on Amazon or optimized real-time directions on Google Maps. The day isn’t far when we will have the option to bring driverless vehicles to take us home, where Alexa would have just arranged dinner subsequent to checking stock with our smart oven and fridge. That being stated, enterprise adoption of AI has been increasingly estimated however, it is advancing quickly to achieve tasks extending from planning, anticipating, and predictive maintenance to customer service chatbots and the like.

Understanding the province of Artificial Intelligence deployment, how comprehensively it is being utilized, and in what ways it is challenging for some business chiefs. AI and different innovations are progressing altogether quicker than many foreseen only a couple of years ago. The pace of development is accelerating and can be difficult to grasp.


KPMG 2019 Enterprise Artificial Intelligence Adoption Study is conducted to pick up understanding into the province of AI and automation deployment efforts to select huge top organizations. This is associated with in-depth interviews with senior pioneers at 30 of the world’s biggest organizations, as well as secondary research on work postings and media coverage. These 30 exceptionally powerful out of Global 500 organizations represent noteworthy worldwide economic value, on the whole, they utilize roughly 6.2 million individuals, with total incomes of US$3 trillion. Together, they additionally represent a noteworthy part of the AI market.

Almost all the employees so surveyed consider Artificial Intelligence to be playing a job in making new champs and losers. Artificial intelligence has wide enterprise applications and the possibility to move the competitive position of a business. The advances under the AI umbrella are as of now adding to product and service upgrades and they will be significant drivers of innovation for completely new products, services, and business models.

O’Reilly survey results show that AI efforts are developing from prototype to production, however, organization support and an AI/ML skills gap remain snags.

5 Reasons to Develop AI Systems In-House

1: The Best Core Technologies Are Open-source Anyway

The academic origins of open-source GPU-accelerated machine learning frameworks and libraries over the last ten years have made it all but impossible for well-funded tech giants to cloister promising new AI technologies into patent-locked, proprietary systems.

This is partly because nearly all the seminal contributing work has been the result of international collaborations involving some mix of academic research bodies and government or commercial institutions, and because of the permissive licensing that facilitated this level of global cooperation.

With occasional exceptions for the military sector and parts of Asia, state-funded research is publicly accountable by necessity, while commercial attempts to take promising code into private branches would starve them, fatally, of ongoing community insight and development.

Ultimately all the major tech players were forced to join the open-source AI Eco structure in the hope that some other differentiating factor, such as Microsoft’s business market capture, Amazon’s gargantuan consumer reach, or Google’s growing data mountains could later reap unique corporate benefits.

This unprecedented level of transparency and open technology gifts any private commercial project with free world-class machine learning libraries and frameworks, all not only adopted and well-funded (though not owned) by major tech players, but also proofed against subsequent revisionist licensing.

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2: Protecting Corporate IP

Most in-house AI projects have a more fragile angle on success than the FAANG companies, such as a patentable use-case concept or the leveraging of internal consumer data — instances where the AI stack configuration and development is a mere deployment consideration rather than a value proposition in itself.

In order to avoid encroachment, it may be necessary to tokenize transactions that take place through cloud infrastructure, but keep local control of the central transaction engine.

Where client-side latency is a concern, one can also deploy opaque but functional algorithms derived from machine learning methods, rather than trusting the entirety of the system to the cloud, and encrypt or tokenize data returns for local analysis.

Such hybrid approaches have become increasingly common in the face of growing breach reports and hacking scandals over the last ten years.

3: Keeping Control of Data Governance and Compliance

The specificity of the input data for machine learning models is so lost in the training process that concerns around governance and management of the source training data might seem irrelevant, and shortcuts tempting.

However, controversial algorithm output can result in a clear inference of bias, and in embarrassingly public audits of the unprocessed training source data and the methodologies used.

In-house systems are more easily able to contain such anomalies once identified. This approach ensures that any such roadblocks in machine learning development neither overstep the terms and conditions of the cloud AI providers nor risk infringing the lattice of varying location-specific privacy and governance legislation that must be considered when deploying cloud-based AI processing systems.

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4: AIaaS Can Be Used for Rapid Prototyping

The tension between in-house enterprise AI and cloud-based or outsourced AI development is not a zero-sum game. The diffusion of open-source libraries and frameworks into the most popular high-volume cloud AI solutions enables rapid prototyping and experimentation, using core technologies that can be moved in-house after the proof-of-concept is established, but which are rather more difficult for a local team to investigate creatively on an ad-hoc basis.

Rob Thomas, General Manager of IBM Data and Watson AI, has emphasized the importance of using at-scale turnkey solutions to explore various conceptual possibilities for local or hybrid AI implementations, asserting that even a 50% failure rate will leave an in-house approach with multiple viable paths forward.

5: High-Volume Providers Are Not Outfitted for Marginal Use Cases

If an in-house project does not center around the highest-volume use cases of external providers, such as computer vision or natural language processing, deployment and tooling is likely to be more complicated and time-consuming. It’s also likely to be lacking in quick-start features such as applicable pre-trained models, suitably customizable analytics interfaces, or apposite data pre-processing pipelines.

Not all marginal use cases of this nature are SMB-sized. They also occur in industries and sectors that may be essential but operate at too limited a scale or within such high levels of oversight (such as the nuclear and financial industries) that no ‘templated’ AI outsourcing solution is ever likely to offer adequate regulatory compliance frameworks across territories, or enough economy of scale to justify investment on the part of off-the-shelf cloud AI providers.

Commodity cloud APIs can also prove more expensive and less responsive in cases where the value of a data transaction lies in its scarcity and exclusivity rather than its capacity to scale at volume or address a large captive user base at a very low latency.