5 Reasons Why You Should Hire an AI Consultant

In recent years, at least partially spurred by the COVID crisis, many businesses have accelerated their plans for artificial intelligence adoption. (1)(2). It’s possible that you’ve already observed your contemporaries utilizing AI to great effect; maybe you’ve even spent the last few years gathering data and have some intuition about how it could transform your business. In any case, unless your core competency lies in artificial intelligence, you’d likely benefit from meeting with an expert to help formulate your strategy and bring your AI projects to fruition. 

If you’re serious about leveraging AI in your business, working with an experienced consultant can be a great place to start — in some cases, it may even be more viable than building your own data science team. Still, you may be wondering what specific benefits an AI consultant can bring to your business.

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1. Start And Launch Your Projects Faster




If you're eager to get your machine learning (ML) projects operational, a good consultant can get you fast results. For starters, you won’t have to go through a drawn-out hiring process. While you’ll still need to set aside some time to research and select the right consultant, this process can often be shorter than building an entire data science team.

Quickly perusing statistics released by some of the U.S.’s top universities, you will notice that most data science graduates receive full-time job offers within three months of graduation (3). With demand this high, it can often feel like you have to tap talent before you can even be sure of their technical abilities. Perhaps this is why the mantra “hire for talent, train for skills” has become so widespread. While some natural talent can be a good thing, you must consider the additional time spent onboarding an inexperienced employee. When you hire a consultant, not only can you drastically cut down on recruitment time, but you can also guarantee experience.

What’s more, you’ll benefit from your consultants' experience in a couple key ways. To begin with, good consultants know how to deliver results. They often have a wide body of previous work to reference, and can quickly determine what is feasible and what challenges you’re likely to encounter. Since they know what to look out for, a consultant can steer you away from impractical solutions, and unproductive time sinks.

On the more technical side, consultants tend to have a wider array of tools than the average data scientist. A consultant that’s worked on a lot of machine learning projects will likely have a reservoir of solution templates to draw on. Many data science consultants will work regularly with powerful though complex open source tools including modeling environments, ground truth annotation, work distribution and data pipelines. Some consultants may even have their own open source platform which can greatly accelerate getting an AI project off the ground while eliminating platform licensing fee concerns.

Since a consultant with a well equipped toolbox doesn't need to start every project from scratch, they can save a lot of time in the early development stage.




2. Gain an Outside Perspective




It’s a good practice to get an outside perspective at multiple points in your project’s life cycle. Early on, it’s worthwhile to bring on a consultant to sanity check your AI opportunities and help to determine those likely to provide the greatest return on investment. Later, when you have some ML capabilities, a consultant can use their outside perspective to troubleshoot problems or inefficiencies you may be experiencing. In the latter case, it can be difficult to know if you’re doing something the best way, especially when you’ve never seen it done differently.

Let’s work through an example. Think of a company stuck in a loop of tweaking and iterating an ML model. However, try as they might, the results continue to be suboptimal or otherwise fail to perform as intended. The company might think the solution lies in further algorithmic adjustments and sink more time into modeling. In actuality, they could be approaching the problem from the wrong direction. This often occurs when someone is too close to a problem to see the solution. A consultant, viewing the problem with fresh eyes, might deduce that the error actually stems from something else — poor training data, for instance.

You could also benefit from an outside perspective when you lack the requisite skills in-house. Returning to the previous example, consider now that the company needs to gather more training data to improve model efficacy. If they have enough time and money, they could attempt to label hundreds of thousands of new data entries; however, this is an incredibly inefficient way of doing things. A consultant specializing in finding optimal training data could perhaps, through a process of active learning, label 1,000 new entries and deliver comparable results in a fraction of the time. In this scenario, getting an outside perspective can be much more cost-effective than continuing to work exclusively in-house.




3. Reduce Costs




Building a data science team can be expensive. In recent years, demand for qualified data scientists has skyrocketed. With the median salary of a mid-level data scientist around $130,000 per year, and top-tier companies paying top-tier talent well north of $500,000 per year, it’s a great time to be a data scientist, but perhaps not the best time to hire one. Additionally, it takes more than a single data scientist to operationalize an ML model. A complete team often requires a data scientist, a backend software engineer, a DevOps engineer, and sometimes a data analyst, architect, or front-end developer.

When you hire a team of consultants, you may be able to meet these requirements for less than the cost of a single data scientist.

Hiring consultants may also be cheaper in the long run, since you can scale your team according to need. While you’ll often need a full team to get your basic infrastructure and ML capabilities up and running, once deployed, you may find that you only require a few members to keep your system maintained. Unless you resort to layoffs — which has its obvious downsides — it’s often not financially viable to build an entire data science team for one-off ML projects. Consultants, on the other hand, can afford businesses the flexibility to upscale, downsize or pause projects as needed.




4. Benefit from Deep Expertise




When you hire a consultant, you’re paying for experience. The breadth of experience covered by a consulting firm may exceed that which can be readily assembled in-house. However, a consultant is not the same as a generalist; that is, someone with a little bit of knowledge spread across many common domains. Instead, a consulting firm will likely hire a range of specialists with deep knowledge in specific fields. For example, a consulting firm could have a member whose expertise lies in natural language processing, another who is proficient in software engineering, and perhaps others proficient in computer vision, semi-structured business data, AI production design, etc.

Additionally, consultants should be familiar with a range of data types. More than structured or unstructured data — which in itself requires different specialization of skills to work with — , each industry has its own data type. Healthcare data, financial data, and educational data are all different, and can present unique challenges. A team of consultants with diverse specializations will likely know how to approach these disparate data types.

Consultants also bring a lot of practical development experience to the table. This is crucial, because many of the problems you are likely to encounter in a live deployment scenario are different from what you would encounter working on purely academic or research-oriented projects. For example, if a data scientist has only worked with experimental models, how can they be sure that their model will work as intended when deployed? Will the model effectively scale to handle thousands or millions of requests? Will they be able to account for latency issues? These are the sort of problems that benefit from the breadth of experience available to consultants.

Although technical knowledge and development experience are important, one final asset separates consultants from the average data scientist: business experience. You’d be surprised how many projects hit snags or stall out due to ineffective communication. In a way, it’s understandable since a representative for a business (e.g, a product manager) and a data scientist or engineer have entirely different frames of reference. However, an experienced consultant, especially one who has worked in a similar capacity to a chief scientist or chief technology officer, should be able to bridge the gap between a project’s business and technical aspects. They should understand what is essential for the business, the product, and the consumer and be able to communicate their own strategy and processes with clarity. The coupling of this uniquely business oriented perspective with the technical proficiency of a data specialist can provide great value to your business.




5. Learn how to run AI projects




By working closely with a consultant and observing first-hand the stages of the machine learning pipeline, you can better understand what it takes to run your own AI projects. To begin with, a consultant can teach you how to ask the right questions. A consultant might instruct you to consider where your employees are spending a lot of time on repetitive tasks, where your operation is not running smoothly, or where your customers are expressing dissatisfaction.

Answering these questions can help you identify AI opportunity candidates or areas where AI is likely to provide efficacy improvements to your business. Once you know what to look for, a consultant can give you a better idea of what’s feasible and how best to approach the development process.

A consultant may also help with capacity building — providing you with the tools, knowledge, and organizational improvements necessary to effectively maintain your newfound machine learning capabilities. If you are interested in building your own team, you’ll need some basic infrastructure for data collection in place; it’s much easier to attract good data scientists if you have good data for them to work with. A consultant can help with data collection and data wrangling, and launch some early models into the field. Once you achieve basic ML competence, a consultant can help you hire team members. Consultants should have a good idea of what roles you’ll need filled and what qualities and skills will be required. If you are inexperienced with giving technical interviews, a consultant might even offer to conduct them for you.

Lastly, when you’re ready to deploy your ML model, a seasoned ML practitioner can help you identify potential problems like model drift and help plan mitigations. Due to unpredictability or “drift” in a model’s operational environment and perhaps the world at large, it’s unlikely that your model will retain the same level of efficacy indefinitely. To keep your model’s predictions accurate, you will need to monitor its performance and retrain it with new data every so often. A consultant can educate you on these processes, and with any luck, you’ll be able to manage your ML projects for a long time to come.

Need help accelerating your AI project?

At Xyonix, we offer AI consulting services tailored for every phase of the AI lifecycle.

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