Remember the time when Artificial Intelligence was just a futuristic technology meant to be movie material? Today, AI is a mainstream technology driving almost all online interactions. As AI-driven everything becomes an enterprise staple, it becomes natural to extend the power of this technology to the learning and development department to drive powerful and contextual initiatives that deliver tangible results.
Some people believe that AI is the future of coaching, however, many are still skeptical as to what an AI-powered life coach really is and how is can change the future. Let’s find out in this article.
- What is an AI Coach?
- Why is AI Coach the Future?
- Can Coaching be Automated?
- 6 Ways to Automate your Coaching Workflow
- How Much does an AI Consultant Charge?
- What are the 4 AI Classes?
- What are the 4 Types of Coaching?
- What Type of Coach is Most in Demand?
- What is the Job Role of a AI Consultant?
- How Much does an AI Trainer Make?
What is an AI Coach?
One new form of AI is a new type of calibrated coaching. After completing a short computer-adaptive assessment, each learner can pick a goal and schedule short, tweet-sized suggestions for how and what they should practice. On the mornings and days they prefer, learners get a push notification they can use to suggest fresh ways they can sharpen their skills throughout their work day.
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Each e-coaching statement is written by an expert and calibrated like an assessment instrument, so that it can target that unique proficiency level of each learner. In this way, e-coaching is always in that person’s “Goldilocks Zone” – neither too hard, nor too easy, but just right for their current skill level.
Further, learners can schedule a second push notification on their smartphone or tablet to remind them to journal about the lessons they’ve learned from trying to apply the artificially intelligent coaching to their jobs. Most learners prefer to capture these private journal entries using the built-in voice-to-text like Siri on their smartphone, another form of AI that makes reflecting on deliberate practice more natural, especially when the learner is tired at the end of a long day.
This mobile reminder serves two key purposes. First, it is developmental for learners to reflect on what they learned and refine their mental models. Second, the journal gives the teacher or coach a private, confidential window into how that specific learner is doing, and whether he or she can benefit from praise, nudging or other forms of support. In the past, teachers and coaches could only do this through methods that felt like nagging by phone, SMS or email spam to learners.
A second new form of AI leverages computer science advances with deep learning to give people a sort of pocket flight simulator to allow people to practice new behaviors, get immediate assessment and qualitative feedback, and improve before having to perform real job tasks. In this way, the AI provides a safe place to practice difficult, dangerous or embarrassing skills in a private setting where only the coach or teacher can see the result through their web portal.
The first such flight simulator has just won the 2018 Society for Industrial-Organizational Psychology Bray/Howard Assessment Grant, for a prototype designed to measure and improve persuasion. Using the science from eminent professor emeritus, Robert Cialdini, of Arizona State University, the “Instant Coach” has a chatbot interface to help people improve their persuasive appeals before they need to do them in real life.
The Instant Coach flight simulator has four different modes. One mode allows the user to simply speak into their phone and get immediate feedback on how effectively they’ve used Cialdini’s Principles of Persuasion, and gives concrete suggestions for improvement.
Another group of modes are more structured to help a person who doesn’t know where to start, learn how to construct an ethical approach to influencing in a specific situation and measures how much progress they have made.
Because AI is developing rapidly, it is very likely that AI will continue to augment the context and relationship components that are important in training and coaching, to better mass-personalize people’s ability to transfer training into their real jobs and lives.
Why is AI Coach the Future?
Battle Information asymmetry
Until recently, coaches have worked on the principle of information asymmetry – a place where they have more information than the learners. However, the world of work is no longer holding the same avatar as a decade back. The enterprise is now a living, breathing organism that is evolving each day…and with this, its needs are evolving and changing.
Given the market forces and disruption at play, the rise of the remote workforce, gig economy and distributed teams, and rising competition, information asymmetry has to reduce to drive better coaching outcomes.
While coaches will have better coaching information, that information has to be relevant and contextual to the learner. For this, it is as important to have accurate and precise information regarding the learner and clarity on their learning needs.
At the same time, the learners have to be aware of where their learning and coaching needs lie, how these efforts translate into progress, and how that helps them progress in their job roles.
AI-enabled coaching platforms help battle this information asymmetry by providing deep and accurate insights that help build a better understanding of how to drive coaching outcomes. They can help organizations drive phenomenal business results by identifying accurate coaching needs and powering better coaching conversations.
Smart coach-learner pairing
“When the student is ready, the teacher will appear”.
While this is a good philosophy to go by, in the enterprise context this doesn’t hold water.
With constant change becoming a staple, enterprises need to increase their agility and flexibility to adapt to change. The role of critical skills such as communication and collaboration are also becoming more pronounced owing to the rise of remote working.
A sharp focus on reskilling, upskilling, and associated learning and development activities become natural outcomes of this climate of change. However, to drive elevated coaching outcomes, it is imperative to have the right coach-learner pairing. After all, this determines the quality of coaching conversations.
While organizations realize the importance of the right coach-learner pairing, many still rely on archaic guesswork to enable this. Unless the pairing is right, the learning interactions and the engagement is not going to be impactful and will be unable to drive powerful results.
An AI-enabled platform alleviates this concern and makes it easier for organizations to drive transformational coaching programs. Such a platform uses advanced technology to make the right coach-learner pairings taking the learning needs and other variables at play. This approach makes it possible for enterprises to pair the right coach with the right learner, and make sure that enablement reaches their workforce when they need it.
Build a thriving internal coaching culture
The number one HR priority for most organizations this year is to create an internal coaching culture to drive organizational and employee agility. Skill development initiatives have to become more future-focused and dynamic. These initiatives have to ensure that employees develop the right skills at the right time and contribute to the development of a robust leadership pipeline.
Integrating coaching in the workplace culture is the only way to achieve goals while driving lasting change. However, to create the right coaching culture, enterprises have to replace guesswork with accurate data to identify the current and future needs of their workforce.
An AI-enabled coaching platform gives enterprises the capacity to identify potential coaches from their internal employees and assess their skill sets versus coaching needs. Using this knowledge, organizations can create an army of internal coaches and build a thriving internal coaching culture.
Drive performance management with coaching
The days of the end-of-year annual review are behind us. The millennials and Gen Z (the dominant workforce demographic) need timely and action-oriented feedback at regular intervals. Whether it is to identify performance gaps proactively or to identify new skill development opportunities to augment career paths, organizations now need to make feedback a proactive mechanism.
Optimizing performance management systems to help accurately identify top performers and high potential employees are also organizational prerogatives. Data-backed insights into both technical and critical skill sets are essential to drive organizational outcomes by ensuring that employees are on the right upskilling, reskilling, or critical skill enhancement trajectory.
An AI-enabled coaching platform helps to drive performance management with coaching and optimizes coaching interactions by providing timely and contextual nudges. The technology also gives organizations the insights they need to identify their high-potential employees and top performers and assess who should be moved into the leadership pipeline to make it more robust and vibrant.
Manage the diversity chasm
Organizations with diverse teams outperform non-diverse teams by 35%.
Diversity is the enabler of innovation and creativity and a factor that builds empathy into the teams’ fabric. To reap the benefits of diversity, these policies have to align directly with organizational goals.
However, strong coaching programs are essential to drive diversity initiatives and to make sure that they stick. For this, it is essential to make the right coach-learner pairing, assess where diversity initiatives are lacking, identify exact skill areas that employees need help with, and develop critical skills like empathy that link back directly to diversity.
But how do organizations make sure that they are not linking their diversity initiatives to become mere affinity programs within organizations? Employing an AI-enabled coaching platform can help organizations build their diversity initiatives by creating transformational relationships that elevate the intellect by addressing relevant and contextual concerns and challenges.
Finally, and perhaps, most importantly, an AI-driven coaching platform helps organizations build contextual and relevant coaching programs by providing clear insights into performance gaps.
With clear insights, organizations can jumpstart relevant company-wide coaching programs, provide actionable insights into skills development, performance, employee engagement, and much more. With overall enterprise transformational insights, an AI-powered platform helps organizations proactively identify and add new skills to address their growing needs.
Can Coaching be Automated?
You’re busy running coaching sessions, onboarding new clients, creating new programs, managing social media…the list is endless! And even though coaching is a fantastic business model, you’ll burn out if you’re always trading time for money.
An automated coaching business lets you step back from work without compromising profits. Grow your income without raising your rates: Automating tasks frees up your time to take on more coaching students and grow your income without raising your coaching prices.
There are four reasons why you should automate your coaching business:
- Stop trading time for money: As a one-person show, there’s a good chance you make money when you’re actively coaching students. An automated coaching business lets you step back from work without compromising profits.
- Grow your income without raising your rates: Automating tasks frees up your time to take on more coaching students and grow your income without raising your coaching prices.
- No need to hire help: Whether you’re just starting your coaching business or you’re a seasoned coach, you might not want to hire a team just yet. Automations grow your business without hiring for help.
- Set it and forget it: Once you set your automations, they’ll continuously run until you stop them, bringing in new clients and making sales even while you sleep.
An automated coaching business helps you step back from mundane tasks to focus your energy where it counts.
6 Ways to Automate your Coaching Workflow
If you’re running a coaching or consulting business, you are likely juggling a few thousand things on any given day. Ok, maybe a slight exaggeration, but it can definitely feel overwhelming as all of your competing priorities stack up. You’re of course speaking with clients, but you’re also marketing, sending invoices and contracts, engaging in sales activities to bring in new business, and networking with other professionals.
Below are six simple automation workflows that you can use right now in your coaching or consulting business. They cover everything from email list building and onboarding to courses, events, and webinars.
The Lead Magnet Flow
Lead magnets are useful resources or content that your audience opts in to receive. They’re a great way of building your email list, as most people are happy to trade their email address for a great resource (and will be happy to receive further high quality resources from you in the future).
Many coaches and consultants develop different lead magnets for different segments of their audience, so you may end up with several versions of this workflow. Just make sure to add a step that tags users in your CRM with which download they opted in for, so that you can later use those segments to provide relevant content.
This workflow usually drops customers into a nurture sequence that follows up on the lessons from the lead magnet. I like to work backwards for this. For instance, if your ultimate goal is to sell your “30 days to mindful nutrition” coaching package (imaginary, but sounds fabulous), then you could design your nurture sequence to build up the questions, pain points, and needs that someone would need to have to buy that course.
So maybe you include nurture emails on “incremental diet changes”, “why mindful eating makes diet changes last”, and your “top 5 tips for bringing awareness into your diet”. Ok, so now I’ve established the benefits and basics for my mindful nutrition program, and should have people ready to think about a coaching package. Working backwards one more step, I need to bring my audience from awareness to consideration. Maybe a downloadable pdf of my “top 5 recipes for mindful eating”?
Working backwards with your lead magnets from the end goal can give you a really idea of what to provide – and as always, you can ask your customers what they want to see!
The Contract Flow
Before a prospect becomes a client, most coaches have some form of contracting workflow. Individual steps will of course vary, but usually include a discovery call of some kind, a proposal, a contract (sometimes combined with the proposal), and a first invoice. This administrative flow can be simplified and sped up with the addition of automation.
After the discovery call, the coach simply needs to enter relevant information into their CRM, including project length, project type, and any other details they’ll need to generate a proposal and invoice. If they break their services up into packages this can be as simple as choosing the package, which would then generate the rest of the necessary information.
This information is pulled into the proposal, contract, and invoice, and sent automatically in sequence as the client completes the necessary steps. At the end of this the “prospect” is officially tagged as a “client”, and the onboarding can begin!
The Onboarding Flow
Most coaches have some onboarding process for their clients, but the best ones have a process that gets the client excited about the coaching process, sets expectations appropriately, and ensures that both the coach and the client are ready to get started, all while making the coach look professional and prepared.
That’s a lot to ask of a welcome email, and one giant email with all that information could be overwhelming for the client. This onboarding flow is put together from my own client onboarding, as well as a few wonderful coaches and consultants with larger teams and more formal onboarding needs.
As soon as you tag a prospect as a client in your CRM, either as a result of your contract workflow or because of a manual switch, a welcome video is sent to the client. This is a great way to start things off, as you can inject lots of energy and excitement into the video and talk authentically about how you like to work with clients and why you do what you do.
Next comes the “what to expect” email with information on the coaching process, what your client will gain from the experience, and how to reach you with questions. You can then introduce your team (if it’s more than just you) and explain who to reach out to for different kinds of support.
Next is an invite to any private support groups you may have, a summary of your schedule with the client, and then an email listing anything that you need from them to get the process started. Finally, the “all in one place” email summarizes all the information from throughout the onboarding process, so that the client (literally) has it all in one place.
This email would be overwhelming if sent at the beginning of the process, but at the end it’s a great way to summarize and highlight key points.
The Course Sales Flow
If you’re a coach or consultant, there’s a good chance that at some point you’ll create an online course to share your expertise with the world. This flow follows a simple customer journey from your website, through a lead magnet like an ebook, and then to your sales page. Depending on the time and money commitment required for your course, you may need more steps or additional pieces in your workflow to inform your audience about course benefits and get people excited.
Some steps you could include after the eBook download would be a mini-course delivered through email, a video series highlighting benefits and case studies from your course, or a webinar teaching a small part of the course and then encouraging viewers to sign up for the full course.
The Event Sales Flow
Events are a big part of marketing and branding for many coaches, especially those in executive coaching, wellness, and entrepreneurship. This flow is designed for marketing an event that you’re hosting, but it can easily be adapted to raise awareness for an event you’re attending, speaking at, or sponsoring.
You can set your event automations to end right before the event starts, with a hard stop on all of your triggers at that point. That way, you can continue reaching out (in an automated way) to anyone who hasn’t yet signed up without worrying that your email will go out after the signup window closes.
The “top” path above, where the customer purchases an event ticket, then leads into a separate flow where the person is given all the necessary information for the event, encouraged to share it with others, and any other calls to action that are important for your event.
Keeping the “event interest” and “event purchase” tags separate means that you can identify members of your audience who might want to get involved the next time you do a similar event, or who may be swayed by a discount code for other events in the future.
The Webinar Flow
Webinars are a great way of engaging with your audience, sharing your expertise, and booking new clients for coaching and consulting services. This flow takes your audience from the signup process through a couple of reminders, and through the sales process afterwards. As with your course workflow, you may need more steps before or after the webinar depending on the commitment needed for both the webinar and for the pitch (whatever you’re asking at the end of the webinar).
How Much does an AI Consultant Charge?
The average machine learning consulting rate depends on the pricing model of your consultant. Hourly consultants often charge an hourly rate of $250 to $350. In comparison, flat-rate consultants cost $5000 to $7000 per project.
|$250-$350 / hour
|$5000-$7000 / project
While deep learning consulting rates may seem straightforward, especially when looking at average prices, several factors determine the cost of machine learning consulting. Understanding these factors is critical, whether you’re looking to hire a consultant or work as one.
What determines machine learning consulting rates?
No matter the field, experience impacts the cost of a product or service. In most cases, data scientists offering machine learning consulting services will base their hourly or flat rate off their background and expertise.
If they have less than four years of experience, for example, they may charge $250 or less per hour. In comparison, a data scientist with five or more years of experience will probably cost closer to $350 per hour or $7000 per project.
Agencies that offer machine learning consulting services will often base their data science consulting rates on the experience of their agency. That’s because a well-known, more experienced agency can usually attract top talent.
For companies looking to hire a machine learning consultant, it’s worth making experience a critical factor in your hiring decision. You want to work with someone that can deliver results, without causing you or your team a headache, especially if you have zero familiarity with machine learning.
Machine learning consulting rates also depend on your project’s scope.
A larger, more complicated project will often equal a higher consulting rate. That’s because a data scientist or machine learning consultant will have to invest more time into setting up and implementing your solution architecture.
Even a flat-rate consultant will increase their prices in response to the work required.
A common challenge for many consultants or contractors, however, is that many companies or clients aren’t sure about what they need or want. It’s understandable, too, as the details of machine learning can be muddy for professionals without a big data background.
Contractors can adapt to this challenge, as well as help potential clients by chatting with the company’s team, learning about what they need and want, and then compiling a proposal with a price. Accepting the job (or even hiring the consultant) before receiving a plan will often cost time and money later.
From a client perspective, working with a contractor that develops a proposal first serves as an excellent trust signal. They want to outline, as well as reach an agreement with your team, about what your company wants before they begin or accept the project.
Plus, these consultants want to provide you with an accurate machine learning consulting rate.
Result expectations are another pricing component when it comes to big data consulting rates.
In big data, result expectations describe what you expect when it comes to a machine learning project’s:
- Accuracy: The proportion of the total number of right predictions.
- Precision: The proportion of positive cases identified correctly.
- Recall: The proportion of actual positive cases identified correctly.
- F-score: The mean of your precision and recall values.
For many clients, these terms can seem foreign. It’s up to the consultant to explain these phrases in a way that makes sense. F-score, for example, describes the balance between precision and recall, which focus on the exactness and completeness of a classifier.
If you’re operating as a contractor, you may receive requests from potential clients that have zero experience when it comes to result expectations. Again, talking with potential clients and building proposals can help create accurate project plans and prices.
In big data, quality is a critical factor.
Businesses want to receive accurate and precise data. That kind of data, however, depends on a large dataset. In some cases, companies may desire high-quality results but provide small datasets. Or, they may request impossible quality standards.
Depending on the requirements quality, machine learning consulting rates may increase.
For example, a business that wants accurate results, but provides a small data set, may need to invest more time and money building its dataset. That way, the contractor can deliver on the client’s requirements quality.
As a client, setting a lower requirements quality will not decrease your machine learning consulting rates. If you want to use a lower requirements quality, it’s essential to think of how that will impact your results and any actions in response to your results.
Big data learning consulting rates also depend on the solution architecture.
For reference, solution architecture refers to an entire system or specific parts of a system for delivering a solution. It’s a critical component in using machine learning, as solution architecture often integrates with business, information, and application architecture.
MarketingCloudFX (from WebFX) is an example of a solution architecture.
A complex solution architecture can require more hours from a consultant. It can also increase the scope of a project, which can influence the flat rate charged by a contractor. Not to mention, a more sophisticated solution architecture can require the expertise of a more experienced data scientist.
If searching for a faster, more immediate solution, companies can use a pre-built and customizable solution like MarketingCloudFX. With the artificial intelligence of IBM Watson and machine learning capabilities, MarketingCloudFX is a go-to solution for companies exploring machine learning.
For perspective, MarketingCloudFX costs $229 to $999 per month.
Again, consultants and clients need to discuss the project in-depth (and develop a proposal) to determine the solution architecture required. This initial work can help streamline the project, provide accurate pricing, and ensure that the consultant and client are a good match.
What are the 4 AI Classes?
We can classify artificial intelligence into 4 distinct types. The types are loosely similar to Maslov’s hierarchy of needs, where the simplest level only requires basic functioning and the most advanced level is the Mohammad, Buddha, Christian Saint, all-knowing, all-seeing, self-aware consciousness.
Reactive Machines perform basic operations. This level of A.I. is the simplest. These types react to some input with some output. There is no learning that occurs. This is the first stage to any A.I. system. A machine learning that takes a human face as input and outputs a box around the face to identify it as a face is a simple, reactive machine. The model stores no inputs, it performs no learning.
Static machine learning models are reactive machines. Their architecture is the simplest and they can be found on GitHub repos across the web. These models can be downloaded, traded, passed around and loaded into a developer’s toolkit with ease.
Limited memory types refer to an A.I.’s ability to store previous data and/or predictions, using that data to make better predictions. With Limited Memory, machine learning architecture becomes a little more complex. Every machine learning model requires limited memory to be created, but the model can get deployed as a reactive machine type.
There are three major kinds of machine learning models that achieve this Limited Memory type:
These models learn to make better predictions through many cycles of trial and error. This kind of model is used to teach computers how to play games like Chess, Go, and DOTA2.
Long Short Term Memory (LSTMs)
Researchers intuited that past data would help predict the next items in sequences, particularly in language, so they developed a model that used what was called the Long Short Term Memory. For predicting the next elements in a sequence, the LSTM tags more recent information as more important and items further in the past as less important.
Evolutionary Generative Adversarial Networks (E-GAN)
The E-GAN has memory such that it evolves at every evolution. The model produces a kind of growing thing. Growing things don’t take the same path every time, the paths get to be slightly modified because statistics is a math of chance, not a math of exactness. In the modifications, the model may find a better path, a path of least resistance. The next generation of the model mutates and evolves towards the path its ancestor found in error.
In a way, the E-GAN creates a simulation similar to how humans have evolved on this planet. Each child, in perfect, successful reproduction, is better equipped to live an extraordinary life than its parent.
What are the 4 Types of Coaching?
We often hear a lot about it, but what exactly is coaching in the workplace? It’s a training method that prompts leaders to clearly address objectives and support their direct reports, giving them space to communicate and receive guidance.
In fact, coaching is a proven method for increasing individual performance, but organizations are moving beyond the “one coach, one executive” approach. Instead, teams are considering multiple coaching approaches to drive accountability, development, and performance at all levels.
Here are 4 types of coaching in the workplace that you and your organization should consider:
1. Executive Coaching
Executive leadership coaching is one of the most common and widely understood types of coaching in the workplace. It’s an effective way to strengthen the performance of your most important leaders, assist them in making key transitions, and enable them to alter behaviors that may be hindering their performance.
Executive leadership coaching typically kicks off with a matching process to ensure a good fit between the coach and the participant, followed by one or more assessments and alignment meetings with key stakeholders.
During the coaching engagement, the coach may help the executive understand and use information from assessments, create and work through a development plan, and address specific business and interpersonal challenges.
The personal, supportive environment provided by an executive coach can foster new ways of thinking, acting, and influencing to achieve significant business results.
2. Integrated Coaching
Integrated coaching is an approach that embeds coaching sessions into — or wrapped around — a broader leadership development program or initiative. It can reaffirm and reinforce lessons learned in leadership training.
For example, an organization running a development program for high-potential, mid-level managers might include a coaching element — or a series of 2-5 coaching sessions — designed to help participants in the program reflect, deepen, and apply what they’re learning in the development experience.
Though often over a shorter term than executive coaching engagements, this type of coaching in the workplace can help ensure that leadership development learnings “stick.”
3. Team Coaching
Team coaching is effective at all levels — from the C-suite to front-line teams. It’s another key type of coaching in the workplace because even high-performing individuals can sometimes struggle to work together effectively.
Team coaching includes a variety of methodologies and formats aimed at fostering healthy interactions and high performance.
These may be fairly structured and prescriptive, such as during a retreat where a coach has worked with the team’s leadership to create the agenda and then facilitates the meeting, possibly even teaching content.
Team coaching may also include methods that are less scripted, such as helping a project team interact more effectively or facilitating a process that evolves in unplanned ways. Sometimes a coach may observe a team in its normal work environment and provide coaching based on those observations.
4. Virtual Coaching
Virtual coaching is now the most common type of coaching in the workplace. Even before the recent spike in working remotely, organizations were becoming more global, virtual meetings were becoming more prevalent, and virtual coaching was on the rise.
Now, this type of coaching has become totally commonplace, and all of the previously mentioned types of coaching in the workplace — executive, integrated, and team coaching — can be delivered virtually.
Virtual coaching is an ideal option for teams that span countries and time zones, as well as for those interested in a coaching arrangement they can easily integrate into their hectic schedules. Through the use of video, a virtual coach is able to engage and facilitate in the same manner they would in a face-to-face setting. Additionally, the coach matching process is not limited to geographic and travel constraints, which often increases compatibility and flexibility.
What Type of Coach is Most in Demand?
1. Career coaching
Career coaching helps people advance in their careers. As a career coach, you offer guidance on different career-related challenges, such as orienting a career transition, finding a dream job, getting a raise, and getting a promotion.
2. Health coaching
A health coach helps people improve their health. For example, you might help clients do this with a specific diet or a work-out regimen you’ve developed.
3. Life coaching
Life coaching as a niche is too broad, but I’m listing it here to help you get a better understanding of what niche you can narrow down to if you want to help clients improve their life. The thing is: You should be more specific. What goal are you trying to help them with? What problem are you solving? That’s your life coaching niche.
4. Mindset coaching
A mindset coach helps people improve their mindset and overcome their limiting beliefs and that way, achieve their goals. You can be a mindset coach for a specific type of person or a niche.
5. Financial coaching
As a financial coach, you help people improve their personal finances. This can be through guiding people on how to use their money, investing, or making more money.
6. Relationship coaching
Relationship or dating coaching is all about helping people find better relationships.
7. Weight loss coaching
As a weight loss coach, you help people lose weight. You can use your own methodology or focus on a specific niche of people (over 40s or women).
8. Nutrition coaching
A nutrition coach helps people eat better. All types of people are interested in nutrition coaching. For example, people who have allergies, who want to lose weight, or who want to eat a plant-based diet.
9. Public speaking coaching
Public speaking coaching helps people improve their speaking skills.
10. Productivity coaching
As a productivity coach, you help people reach their goals by becoming more effective and efficient.
What is the Job Role of a AI Consultant?
The job opportunities for Artificial Intelligence specialists are broad, meaning there are a variety of routes for a specialist to take. However, all routes have one similarity, specialist’s program computers to comprehend a diversity of situations. One role for an AI specialist is to program computers to test hypotheses in relation to how the human mind works, through cognitive simulation.
An example of this can be seen in Cognitecs ‘FACEVACS’ technology. They use AI for security purposes, to recognise faces and identify whether the person is who they claim to be. The majority of AI specialists work in applied AI, their purpose is to program computer smart systems. These systems cover a number of aspects, ranging from recognizing voices to solving complicated problems.
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Furthermore, the role of an Artificial Intelligence specialist is to enhance the offerings and operations within a number of industries. Specialists have already made an impact in the financial, health and sport industries, but also have the potential to impact the law and geology sectors just to name a few. As there are many more, this makes the role of an artificial intelligence specialist flexible.
Key Roles and Responsibilities:
- Engage with clients (public sector and commercial enterprises) in designing solutions which will deliver real business value leveraging cutting edge AI & Intelligent Automation technologies
- Contribute to pre-sales, delivering presentations and crafting of AI solutions – develop proposals to help our clients understand proposed solutions and their business impact
- Formulate and deploy AI strategies for clients which include creating an AI blueprint & roadmap, setting up and operating AI COE.
- Create thought leadership that articulate our perspective on AI & Intelligent Automation in the industry and for our clients.
- Develop tools and methodologies for the practical application of AI to address business problems
- Staying updated with key technology and industry trends
How Much does an AI Trainer Make?
The average annual pay for an Ai Trainer in the United States is $95,309 a year. Just in case you need a simple salary calculator, that works out to be approximately $45.82 an hour. This is the equivalent of $1,832/week or $7,942/month.
While ZipRecruiter is seeing annual salaries as high as $196,500 and as low as $20,500, the majority of AI Trainer salaries currently range between $45,000 (25th percentile) to $139,000 (75th percentile) with top earners (90th percentile) making $174,000 annually across the United States.
The average pay range for an AI Trainer varies greatly (by as much as $94,000), which suggests there may be many opportunities for advancement and increased pay based on skill level, location and years of experience.
The use of AI in coaching is always helpful where AI can really add value compared to real coaches. This is especially the case in the area of speech and pattern recognition, location analysis, bias detection, and unlimited capacity. AI-based assessment tools allow users to receive daily notifications based on their personal assessments and reflect on content learned via reminder features.
AI enables coachees to access coaching as a low-threshold offering in a low-risk environment on their own schedule. An AI-powered suggestion system can offer coachees further suggestions based on their previous sessions, such as working on beliefs after uncovering inner conflicts. Human coaches can also use AI to regularly track changes in a user’s behavior, as it can more easily detect these deviations in behavior.
Bias in coaching is something we as coaches are always confronted with and should be aware of. Studies suggest that objectivity is not part of the human condition; that our minds are not capable of being completely objective. Our perceptions are always interwoven with our understanding of the world, our own history, our past, etc. One of the most common biases in coaching is the “confirmation bias.”
In this, the coach attempts to confirm his or her initial hypothesis regarding the coachee’s issue and, as part of this, risks not capturing the coachee’s whole story or point of view. This type of bias can be counteracted through the use of AI when algorithms are created on a high-quality, diverse, and unbiased data set.