AI is still an emerging technology. Widespread use is on the horizon, but what will it cost you to get you there? Time and money, of course, but there’s something more.
The context: An emerging technology
When artificial intelligence, or AI, first appeared in the commercial market, it was affordable only to large enterprises. AI was an emerging technology at the time; most solutions were custom — and therefore, costly.
As the technology continued to develop and break into new markets, however, companies began to create new products and services with a more affordable approach. This widespread accessibility allowed for mainstream popularity to explode, creating the AI environment we know today.
Today: Mainstream technology
Do you remember how websites were first made? Before SquareSpace and Wix, before Wordpress and Github, websites in the 90s were custom-made web development services — valued at around $10K — created by a boutique consultant shop. These days, you can find website builder subscriptions at around $10/month.
What happened between now and then? Competition and barrier to entry. In the 90s, the competition was low and the barrier to entry was high; you had to know coding and website design. However, as time progressed, the opposite began becoming true; solutions for creating a website yourself are now near-endless.
The 3 Phases of Commercialization in Emerging Technology
The accelerated crossover from emerging technology (low competition, high barrier to entry) to mainstream technology (high competition, low barrier to entry) is due in large part to the famous Software as a Service (SaaS) model.
Service → 2) SaaS Products → 3) Build it yourself
The model is simple: Package tech into a product, bill monthly or annually, and scale.
You don't have to build it; you barely know what happens in the back-end, and it's ready to use for your market, right off the shelf.
The SaaS model reignited an entirely new way of modeling a business — the “get it going” model of today.
Where Does Cost Come Into Play?
In 2022, not only has the next wave of tech appeared, it's moving so fast that we are now hitting the “product phase” (post-SaaS). In essence, we’re seeing droves of service and product companies all competing to help enterprises adopt AI.
And while emerging technology has its own universal commercialization phases — as discussed above — AI seems to be following its own path.
Competition has been continuing to climb in the AI space, however, roughly 60% of “AI startups” don’t actually use AI in their businesses. The barrier to entry was recently greatly lowered due to new releases in AI hardware like CPUs and GPUs.
With increasing competition and lowered barrier to entry, AI is headed into familiar, mainstream territory — and quickly. Because of the pandemic and recent hardware releases, the product phase for AI’s growth cycle is kickstarting early, bringing speculative interest to the future of AI.
The Costs of AI — The Breakdown by Commercialization Phase
At each of the three commercialization phases (service, SaaS product, build it yourself), you’re taking on different goals and approaches to accomplishing those goals. Naturally, this leads to varying costs, depending on your understanding of your problems, markets, and potential solutions.
If at any point you’re unsure of what you need to adopt AI in 2022, reach out to AI affiliate companies like AI Partnerships Corp.
Phase 1: Service and Consulting
Right now, not many companies can build AI, so adopting a service and consulting agency into your AI adoption plan can have its benefits. Most businesses aren’t facing life-changing problems necessary to adopt custom AI, but in industries like healthcare, manufacturing, and agriculture, innovative solutions are available.
Building AI teams is also expensive and at least for now, hiring a service provider or consultant can be the most affordable. Of course, it all depends on the need or project at hand.
When considering the cost, most service or consulting AI companies have a proof of concept, or POC, phase for their clients. This is usually a test to see if AI can be applied to the your business, based on factors like quality of data, current systems in place, and more.
A POC test takes time and therefore money, with market leaders charging up to $1M for just the discovery aspect. If you’re in the 8-9 figure range, you can easily go to IBM, Google, or Microsoft for full implementation, but there are plenty of other options.
Market leaders will charge $500,000 - $1,000,000 and that doesn't cover full implementation.
Through our network of 120+ AI Affiliates, we’ve found plenty of service and consulting companies giving POC quotes between $40,000 to $100,000, a fraction of the major market rates.
Not only that, but most smaller AI consulting companies are focused on making AI more available by creating their own tools that increase efficiency, and therefore affordability.
Phase 2: AI Software or Products
Technological growth is exponential, and AI is no exception to this. There is a vast market of SaaS products right now that have AI packaged into them, all of them tackling different needs and problems. If you want something fixed or improved, most likely other companies in your sector or industry are experiencing the same pain, all at a lower cost than hiring someone directly.
Companies can find AI Software that costs anywhere from $5,000 to $50,000 a month.
With AI packaged into a software, the ROI or outcome can be clearly defined. There’s no need for your own AI team or consultant agency; all you need is clear goals and budgets set. You can expect AI software to cost anywhere from $5,000 to $50,000 a month, with a few exceptions that exceed that.
Because AI is next-level business technology, you’re going to see next-level results. Some of the ROIs achieved from AI software exceed 7 to 8 figures, but if leadership is not fully invested in their company's AI journey, then that end goal or ROI will never be successfully met.
Phase 3: Built It Yourself
Before we know it, companies will be building their own AI, much like they’re building their own websites using SquareSpace or Wix today.
The largest challenge standing in AI companies’ ways right now is to find practical applications with attainable results. At the end of the day, the mathematics and modeling for AI can be given easily without code. This is where “low code” or “no code” solutions come into play, which we will undoubtedly be seeing more of.
Once this phase is achieved, B2B AI tools will be widespread and mainstream.
The Future of AI is Endless
From software that helps reduce computing costs to platforms that automate development, engineers and scientists are producing AI faster and better every year.
We’re not there yet, but it's going to arrive a lot sooner than you think. In the meantime, consider a service provider or AI software that will deliver what you are looking for.