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5 Critical Success Factors for AI Projects

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With more users getting on to the internet, there’s a noticeable rise in searches for growth prospects. And one such bubble employers and employees are looking to penetrate, is Artificial Intelligence.

Whether you’re a technology enthusiast, or a business owner looking to augment your digital stack, there’s a reason for the burgeoning interest in the hand AI applications have in improving workforce productivity and process efficiency, while lowering operational costs.

Healthcare, for instance, is already using AI to interpret patient data, populate appointment calendars and medical charts, send out prescription refill reminders and helping clinicians take informed decisions for at-risk patients.

What’s more, it connects patients to hospitals, ensuring people avail periodic checkups for insights into their health.

By nature, AI projects are challenging and can misfire with the smallest oversight, what with data sensitivity and concerns of security breaches.

But knowing what an AI project’s critical success factors are lets you drive them to completion successfully. Here are 5 worth looking into;

Skills database management

The backbone to any Artificial Intelligence project is a technically competent workforce possessing specialist capabilities. Identifying and acquiring these experts is critical to scheduling work, given the disproportionate gap between talent and demand. Even with shifts in demand driving the creation of new roles, the level of expertise and type of skills remain insufficient.

In fact, 2018’s UK Engineering report revealed that over 200,000 human resources with engineering skills will be needed every year to meet the demand.

This problem is resolved with the usage of resource management software. Besides simplifying the process of creating business-wide project schedules, a tool to manage the resources hired lets your staff projects with the right and available skills.

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What’s more, you can size up project statuses from a bird’s eye-view, and release additional staff to projects that contain one or more time-sensitive dependencies. After all, it’s easier to track priorities across inflight projects with an overview of the competencies being mobilized.

Prioritize outcomes over outputs

No two businesses think alike, and the outcomes of an AI initiative can be a key differentiator in taking your company to the next level. Choosing to prioritize outcomes over outputs let you align the benefits strategically to your business objectives.

For some, it’s about getting a boost in sales from creating more marketable products. For others, it would be increasing customer satisfaction with buyer-specific loyalty programs. The outcomes of an AI project inform you of the attributes of its success. You can then apply these insights into future goals and establish a baseline for efficiency improvement.

Strategic communication

Plying your project teams with relevant information is a critical success factor, given that siloed data puts up an unnecessary communication barrier. Besides giving business context, keeping the lines of communication open between you and team members lets you know who is working on what, what will be requested for and by when you can expect work to be completed. What’s more, team dynamics would center around collaborative knowledge transfers and learning endeavors.

AI projects require strong statistical, computing and data science skills, which should ideally be available in line with the type and quantity of demand. By strategizing communication, you are informed of absences and skills gaps that impact the team composition and size. This can help you level out workloads according to the talent pool’s seasonality.

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Your team members, in turn, are aware of how their schedules are programmed and can contribute to future projects. Installing messaging platforms on multiple devices and encouraging your workers to bring their own devices to connect them to the workplace, thereby ensuring updates pertaining to their work aren’t missed.

Performance metrics

The confidence in a project increases when it proves to meet the benchmark of what constitutes acceptable quality. While newer models are being run, some AI projects do reuse bits of machine learning algorithms and repurpose its functioning for newer aspects. For work that makes use of scalable technology, a scorecard is a critical success factor to understand which AI initiatives are performing as expected, or are deviating from the norm.

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After all, you cannot improve what you don’t measure. With performance metrics, you will know which areas of the project are under or over performing.

For example, if the opportunity costs to procure a technology outweighs any benefits it promises, you can sidestep that particular activity and avoid wasting effort investments. The bandwidth could then go into a program that does work and has an actual, and systematic benefits realization scheme.

Consult domain experts

AI projects feed on soft data and the market trends captured from collaborative tools. A business with several departments, such as human resources and operations all the way to sales and marketing would have a sizable data sample to collect, process and act upon. By involving domain experts from diverse fields, you can benefit from a combination of their experiential judgment and data analytics to understand company data better.

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What’s more, you can weed out risks that have cropped up on previous AI initiatives, measures are taken to mitigate them and how successful projects were planned within the boundaries defined.

Plugging a domain expert’s consultation into every stage of the AI lifecycle lets you resolve technical and business bottlenecks. Not only can they evaluate AI models and test them but can also provide you with predictions into what product or service line changes will need to be made in line with user behavior. Market uncertainties then become less of a threat and more of an opportunity, given the timeliness and accuracy of the information you work with.

Got the data and information system? Let us know how useful these critical success factors were in extracting value out of your AI projects!

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