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8 Factors to Consider Before Buying or Building your AI Solution

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Artificial intelligence (AI) – it’s here, it’s there, it’s everywhere. Businesses in every vertical – retail, finance, healthcare, manufacturing, transport, supply chain, entertainment, and technology – use AI.

More than a quarter of companies using AI attribute at least 5% of their earnings before interest and taxes to AI. Almost two-thirds of early AI adopters note AI gives them a competitive advantage. Given the benefits, it’s hardly surprising that some businesses have FOMO on AI.

But when the company is ready to adopt AI, they face a dilemma – should they buy or build? Read on to learn about factors to consider when deciding on AI solutions for your business.

AI for business

Startups and big enterprises ready to adopt AI have two ways to go about it: Build their own AI model or buy commercially available AI software. Both options involve the use of open-source AI or off-the-shelf product such as free live chat apps

Before dissecting the build vs buy dilemma, it’s important to understand open-source and proprietary AI software.

Open-source AI

Open-source AI includes freely available AI tools. These could be algorithms, datasets, ready-to-use application programming interfaces (APIs), libraries of codes, or a combination of all. Some of the free AI and machine learning (ML) platforms are Tensorflow, Python, PyTorch, KNIME, Apache Spark, and H2O.ai.

Usually, each AI software has specific use cases like speech recognition, computer vision, ML, natural language processing (NLP), or big data analytics. For instance, TensorFlow and PyTorch are suited for building ML and deep learning models. Libraries like Keras and OpenNN, on the other hand, are artificial neural network (ANN) frameworks. Businesses leverage these libraries and platforms to build their own AI system based on their requirements.  

Commercial AI software

From chatbots and conversational AI to automation and advanced data science, there are many ready-to-use AI tools.  Though it’s costly, these tools can save a lot of resources and time for the company. The most common commercial model of AI software is the annual subscription model that includes necessary licenses, support, and service from vendors. Another type is “pay-as-you-go”  where one can access the AI software as an API.

Major tech companies like Google, Microsoft, Amazon, IBM, and Salesforce also offer AI and ML as a platform service. These AI services provide end-to-end cloud-hosted platforms for developing and deploying AI models.

Breaking down the build vs buy dilemma

Imagine you need a new house – would you buy or build one? Choosing between the two depends on a lot of factors. What kind of house do you want? What resources do you have on hand? How much time and money do you have? And how much of each are you willing to spend?

Deciding on whether you want to build or buy your AI for your business is similar. Let’s look at the factors you should consider before taking the plunge.

The need: Why do you need AI?

Diving into algorithms and neural networks before figuring out what you want from AI is a bit like diving into the ocean to find a lost treasure that might or might not exist. What is your business looking to achieve by using AI tools? Is AI a core component of future business growth? Or is it to automate a task or improve business processes?

If AI is the core of your business, build it. If you want to use AI for just specific business operations, then buy. A chatbot provider needs to build its own NLP model. But a small online startup doesn’t have to build a huge NLP model to have a chatbot on its website. It can simply buy the tool from conversational AI providers like Dialpad or Drift and improve customer experience.

The difficulty: How complex is the AI solution?

The complexity of the AI solution the business is looking to adopt is also an important element to think about. There are many tried and tested products in the market that you can buy for common AI applications, like sales and marketing automation processes, predictive forecasting, chatbots, speech-to-text, or machine translation.

But sometimes, the data involved is sensitive or the business is looking for a novel solution.  Commercial tools are simply not suitable or sufficient in these cases. Netflix, for instance, built its own proprietary AI model that offers personalized movies and shows recommendations based on user data. It was worth it to build because Netflix considers this feature critical to its business.

On the other hand, Woodside Energy, an oil and gas company, leveraged IBM Watson’s ML and NLP tech to make 30 years of data on oil platform operations accessible throughout the company.  Woodside owns the data, but shared it with IBM to tap their expertise instead of building it in-house.

The human effort: Do you have the right people?

Evaluate the AI capabilities of your in-house experts. Can your pool of technical experts develop AI frameworks?  Or does your firm have the resources to hire such experts? If yes, then the firm can think about building an AI model in-house.

Tech companies, AI vendors, and large enterprises in the banking, retail, and healthcare sectors have a huge pool of AI experts working exclusively on AI and have the resources to develop their solutions in-house.

Bank of America, for example, built its own virtual banking assistant, Erica. By the time the bank’s AI assistant was in the beta phase in 2017, the bank had a dedicated team of more than 100 people working on the project.

However, if the firm doesn’t have technical expertise or resources to hire, it’s best to purchase from a vendor. For example, a small marketing firm can buy an AI-powered content creation tool rather than investing millions to hire a team and develop it in-house.

The time: What’s the deadline?

Do you need the AI software immediately? Or can you wait to get a custom tool?

If your business needs the tool at once, and there are already multiple products available to purchase, then buying is the ideal solution. But, if the company needs a custom tool that needs time to build, the firm can develop on its own, given it has the resources to do it.

The cost: What’s the budget?

Many enterprises fail to understand the true cost of building or buying an AI model. Both options require heavy investment to get the right people, hardware, and software.

A company can cut some costs by building AI using free, open-source software. But there are other prices to pay. From getting training data to buying necessary software and hardware like cloud storage, expensive computing power, and AI operationalization software, there are significant hidden costs involved. Companies have to cough up more to hire (expensive and rare) AI experts. The average base pay for an AI job is easily above $100,000 in the USA.

Buying a third-party AI solution solves the problem of having to spend on hiring an AI team. But commercial tools can still be expensive.  A custom AI solution can cost from $6000 to over $300,000 per solution while a third-party software can cost up to $40,000 per year. Firms will have to weigh the costs of both options and decide.

The integration:  Can your current tech support AI?

It’s critical to gauge how well the existing IT infrastructure can integrate with AI, whether building or buying. The team should look at whether the AI platform can sync with their current tech stack. More often than not, open-source platforms have integration and scaling issues.

Commercial vendors take care of the dreaded task of AI integration, scaling, and compatibility issues. But it’s always better to check the compatibility of the vendor tool with the existing ecosystem. If you’re integrating an AI solution to optimize your lead generation process, it should seamlessly integrate with your existing CRM. Ignoring this can become costly to fix.

The service: What happens when something goes wrong??

With commercial products, there’s always vendor service and support. The AI vendor has responsibility and accountability for the smooth functioning of the tool. Your team can rely on them  to fix bugs and other issues that crop up in the AI model. While building AI, the company has to deal with any issues that crop up without much support.

The security: Is your AI safe?  

Both open-source and commercial AI platforms have potential security risks. Unlike popular opinion that open source has more vulnerabilities, nearly 90% of IT leaders think it’s as secure or more secure than proprietary software. So the question is not about which option is more secure but which needs more support. The answer is building with open source. AI vendors take care of security issues and fix bugs and patches themselves. But building AI in-house means having a team dedicated to looking at potential security issues. This adds significant costs to the budget.

Building vs buying AI: Weighing the pros and cons

Let‘s make your decision-making on AI solutions a little easier. Here are some common advantages and disadvantages of building vs buying AI.

Pros and cons of building AI

Building an in-house AI using open-source frameworks suits firms that have unique data and the capability to invest time and resources. Its advantages include:

  • no or low-cost investment in the tech
  • better customization based on needs
  • control over both data and the model
  • flexibility to change the model when required
  • community support

However, open-source AI platforms have several gaps like:

  • high turnaround time
  • difficulty in hiring AI experts and data scientists
  • lack of service and support to solve issues
  • added security cost  
  • compatibility issues with other software

Pros and cons of  buying proprietary AI

Buying an off-the-shelf commercial tool might be best for companies that don’t have the human resources to build their own solution or lack unique datasets. Its advantages include:

  • specialized knowledge for a particular AI use case
  • availability of large, well-organized training datasets
  • seamless integration of AI tools into existing infrastructure
  • ability to scale AI models
  • continuous vendor service and support to solve issues

But commercial products have their own drawbacks like:

  • high investment cost
  • fewer customization options
  • risk of losing access to critical data
  • vendor lock-in periods

Still confused? Go for the lean strategy

If you’re still confused after assessing all factors, think about the lean AI strategy. The strategy comes from the Japanese manufacturing industries’ philosophy called the lean manufacturing process. Adopted by carmakers like Toyota, the strategy aims to reduce production waste without sacrificing efficiency and quality.

The lean AI strategy also aims for the same: implement AI incrementally while reducing resource costs and risks. The business can start with a small AI project that delivers a minimum viable AI product (MVAP).

The build-vs-buy decision tree

With MVAP in mind, consider the following decision tree. Scout for an available solution. If there are multiple commercial products, buying would be the best solution. If there are not many vendors offering the required tool, look for a partner to develop a custom AI solution. This saves the company time, efforts, and resources of building from scratch.

However, if both buying and partnering don’t work, the company can build its MVAP. Aiming at MVAP helps the company kickstart the AI project sooner. With established deadlines and goals, companies can assess the performance and business impact of MVAP faster. As the project matures, the business gets more opportunities to evaluate and undertake more ambitious AI projects.

Winning with AI

Like all deliberations about implementing new technologies, it’s important to make an informed decision on AI in your company. Whether you choose to build or buy your AI, just keep in mind that AI is not going anywhere anytime soon – so start early to reap the rewards early.

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How Does Success of Your Business Depend on Choosing Type of Native Advertising?

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How Does Success of Your Business Depend on Choosing Type of Native Advertising?

The very first commercial advertisement was shown on TV in 1941. It was only 10 seconds long and had an audience of 4,000 people. However, it became a strong trigger for rapid advertising development. The second half of the 20th century is known as the golden age of advertising until the Internet came to the forefront and entirely transformed the advertising landscape. The first commercial banner appeared in the mid-90s, then it was followed by pop-ups, pay-by-placement and paid-pay-click ads. Companies also started advertising their brands and adding their business logo designs, which contributes to consumer trust and trustworthiness.

The rise of social media in the mid-2000s opened a new dimension for advertising content to be integrated. The marketers were forced to make the ads less intrusive and more organic to attract younger users. This is how native advertising was born. This approach remains a perfect medium for goods and services promotion. Let’s see why and how native ads can become a win-win strategy for your business.

What is native advertising?

When it comes to digital marketing, every marketer talks about native advertising. What is the difference between traditional and native ones? You will not miss basic ads as they are typically promotional and gimmicky, while native advertising naturally blends into the content. The primary purpose of native ads is to create content that resonates with audience expectations and encourages users to perceive it seamlessly and harmoniously.

Simply put, native advertising is a paid media ad that organically aligns with the visual and operational features of the media format in which it appears. The concept is quite straightforward: while people just look through banner ads, they genuinely engage with native ads and read them. You may find a lot of native ads on Facebook, Twitter and Instagram – they appear in the form of “in-feed” posts that engage users in search for more stories, opinions, goods and services. This unobtrusive approach turns native ads into a powerful booster for any brand.

How does native advertising benefit your business?

An average Internet user comes across around 10,000 ads a day. But even physically, it is impossible to perceive this amount of information in 24 hours. So, most of them use adblockers, nullifying all efforts of markers. Native ads successfully overcome this digital challenge thanks to their authenticity. And this is not the only advantage of native advertising. How else does your business benefit? Here are just a few major benefits that prove the value of native ads:

Better brand awareness. Native ads contribute to the brand’s visibility. They seamlessly blend into educational, emotional, and visual types of content that can easily become viral. While promotional content typically receives limited shares, users readily share valuable or entertaining content. Consequently, while you incur expenses only for the display of native ads, your audience may go the extra mile by sharing your content and organically promoting your brand or SaaS product at no additional cost.

Increased click-through rates. Native ads can generate a thrilling click-through rate (CTR) primarily because they are meticulously content-adaptable. Thus, native ads become an integral part of the user’s journey without disrupting their browsing experience. Regardless of whether your native advertising campaign is designed to build an audience or drive specific actions, compelling content will always entice users to click through.

Cost-efficient campaign performance. Native advertising proves to be cheaper compared to a traditional ad format. It mainly stems from a higher CTR. Thanks to precise targeting and less customer resistance, native ads allow to bring down cost-per-click.

Native ads are continuously evolving, enabling marketers to experiment with different formats and use them for successful multi-channel campaigns and global reach.

Types of native advertising

Any content can become native advertising as there are no strict format restrictions. For example, it can be an article rating the best fitness applications, an equipment review, or a post by an influencer on a microblog. The same refers to the channels – native ads can be placed on regular websites and social media feeds. Still, some forms tend to be most frequently used.

  • In-feed ads. This type of ad appears within the content feed. You have definitely seen such posts on Facebook and Instagram or such videos on TikTok. They look like regular content but are tagged with an advertising label. The user sees these native ads when scrolling the feed on social media platforms.
  • Paid search ads. These are native ads that are displayed on the top and bottom of the search engine results page. They always match user’s queries and aim to capture their attention at the moment of a particular search and generate leads and conversions. This type of ad is effective for big search platforms with substantial traffic.
  • Recommendation widgets. These come in the form of either texts or images and can be found at the end of the page or on a website’s sidebar. Widgets offer related or intriguing content from either the same publisher or similar sources. This type of native ads is great for retargeting campaigns.
  • Sponsored content. This is one of the most popular types of native advertising. Within this format, an advertiser sponsors the creation of an article or content that aligns with the interests and values of the platform’s audience. They can be marked as “sponsored” or “recommended” to help users differentiate them from organic content.
  • Influencer Advertising. In this case, advertisers partner with popular bloggers or celebrities to gain the attention and trust of the audience. Influencers integrate a product, service, or event into their content or create custom content that matches their style and topic.

Each of these formats can bring stunning results if your native ads are relevant and provide value to users. Use a creative automation platform like Creatopy to design effective ads for your business.

How to create a workable native ad?

Consider these 5 steps for creating a successful native advertising campaign:

  • Define your target audienceUsers will always ignore all ads that are not relevant to them. Unwanted ads are frustrating and can even harm your brand. If you run a store for pets, make sure your ads show content that will be interesting for pet owners. Otherwise, the whole campaign will be undermined. Regular market research and data analysis will help you refine your audience and its demographics.
  • Set your goals. Each advertising campaign should have a clear-cut objective. Without well-defined goals, it is a waste of money. It is a must to know what you want to achieve – introduce your brand, boost sales or increase your audience.
  • Select the proper channels. Now, you need to determine how you will reach out to your customers. Consider displaying ads on social media platforms, targeting search engine result pages (SERPs), distributing paid articles, or utilizing in-ad units on different websites. You may even be able to get creative and use email or SMS in a less salesy and more “native”-feeling way—you can find samples of texts online to help give you ideas. Exploring demand side platforms (DSP) can also bring good results.
  • Offer compelling content. Do not underestimate the quality of the content for your native ads. Besides being expertly written, it must ideally match the style and language of the chosen channel,whether you’re promoting professional headshots, pet products, or anything else. The main distinctive feature of native advertising is that it should fit naturally within the natural content.
  • Track your campaign. After the launch of native ads, it is crucial to monitor the progress, evaluating the costs spent and results. Use tools that help you gain insights beyond standard KPIs like CTR and CPC. You should get engagement metrics, customer data, campaign data, and third-party activity data for further campaign management.

Key takeaway

Summing up the above, it is time to embrace native advertising if you haven’t done it yet. Native ads seamlessly blend with organic content across various platforms, yielding superior engagement and conversion rates compared to traditional display ads. Marketers are allocating higher budgets to native ads because this format proves to be more and more effective – content that adds value can successfully deal with ad fatigue. Native advertising is experiencing a surge in popularity, and it is to reach its peak. So, do not miss a chance to grow your business with the power of native ads.or you can do digital marketing course from Digital Vidya.

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OpenAI’s Drama Should Teach Marketers These 2 Lessons

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OpenAI’s Drama Should Teach Marketers These 2 Lessons

A week or so ago, the extraordinary drama happening at OpenAI filled news feeds.

No need to get into all the saga’s details, as every publication seems to have covered it. We’re just waiting for someone to put together a video montage scored to the Game of Thrones music.

But as Sam Altman takes back the reigns of the company he helped to found, the existing board begins to disintegrate before your very eyes, and everyone agrees something spooked everybody, a question arises: Should you care?

Does OpenAI’s drama have any demonstrable implications for marketers integrating generative AI into their marketing strategies?

Watch CMI’s chief strategy advisor Robert Rose explain (and give a shoutout to Sutton’s pants rage on The Real Housewives of Beverly Hills), or keep reading his thoughts:

For those who spent last week figuring out what to put on your holiday table and missed every AI headline, here’s a brief version of what happened. OpenAI – the huge startup and creator of ChatGPT – went through dramatic events. Its board fired the mercurial CEO Sam Altman. Then, the 38-year-old entrepreneur accepted a job at Microsoft but returned to OpenAI a day later.

We won’t give a hot take on what it means for the startup world, board governance, or the tension between AI safety and Silicon Valley capitalism. Rather, we see some interesting things for marketers to put into perspective about how AI should fit into your overall content and marketing plans in the new year.

Robert highlights two takeaways from the OpenAI debacle – a drama that has yet to reach its final chapter: 1. The right structure and governance matters, and 2. Big platforms don’t become antifragile just because they’re big.

Let’s have Robert explain.

The right structure and governance matters

OpenAI’s structure may be key to the drama. OpenAI has a bizarre corporate governance framework. The board of directors controls a nonprofit called OpenAI. That nonprofit created a capped for-profit subsidiary – OpenAI GP LLC. The majority owner of that for-profit is OpenAI Global LLC, another for-profit company. The nonprofit works for the benefit of the world with a for-profit arm.

That seems like an earnest approach, given AI tech’s big and disruptive power. But it provides so many weird governance issues, including that the nonprofit board, which controls everything, has no duty to maximize profit. What could go wrong?

That’s why marketers should know more about the organizations behind the generative AI tools they use or are considering.

First, know your providers of generative AI software and services are all exploring the topics of governance and safety. Microsoft, Google, Anthropic, and others won’t have their internal debates erupt in public fireworks. Still, governance and management of safety over profits remains a big topic for them. You should be aware of how they approach those topics as you license solutions from them.

Second, recognize the productive use of generative AI is a content strategy and governance challenge, not a technology challenge. If you don’t solve the governance and cross-functional uses of the generative AI platforms you buy, you will run into big problems with its cross-functional, cross-siloed use. 

Big platforms do not become antifragile just because they’re big

Nicholas Taleb wrote a wonderful book, Antifragile: Things That Gain From Disorder. It explores how an antifragile structure doesn’t just withstand a shock; it actually improves because of a disruption or shock. It doesn’t just survive a big disruptive event; it gets stronger because of it.

It’s hard to imagine a company the size and scale of OpenAI could self-correct or even disappear tomorrow. But it can and does happen. And unfortunately, too many businesses build their strategies on that rented land.

In OpenAI’s recent case, the for-profit software won the day. But make no bones about that victory; the event wasn’t good for the company. If it bounces back, it won’t be stronger because of the debacle.

With that win on the for-profit side, hundreds, if not thousands, of generative AI startups breathed an audible sigh of relief. But a few moments later, they screamed “pivot” (in their best imitation of Ross from Friends instructing Chandler and Rachel to move a couch.)

They now realize the fragility of their software because it relies on OpenAI’s existence or willingness to provide the software. Imagine what could have happened if the OpenAI board had won their fight and, in the name of safety, simply killed any paid access to the API or the ability to build business models on top of it.

The last two weeks have done nothing to clear the already muddy waters encountered by companies and their plans to integrate generative AI solutions. Going forward, though, think about the issues when acquiring new generative AI software. Ask about how the vendor’s infrastructure is housed and identify the risks involved. And, if OpenAI expands its enterprise capabilities, consider the implications. What extra features will the off-the-shelf solutions provide? Do you need them? Will OpenAI become the Microsoft Office of your AI infrastructure?

Why you should care

With the voluminous media coverage of Open AI’s drama, you likely will see pushback on generative AI. In my social feeds, many marketers say they’re tired of the corporate soap opera that is irrelevant to their work.

They are half right. What Sam said and how Ilya responded, heart emojis, and how much the Twitch guy got for three days of work are fodder for the Netflix series sure to emerge. (Robert’s money is on Michael Cera starring.)

They’re wrong about its relevance to marketing. They must be experiencing attentional bias – paying more attention to some elements of the big event and ignoring others. OpenAI’s struggle is entertaining, no doubt. You’re glued to the drama. But understanding what happened with the events directly relates to your ability to manage similar ones successfully. That’s the part you need to get right.

Want more content marketing tips, insights, and examples? Subscribe to workday or weekly emails from CMI.

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Cover image by Joseph Kalinowski/Content Marketing Institute

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The Complete Guide to Becoming an Authentic Thought Leader

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The Complete Guide to Becoming an Authentic Thought Leader

Introduce your processes: If you’ve streamlined a particular process, share it. It could be the solution someone else is looking for.

Jump on trends and news: If there’s a hot topic or emerging trend, offer your unique perspective.

Share industry insights: Attended a webinar or podcast that offered valuable insights. Summarize the key takeaways and how they can be applied.

Share your successes: Write about strategies that have worked exceptionally well for you. Your audience will appreciate the proven advice. For example, I shared the process I used to help a former client rank for a keyword with over 2.2 million monthly searches.

Question outdated strategies: If you see a strategy that’s losing steam, suggest alternatives based on your experience and data.

5. Establish communication channels (How)

Once you know who your audience is and what they want to hear, the next step is figuring out how to reach them. Here’s how:

Choose the right platforms: You don’t need to have a presence on every social media platform. Pick two platforms where your audience hangs out and create content for that platform. For example, I’m active on LinkedIn and X because my target audience (SEOs, B2B SaaS, and marketers) is active on these platforms.

Repurpose content: Don’t limit yourself to just one type of content. Consider repurposing your content on Quora, Reddit, or even in webinars and podcasts. This increases your reach and reinforces your message.

Follow Your audience: Go where your audience goes. If they’re active on X, that’s where you should be posting. If they frequent industry webinars, consider becoming a guest on these webinars.

Daily vs. In-depth content: Balance is key. Use social media for daily tips and insights, and reserve your blog for more comprehensive guides and articles.

Network with influencers: Your audience is likely following other experts in the field. Engaging with these influencers puts your content in front of a like-minded audience. I try to spend 30 minutes to an hour daily engaging with content on X and LinkedIn. This is the best way to build a relationship so you’re not a complete stranger when you DM privately.

6. Think of thought leadership as part of your content marketing efforts

As with other content efforts, thought leadership doesn’t exist in a vacuum. It thrives when woven into a cohesive content marketing strategy. By aligning individual authority with your brand, you amplify the credibility of both.

Think of it as top-of-the-funnel content to:

  • Build awareness about your brand

  • Highlight the problems you solve

  • Demonstrate expertise by platforming experts within the company who deliver solutions

Consider the user journey. An individual enters at the top through a social media post, podcast, or blog post. Intrigued, they want to learn more about you and either search your name on Google or social media. If they like what they see, they might visit your website, and if the information fits their needs, they move from passive readers to active prospects in your sales pipeline.

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