15 Applications of Artificial Intelligence in Marketing

 

Mapping the most effective AI technologies for marketing across the customer lifecycle in the coming year

Artificial Intelligence (AI) technology is a hot topic in marketing at the moment, with the huge interest in ChatGPT (see our articles on using ChatGPT for marketing).

But AI is a broad term covering a wide range of different technologies, many of which have been available for some time. We originally wrote this article based on the opportunities for using AI for marketing in 2017 and have updated it since with examples.

Artificial intelligence means any technology that seeks to mimic human intelligence, which covers a huge range of capabilities such as voice and image recognition, machine learning techniques and semantic search.

That's why, in our AI and Machine Learning briefing for members, we have identified fifteen artificial intelligence techniques that businesses of all sizes can implement, rather than techniques that only major tech giants can devote resources to. We've plotted the techniques across the customer lifecycle so you can see how each AI tactic can help take your customers down the marketing funnel.

All the techniques are 'AI' in the sense that they involve computer intelligence, but we've broken them down into 3 different types of technology - Machine learning techniques, applied propensity models, and AI applications. Machine learning techniques involve using algorithms to 'learn' from historical data sets, which can then create propensity models. Applied propensity models are when these propensity models are put to work predicting given events- such as scoring leads based on their likelihood to convert. AI applications are other forms of AI, which do tasks one would usually associate with a human operator such as answering customer questions or writing new content.

Each different application has major implications for marketers, but the applications have different roles to play across the customer journey. Some are better for attracting customers, whilst others are useful for conversion or re-engaging past customers. That's why we've divided the techniques across the RACE framework.

If you're looking to apply technology to optimize your marketing strategy, our RACE Framework is the perfect structure for you to plan and assess your marketing activities. Through the journey of reach, act, convert, and engage, our RACE Framework empowers marketers and managers to apply customer insights and data to measure and prioritize their best-performing omnichannel touchpoints.

Reach - Attract visitors with a range of inbound techniques

Reach involves using techniques such as content marketing, SEO and other 'earned media' to bring visitors to your site and start them on the buyer's journey. AI and applied propensity models can be used at this stage to attract more visitors and provide those that do reach your site with a more engaging experience.

1. AI-generated content

This is a really interesting area for AI. AI can't write a political opinion column or a blog post on industry-specific best practice advice, but there are certain areas where AI-generated content can be useful and help draw visitors to your site.

For certain functions AI content writing programs are able to pick elements from a dataset and structure a ‘human-sounding’ article. An AI writing program called ‘WordSmith’ produced 1.5 billion pieces of content as early as 2016.

AI writers are useful for reporting on regular, data-focused events. Examples include quarterly earnings reports, sports matches, and market data. If you operate in a relevant niche such as financial services, then AI-generated content could form a useful component of your content marketing strategy. There are now solutions from vendors available and returning good results in copywriting for Facebook Ads, email subject lines and nurturing emails that we highlight in the latest edition of our AI guide.

2. Smart content curation

AI-powered content curation allows you to better engage visitors to your site by showing them content relevant to them. This technique is most commonly found in the 'customers who bought X also bought Y' section on many sites, but can also be applied to blog content and personalizing site messaging more widely. It's also a great technique for subscription businesses, where the more someone uses the service, the more data the machine learning algorithm has to use and the better the recommendations of content become. Think of Netflix's recommendation system being able to consistently recommend to you shows you'd be interested it.

3. Voice search

Voice search is another AI technology but, when it comes to using it for marketing, it's about utilizing the technology developed by the major players (Google, Amazon, Apple) rather than developing your own capability. Voice search will change future SEO strategies, and brands need to keep up. A brand that nails voice search will leverage gains in organic traffic with high purchase intent thanks to increased voice search traffic due to AI-driven virtual personal assistants.

4. Programmatic media buying

Programmatic media buying can use propensity models generated by machine learning algorithms to more effectively target ads at the most relevant customers. Programmatic ads need to get smarter in the wake of Google's brand safety scandal. It was revealed ads placed programmatically through Google's ad network were appearing on terrorist's websites. AI can help here by recognizing questionable sites and removing them from the list of sites ads can be placed on.

By increasing your reach through the strategic implementation of AI, you are filling the top of your marketing funnel and giving yourself the best chance of success in marketing. Find out how you can use the RACE Framework to win more customers.

Act - Draw visitors in and make them aware of your product

5. Propensity modelling

As already mentioned, propensity modelling is the goal of a machine learning project. The machine learning algorithm is fed large amounts of historical data, and it uses this data to create a propensity model which (in theory) is able to make accurate predictions about the real world. The simple diagram below shows the stages of this process.

6. Predictive analytics

Propensity modelling can be applied to a number of different areas, such as predicting the likely hood of a given customer to convert, predicting what price a customer is likely to convert at, or what customers are most likely to make repeat purchases.  This application is called predictive analytics because it uses analytics data to make predictions about how customers behave. The key thing to remember is that a propensity model is only as good as the data provided to create it, so if there are errors in your data or a high level of randomness, it will be unable to make accurate predictions.

7. Lead scoring

Propensity models generated by machine learning can be trained to score leads based on certain criteria so that your sales team can establish how 'hot' a given lead is, and if they are worth devoting time to. This can be particularly important in B2B businesses with consultative sales processes, where each sale takes a considerable amount of time on the part of the sales team. By contacting the most relevant leads, the sales team can save time and concentrate their energy where it is most effective. The insights into a lead's propensity to buy can also be used to target sales and discounts where they are most effective.

8. Ad targeting

Machine learning algorithms can run through vast amounts of historical data to establish which ads perform best on which people and at what stage in the buying process. Using this data they can serve them with the most effective content at the right time. By using machine learning to constantly optimize thousands of variables you can achieve more effective ad placement and content than traditional methods. However, you'll still need humans to do the creative parts!

By increasing customer interaction on your site through the strategic implementation of AI, you are nurturing cold traffic to increase their likelihood of converting. Find out how you can use the RACE Framework to win more customers.

Convert - nudge interested consumers into becoming customers

9. Dynamic pricing

All marketers know that sales are effective at shifting more product. Discounts are extremely powerful, but they can also hurt your bottom line. If you make twice as many sales with a two-thirds smaller margin, you've made less profit than you would have if you didn't have a sale.

Sales are so effective because they get people to buy your product that previously wouldn't have considered themselves able to justify the cost of the purchase. But they also mean people that would have paid the higher price pay less than they would have.

Dynamic pricing can avoid this problem, by targeting only special offers only at those likely to need them in order to convert. Machine learning can build a propensity model of which traits show a customer is likely to need an offer to convert, and which are likely to convert without the need for an offer. This means you can increase sales whilst not reducing your profit margins by much, thus maximizing profits.

10. Web and app personalisation

Using a propensity model to predict a customer's stage in the buyer's journey can let you serve that customer, either on an app or on a web page, with the most relevant content. If someone is still new to a site, content that informs them and keeps them interested will be most effective, whilst if they have visited many times and are clearly interested in the product then more in-depth content about a product's benefits will perform better.

11. Chatbots

Chatbots mimic human intelligence by being able to interpret consumer’s queries and complete orders for them. You might think chatbots are extremely difficult to develop and only huge brands with massive budgets will be able to use them. Actually, using open chatbot development platforms, it's relatively easy to create your own chatbot without a big team of developers.

Our AI guide highlights open source technologies from Facebook and Google which brands such as Dominos and KLM are using for developing their own chatbots for customer service.

12. Re-targeting

Much like with ad targeting, machine learning can be used to establish what content is most likely to bring customers back to the site based on historical data. By building an accurate prediction model of what content works best at winning back different types of customers, machine learning can be used to optimize your retargeting ads to make them as effective as possible.

Find out how you can increase your customer conversions today. Our RACE Framework gives you a marketing funnel structure integrated across your customers' experiences of your company to help you identify and prioritize high-converting journies. Book your call today to discover how RACE planning can help you drive the results you need

Engage - keep your customers returning

13. Predictive customer service

It's far easier to make repeat sales to your existing customer base than it is to attract new customers. So, keeping your existing customers happy is key to your bottom line. This is particularly true in subscription-based business, where a high churn rate can be extremely costly. Predictive analytics can be used to work out which customers are most likely to unsubscribe from a service by assessing what features are most common in customers who do unsubscribe. It's then possible to reach out to these customers with offers, prompts or assistance to prevent them from churning.

14. Marketing automation

Marketing automation techniques generally involve a series of rules, which when triggered initiative interactions with the customer. But who decided these rules? Generally, a marketer who's basically guessing what will be most effective. Machine learning can run through billions of points of customer data and establish when are the most effective times to make contact, what words in subject lines are most effective and much more. These insights can then be applied to boost the effectiveness of your marketing automation efforts.

15. 1:1 dynamic emails

In a similar fashion to marketing automation, applying insights generated from machine learning can create extremely effective 1:1 dynamic emails. Predictive analytics using a propensity model can establish a subscriber's propensity to buy certain categories, sizes and colours through their previous behaviour and displays the most relevant products in newsletters. The product stock, deals, pricing is all correct at the time of opening the email.

By Dave Chaffey

https://www.smartinsights.com/

How AI Rewrote the Slogans of 50 Well-Known Brands

 Can AI do a decent job of rewriting the taglines of popular brands?

To find out, Quality Logo Products had a generative AI program rewrite the slogans of 50 top companies. The researchers then surveyed 1,007 Americans to find if they liked the original slogan or the AI-generated slogan better.

Overall, people preferred the slogans generated by AI 46% of the time.

The AI-generated slogans that people preferred most over the original slogans were for Google, Facebook, Samsung, Adobe, GoPro, and Microsoft.

The original slogans that people preferred over the AI-generated slogans included those for McDonald's, Coca-Cola, Walmart, and Pizza Hut.

The slogans that people were fairly evenly split on included those for LEGO, Levi's, and Ford.

About the research: For the report, the researchers had a generative AI program rewrite the slogans of 50 top companies. They then surveyed 1,007 Americans to find if they liked the original slogan or the AI-generated slogan better.

https://www.marketingprofs.com/

Using AI to Drive Strategy Execution

 

by Terry Sterling

Over the years, I have been afforded the opportunity to work with numerous organizations in a wide variety of sectors in the development of their strategy. Generally, I have found many of these organizations share similar challenges when it comes to strategy execution. Based on this interaction, I have found three common areas shared by these organizations in trying to achieve success in the deployment and execution of their strategy:

• The lack of real-time visibility into performance metrics and progress towards strategic goals
• Inefficient utilization of resources, resulting in multiple delays in schedule projects and missed targets
• Difficulty in identifying patterns and trends in large amounts of data

To successfully overcome these challenges, it is of utmost importance to be able to properly define Objectives and related Key Performance Indicators (KPIs) utilized to measure strategy. If an organization fails to properly establish these elements and align them with their strategy, their likelihood of achieving any success is non-existent. The second part of this equation includes aligning objectives and strategic goals throughout the various business units or departments that may exist within the organization.

The second critical area that must be addressed is the timely collection and analysis of data to provide leaders with the information necessary to gauge the organization’s progress in achieving its strategy. Many times, organizations get bogged down due to the sheer volume of data available to be properly analyzed and they often miss a window of opportunity due to not having timely validated data.

Incorporating AI to Improve Strategy Execution

Let’s take a brief look at Siemens AG and how they incorporated the utilization of artificial intelligence to improve their strategy execution. Siemens AG is a global technology company that operates in multiple industries such as energy, healthcare, and transportation. The company has over 300,000 employees and has operations in more than 200 countries worldwide. In 2022, the company reported revenues of $78.037B, an increase of 4.83% from 2021.

Siemens AG, like many companies, faced the three areas characterized earlier. To address these issues, the organization implemented an AI-driven strategy execution plan. The first step in this plan was to review objectives and KPIs to ensure they were correct and necessary to drive organizational performance. To accomplish this, they utilized a combination of internal and external data to identify which KPIs and objectives were most relevant to the organization’s strategy.

The company then utilized AI algorithms to collect and analyze data from a wide variety of sourced to include financial reports, customer feedback, and current/historical market trends. The algorithms utilized were designed to detect and identify patterns and trends in the data and provide real-time insights into the performance metrics and progress towards strategic goals.

The AI algorithms identified areas where the organization was falling short of its intended strategic results and provided recommendations for improved performance. The algorithms also identified areas where the company could leverage and optimize it resource allocation to achieve better results.

Based on the data and insights provided by their algorithms, Siemens AG implemented a series of action plans to address areas of improvement and optimization of their resource allocations. This allowed the organization to have improved visibility into performance metrics and progress being made towards achievement of strategic goals. It also resulted in better utilizing resources, resulting in faster project completion and improved results. Finally, the AI algorithms allowed the organization to better be able to identify patterns and trends in large amounts of data, and do so much quicker.

Specifically, Siemens AG accomplished the following:

• Reduction in development times by up to 30%
• Reduction in time to market up to 40%
• Increase in engineering efficiency by up to 50%

AI wasn’t something new at Siemens AG. They have been progressive in developing and applying new technologies for decades. Unfortunately, many organizations in today’s world struggle with technology and the vast amount of available data. Many become incapacitated and unable to achieve strategy simply from the perspective of too much data and not knowing exactly how to deal with it or what they want out of it.

Transitioning to AI 

The simple fact of the matter is that AI is here whether your organization is ready for it or not…the genie is out of the bottle. Those organizations that are successful in leveraging AI technology will still, most likely, be in business tomorrow. Those who fail to do so will struggle to keep up and at best, their future will be filled with distraught and uncertainty. Here are some things organizations can do to make the transition to AI a little less daunting

• Understand the potential benefits of AI as it applies to your organization before diving into AI implementation
• Assess your organization’s readiness for AI. Do you have the infrastructure, data, and talent necessary
• Start small: Consider piloting a project in a single department or with a small team to test the waters
• Get external expertise. If struggling with AI implementation, utilize consultants who are experts in the field and seek partnerships with technology vendors who specialize in AI
• Adopt a culture of experimentation. AI requires a culture to be agile; a willingness to fail fast and learn from mistakes
• Invest in training. Organizations have to invest in training their employees to understand AI and how to use it effectively

Many organizations may shy away from AI as it is difficult to measure the precise financial impact of AI-driven initiatives. This is a challenge due to their being many factors that contribute to the overall benefits and costs of such initiatives. While not everyone will immediately move to board the AI train, there are still some things your organization can do to prepare you for when you are ready to integrate AI into your organization.

• Spend the necessary time to develop a solid strategy for your organization and do it the right way
• Spend the necessary time to develop a robust set of key metrics based on your strategic objectives
• Foster a performance culture that encourages experimentation and involve as many people in your strategy as is possible
• Learn to use the right data to make the right decisions
• Learn from mistakes and make immediate corrections. Be agile and willing to change course and adapt to changing environments
• Don’t be afraid to venture out on a limb…that is where the fruit is located

https://balancedscorecard.org/

The New Frontier: How AI Will Impact Businesses in 2023

 

Original data from 1.35k professionals and expert insights to help you leverage AI for your business.

Written by: Sara Friedman

“It’s the biggest thing since the internet.”

“This is the modern-day industrial revolution.”

“This will change everything.”

You’ve heard about it from co-workers and leadership teams, read about it in the media, and seen it splashed across social media platforms: artificial intelligence. 

But how will leaders use this new technology on the ground to innovate, scale, and improve their businesses? And what new opportunities will arise?

To answer that question, HubSpot Blog Research polled 1.35k+ business professionals in the US, including marketers, sales pros, bloggers, and customer support specialists in March 2023 on AI and automation.

You’ll also hear from experts at HubSpot, Jasper, Zapier, and more on everything from employing AI to make better written content and videos to using AI-powered tools to become a more productive leader.

1. How Business Professionals Across Industries Are Using AI 

HubSpot’s data makes one thing clear: AI is not a far-off pipe dream; it’s here, and it’s already being used in the day-to-day operations of real businesses. 

From C-suites and marketing teams to sales, customer service, and blog teams, AI and automation is being used in unique ways across varied business use cases. 

Below, we break down the data by business specialty:

Leadership 

Business leaders are always responsible for steering the ship and making big-picture decisions, so it’s no surprise that many are already implementing AI-powered tech into their business models and teams — with 62% reporting that their company has already invested in AI/automation tools for employees to leverage in their roles.

And the trend will likely not only continue in 2023 but also steeply increase: 43% of business leaders say they plan to increase their investment in AI/automation tools over the course of 2023, and 31% of business leaders say AI/automation tools are very important to their overall business strategy.

Implementing AI tools is not just an effort to stay on trend, with 27% of business leaders reporting that their company’s investment in AI/automation tools has returned a very positive ROI, and 44% reporting a somewhat positive ROI.

In the coming year, business leaders will likely dig deeper into the many ways AI and automation can affect and improve the bottom line.

This shift will also change whom the company is hiring: Of business leaders whose companies invested in AI/automation, 66% say their company has already hired new employees specifically to help with leveraging/implementing AI/automation tools. 

Marketing 

As consumers increasingly seek out personalized experiences, marketers need to find ways to deliver tailored messages to individuals at scale. 

AI and automation have emerged as powerful tools that can help marketers achieve this goal by streamlining processes, analyzing large amounts of data, and identifying patterns that humans might miss.

HubSpot’s data makes one thing clear: Marketers are not only ready for AI to change their industry, but also it already is:

• 69% of marketers say generative AI is important to their overall content marketing strategy 

• 53% of marketers report using chatbots in their roles (ChatGPT, Bing AI, Google Bard) 

• 44% use visual AI tools (DALL-E, Synthesia)

• 44% of marketers use text-generation tools (copy.ai, Compose AI) 

• 48% report using generative AI for market research, finding data sets, and summarizing articles

• 45% use AI for content creation and for data analysis/reporting 

And, arguably the most important stat, 79% of marketers believe generative AI can improve the quality of marketing content they create. 

For a deep dive on all things marketing and AI, check out this page. 

Sales

Salespeople are busy: They’re closing deals, reaching out to potential customers, and building relationships that will move the company’s bottom line. 

With AI and automation, sales professionals can spend less time on administrative tasks and more time doing what only humans can do: building relationships and trust. 

As far as the types of AI tools sales professionals are already using in their roles, here’s the data:

• 35% said they use AI tools that automate manual tasks (data entry, note taking, scheduling, etc.)

• 34% reported using AI tools that offer data-driven insights (sales forecasting, lead scoring, pipeline analysis, etc.)

• 31% of sales pros said they use generative AI tools that help write sales content or prospect outreach messages (ChatGPT, Jasper, DALL-E, etc.)

• 28% said they use AI tools that analyze or simulate sales calls for training/coaching purposes

• 26% reported using AI tools/chatbots that assist with prospect outreach/lead generation

• 25% said they use AI tools/chatbots that assist with qualifying leads

When it came to the tasks sales professionals use AI to assist with, the most reported answer was content creation at 18% (followed by prospect outreach and research, both at 16%). Plus, 60% of sales professionals say AI tools are important to their overall sales strategy. 

Most importantly, the data shows that sales professionals feel that AI and automation will allow them to do what they do best — sell — and 67% agree that AI tools can help them spend more time selling. 

Customer Support 

Customer support specialists are the backbone of any good company: They are experts in communicating with customers, understanding their needs, and bettering the businesses as a whole by relaying those needs to their colleagues. 

With that kind of responsibility, AI will likely put the emphasis on support in customer support by backing up the humans in those roles. 

Customer service pros are ready for the assistance, with 53% saying that AI and automation tools will be important to their customer service strategy and 49% saying that by 2024, AI tools will be able to do most customer service-related tasks independently.

Automation and AI-powered tools, at their core, are created to help make the jobs of humans easier. And 67% of customer service pros think AI tools will make it easier to respond to customer service requests.

Blog/SEO

Conversations around AI often center around generative AI’s ability to produce written content, making it particularly applicable to bloggers and SEO professionals. 

AI and automation can help bloggers create better content, optimize their websites, and improve their search engine rankings. 

Of bloggers and SEO professionals surveyed, 35% said they strongly agree that by 2024, most SEO pros will use AI and automation in their roles (36% agree).

Also by 2024, 75% of bloggers/SEO pros agree that AI/automation tools will be able to do most SEO-related tasks completely independently.

With 70% agreeing that AI and automation tools can help them optimize their website for SEO more efficiently, AI-powered tools will become increasingly common when the goal is optimizing a website for organic content.

2. AI Tools and Applications Used Among Professionals

To more deeply understand the ways in which AI might improve our day-to-day operations at work, it’s helpful to first assess how business professionals are using the tools currently available: 

• 69% of business professionals use communication tools (Slack, Zoom, Microsoft Teams, etc.)

• 40% report that they use CRM software (HubSpot, SalesForce, etc.)

• 21% use artificial intelligence/automation tools (chatbots, Jasper, ZoomInfo, Marketo, Grammarly, Gong.io, etc.)

• 27% say they use project management tools (Monday, Asana, etc.)

When it comes to AI, there are several subfields:

• Natural language processing (NLP): Software that helps machines process human language through language prompts

• Machine learning (ML): When machines analyze data and make recommendations based on it

• Computer vision: Machines that can understand and interpret visual information

Within those fields are the AI tools that professionals are using or are interested in one day using. Arguably most popular are chatbots, with 40% of business leaders saying that they’ll be most likely to use ChatGPT on work-related tasks and 32% saying the same for Bard. 

There’s also growing interest in using AI to generate still images and video for a variety of business use cases. Already, 20% of marketers report using AI for creating images specifically when making content. 

And tools that can analyze large amounts of data quickly will be important across business sectors, with 37% of sales professionals already reporting that they use AI tools that analyze your website data and provide actionable insights (improving UX, finding content gaps, etc.).

These tools, while intimidating at first, can be easily unlocked with some time, concentration, and dedication, as with any skill. 

In the words of HubSpot CMO Kipp Bodner: “If you are a know-it-all, you’re done. But there’s never been a better time to be a learn-it-all.”

3. How AI Will Help Business Leaders 

We wouldn’t be talking about any of this if it wasn’t going to seriously improve our lives, right? (Right.)

Across job roles, professionals are busier than ever before, and time is certainly money when it comes to business. 

Professionals estimated that they save an average of 2 hours and 24 minutes each day using AI and automation tools compared to without them. These tools can be particularly helpful for a task like taking notes in a meeting, where professionals estimated AI tools saved them an average of 1 hour and 49 minutes. 

Meghan Keaney Anderson, the VP at Jasper AI, said on a recent episode of Marketing Against the Grain that the time we save shouldn’t go to waste but rather should get reallocated into high-impact projects. 

“Our role now is to figure out how to use [AI] in a way where we don’t just put junk on the internet, but we’re reinvesting the time that we get back,” she says. 

“Productivity and the act of creating builds upon itself. We’ve seen it with the internet and the printing press — whenever you have this moment when something releases all the barriers on creation, you get this period of massive productivity and creation.”

Business professionals who understand this premise will be at an advantage in the near future: 78% agree that AI can help them spend more time on the most important parts of their roles, and 78% strongly agree that AI can help them be more efficient in their roles.

Excitingly, AI will continue to take the labor-intensive but uninspiring projects off our desks. Of surveyed business professionals, 81% agree that AI tools can help them spend less time on manual tasks such as data entry and scheduling meetings. 

Can’t say we’ll be sorry to see those administrative tasks go...

4. Business Leaders’ Biggest Concerns About AI 

Innovation is never perfect, and that holds true for artificial intelligence. There are real fears surrounding AI, from how it will change our jobs and affect our livelihoods to what this means for trust and ethics. 

As far as what business professionals are most worried about, 41% report being concerned that AI will replace their jobs in the next few years. On the flip side, 36% agree that at some point in the future, AI will completely replace humans in the workplace.

“One of the things we are all forced to do now is to break down our roles and figure out the parts of our roles that actually can be automated away,” says Zapier CMO Kieran Flanagan. “We are better off automating those things to get time back.”

There are also broader societal fears at play, with 38% of business professionals agreeing that AI poses a threat to humanity.

Ultimately, change is scary, and most technological advancements in history were met with fear and doubt from the general public. 

Regardless of whether the change will be good or bad, 64% of business professionals agree that AI/automation tools will make a significant impact on how they do their jobs in 2023. 

5. The Future of AI in Business 

If your head is spinning after reading all the ways AI could change our businesses — and our lives — you’re not alone. 

The innovation is dizzying, and it’s only getting started. By 2024, 64% of business professionals agree that most people will use some form of AI or automation to assist them in their jobs. 

And they agree this will majorly affect the bottom line of the business: 53% agree if AI were fully implemented within their company, the business would see unprecedented growth, and 56% agree AI can help their business scale in a way that would be impossible without it.

“It’s inescapable; there are machines and programs getting as good, or better, than humans,” says Jamal Meneide. “AI is profoundly changing the world that we live in. It underpins how businesses and customers interact everyday.”

If you’ve made it all the way through this article, you know we started off with mention of the industrial revolution. And we’re finishing by coming full circle: 57% of business pros agree that when it comes to the impact it will have on human productivity, AI will rival the industrial revolution

“It’s going to change how you communicate with people, how you buy things, how you travel, the list goes on,” says Meneide. “While that can feel intimidating and the future is far from certain, it’s important to figure out how we can use it to get the most out of our lives.”

Artificial intelligence is on the move, and it looks like there’s no stopping it — so it might just be time to get on board. 

https://blog.hubspot.com/