Artificial Intelligence, AI is one of the key emerging technology breakthroughs, also, disruptive because it can transform the world in unprecedented ways. AI will change the way businesses operate and improve efficiencies and cost. According to a McKinsey, AI investment is growing fast, dominated by digital giants such as Google and Baidu. Globally, we estimate tech giants spent $20 billion to $30 billion on AI in 2016, with 90 percent of this spent on R&D and deployment, and 10 percent on AI acquisitions. With the adoption of AI on the rise, enterprises across the world are using this technology to tap into endless new business opportunities – aiming us towards an AI-centric future.
However, for AI to be fully operationalised in analytics driven organisations, data is essential to help find, increase and sustain opportunities. A big part of the challenges that motivated organisations generally face are in the space of defining and identifying data use cases to acquiring data, creating value to a lack of democratisation to discovering metrics and so on. According to McKinsey’s paper on the Next Digital Frontier, “A successful program requires firms to address many elements of a digital and analytics transformation: identify the business case, set up the right data ecosystem, build or buy appropriate AI tools, and adapt workflow processes, capabilities, and culture. In particular, our survey shows that leadership from the top, management and technical capabilities, and seamless data access are key enablers.”
Below are highlighted a few key challenges that these key enablers will face when operationalising AI in their organisations:
Relevant data use cases
In the midst of all the excitement linked with the possibilities of AI, it’s a very real struggle for leaders to aptly navigate this ‘path to sure success’. In a scenario where most organizations are struggling to evaluate and finalize investment strategies, the identification of proven use case scenarios is extremely pertinent. Mainly because, it can help business design and implement strategies that realize business value and gain competitive advantage.
Preparing data for analysis
A lot of organisational data is sitting not only spread across various siloed systems but, also in proprietary formats that are not always compatible. The analysis and acquisition of this scattered data is a key challenge for companies embarking on an AI journey. Apart from difficulties with respect to ‘searchability’ of the data, a lack of standardisation when it comes to data governance and management become additional issues when getting data to be analytics-ready.
The data delicacy and inconsistency impedes the building of advanced AI models because of the lack of training data. Also, considering that organizations have begun exploring the possibilities of AI operationalization in the last years, they are also only now understanding the technology as a business enabler, right on the heels of Machine Learning and Analytics.
Democratization of data ownership
A siloed approach where there is a lack of democratization is a fairly common occurrence in organizations. How this plays out (as a challenge) when it comes to data ownership is that specific business units within a larger enterprise have their own standards and policies for collecting and managing pertinent data. This limits access and collaborative possibilities for the teams that are actually working on AI solutions leveraging enterprise wider data. This further limits organizations from generating new ideas and moving past stagnation when it comes to AI operationalization.
When it comes to AI and analytics, the nature of the investment is such that it’s hard to determine sure profit in the long run. Given the lack of a straightforward path that explicitly measures value of analytics, it’s harder to build support and justify the investment within leadership and key stakeholders. A GoodData article aptly describes the nature of metrics when it comes to AI, “Measuring the business success of AI is a complex question. For some companies, these solutions are a labor and cost savings measure and should be analyzed accordingly. Other implementations are tied to revenue generation, while some might not fit so nicely into “hard” metrics…”
A coherent team is extremely important when it comes to ensuring the success of an organisation’s AI linked objectives. Such a team would ideally comprise a balance of three skill sets, rather than center on the shoulders of a data genius. The formidable combination: Consultants who handle stakeholder management apart from identifying opportunities, problems and designing solution roadmaps, Data Scientists who arrive and build analytics models by cleaning, organising data and developing insights , and Data Engineers who develops, tests and maintains the architecture of the actual solution ground up.
In conclusion, the above listed challenges are not unassailable however it is imperative to solve them if the organisation intends to win this market’s AI fueled race.
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