By Megana Natarajan - January 12, 2021 4 mins read
Enterprise leaders discuss how they can prevent AI projects from failing by elucidating best habits of successful AI leaders
CIOs say that failure to plan AI initiatives properly often wastes millions of dollars and years in man hours. It may be good to create a single meta-model before implementing any AI initiative.
Similarly, leaders would hire thousands of data scientists at high salaries to potentially unleash the true power of the tech or lining up ambitious projects that would use unstructured data -without building a proper workflow or operation processes.
Leaders point out that it requires meticulous and structured responsibility matrix well before the project starts and long after it goes live.
Initiating AI projects that aren’t aligned with the corporate vision:
CIOs say that only a small number of enterprises accurately aligned their AI policy with corporate strategy. Most of the initiatives have been a waste of funds, in the name of AI adoption.
It is imperative that AI projects address key issues.
It is also critical that the planned outcome of these projects aligns with the business strategy. Hence, enterprises need to have a clear business vision and a strong grip on data insights to leverage the information residing in the organization for business benefit.
The stakeholders need to be clearly defined and the entire leadership team should be very clear on the goals, and the roadmap to reach them. It is essential to identify strategic initiatives that will bring all leadership stakeholders closer to their business goals.
An extensive brainstorming session with team inputs is a must to build a list of AI projects that can be evaluated. Innovative ideas and inputs from skilled resources about how they can be brought to fruition should be at the core of these discussions. Moving ahead with innovative tools is the only way to stay ahead of everyday technology implementations.
Delaying the ROI planning till after the project goes up live:
CIOs believe that most enterprises track ROI after the project goes live. C-suite leaders often settle for unclear outcomes like brand value, happier customers, or efficiency improvement. But smart CTOs and CIOs start evaluating the RoI in measurable outcomes, in stages, phases and levels, even before the project goes live and imparts business value.
Quantifying the dollar value of results is complicated, but completely doable.
In fact, CIOs must demand for outcome based value addition, benefits to the business even before approving the project. They understand that value could be either in increasing revenue or decreasing expenses.
Both the results are equally valuable, making it critical to define which of these outcomes will be achieved when the project is deployed. It is important to detect a blend of lagging and leading metrics that can help calculate these results. Collate the data required to calculate the metrics by creating new processes or updating existing ones.
As a final step, enterprises must track their investments by going beyond technology, software, and hardware costs. Such an ROI metric should be a critical factor when a project needs to be approved.
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Creating a conducive enterprise culture for an AI-driven transformation:
Enterprise leaders say that the biggest hurdle to gaining value from analytics is a culture of analysis, and not technology or data. If the organizational culture is not carefully molded, even the best laid AI project and strategy will fail.
Leaders acknowledge that such culture change must be initiated at the top level. They need to demonstrate to the organization how adopting AI strategies will be beneficial to the business in the long term.
If there are any doubts or misgivings that are driven by the popular culture around AI adoption, they need to be mandatorily and clearly addressed. Such a culture shift will take a couple of years, and leaders should be capable of influencing the projects long after they have gone live.
Megana Natarajan is a Global News Correspondent with OnDOt Media. She has experience in content creation and has previously created content for agriculture, travel, fashion, energy and markets. She has 3.9 years’ experience as a SAP consultant and is an Engineering graduate.
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