Artificial intelligence is transforming industries, and it is providing a competitive advantage to early adopters.
But the headlines hide the fact that success isn’t the norm.
While some companies are adopting artificial intelligence to great advantage – most companies are spinning their wheels with fruitless AI projects that go nowhere.
While some leaders are championing genuinely impactful AI solutions that will drive their business and career forward – most leaders are shepherding projects that were destined to fail (almost as soon as they were conceived).
The fact of the matter is this:
Millions are Wasted on Artificial Intelligence Initiatives Every Year – and Only a Small Portion of Projects are Successful
Enterprises and mid-sized companies are attending events, meeting with AI vendors, and hiring data scientists.
The press releases are everywhere: Banks revealing their new chatbot, eCommerce companies adopting recommendations engines, insurance companies detecting fraud in new ways.
Our research at Emerj shows that companies exaggerate their use or results of AI by as much as 300%, and often only publicly reveal the AI initiatives that they think will reflect well with their customers. Most AI initiatives are failures, beginning with no clear end in mind, or done entirely to appear “innovative”, and with little or no strategic value.
Champions of these failed AI initiatives end up looking more like fools than champions.
What could have been an opportunity for competitive advantage with AI turns into wasted time and money.
What could have been a great career move up becomes a major embarrassment, or a firing.
The “echo chamber” of AI press releases means that more and more companies will believe that the wrong AI projects are the right ones.
How Professionals with No Data Science Skills Can Win in the Era of AI
As it turns out, many of the most critical factors in successfully leveraging AI for a business use-case have nothing to do with data science expertise.
Team structure, executive expectations, and decision-making frameworks are all advantages that even some Silicon Valley startups (with tremendous AI skills) are lacking entirely.
The explicit goal of this report was the find principles and tenets that would transfer to professionals who have very little (or zero) prior AI experience.
For this reason, we drew upon three primary sources of expertise in the construction of this report:
- AI Leadership at Top Tech Firms: We’ve interviewed AI leaders and experts at Facebook, AirBnb, Google Deepmind, and other tech firms. We don’t expect most companies to be able to fully transform into tech unicorns – but many of the best-practices of applying data science in unicorn companies can be easily applied to more mundane companies once those best-practices are understood.
- PhD AI Consultants with Startup and Enterprise Experience. Most AI “consultants” are amateurs, but some have been at it for decades, and have hands on experience transferring true data science into business value (often in business environments where it’s incredibly hard to get AI off the ground).
- AI Leadership in Older, Established Companies. From retail banks to pharma giants, from manufacturing companies to trucking conglomerates – our interviews and research span to every sector.
As many of you know, at Emerj we run the largest B2B AI podcast online, “AI in Industry”, giving us extraordinary access to AI leaders and nontechnical business leaders in order to learn the critical factors for AI success. From the US Department of State – to IBM. From the largest oil and gas companies in the world – to the hottest AI startups in Silicon Valley.
A Strategic Step-by-Step Guide to Reducing Risk, Improving ROI, and Getting Started with an AI Initiative
Our goal was not to write a book, or tell a long and colorful story – but to put together the maximum value in a succinct and actionable guide, and what we’ve arrived at is something we’re truly proud of.
This compact 25-page report is broken down into five key chapters:
- Critical Executive Expectations in AI Adoption – Overcome the early hurdles to AI adoption by understanding how to assess costs and resources, and grounding your project in the right expectations of an AI project’s requirements.
- Short-Term and Long-Term AI Investment – How to lock in the near-term benefits of AI by positioning your firm or department to win the long term, and handle unrealistic ROI-based objections or unrealistic ROI expectations.
- AI Adoption Motives – Discover the three reasons for AI adoption that lead to successful outcomes and more likely ROI, and the two mistaken AI adoption motives that almost inevitably lead to wasted money, setbacks, and embarrassment.
- Timing and Purchasing of AI – Learn when to adopt artificial intelligence, what to do directly before beginning an AI initiative, and a simple, powerful framework for build vs buy decision-making.
- The Three Phases of AI Adoption – Discover the crucial importance of a post-pilot “incubation” phase for AI projects, and critical insights to turn a successful AI proof of concept (PoC) into a truly functional business solution – without making the costly errors that stop many PoCs cold.
Whether you’re a manager SVP, moving forward toward AI adoption with confidence is critical. Nontechnical professionals, such as yourself, are often hesitant to suggest AI initiatives due to a lack of strategic or technical understanding, and an unwillingness to champion a losing project – losing money and reputation at the same time.
If you’re anything like the professionals on our AI Advantage newsletter (or those who listen to our AI in Industry podcast), you want:
- To show up and “own” the conversation of AI adoption, guiding your project with the confidence of proven best-practices
- A career with more opportunity, security and income in the era of AI (even without ever having to learn to code), not less opportunity
- A simple framework for evaluating new AI opportunities, and making build-versus-buy decisions
- Proven best-practices for reducing the risk of a low-ROI AI initiative by “filtering out” poor ideas early
That’s exactly why we set out to create a simple set of best-practice standards for AI adoption in any industry, based on what’s working in companies with no previous data science expertise.
What Other Professionals Think of the “Getting Started with AI” Report
A very small group of professionals (including nontechnical managers, consultants, and AI leaders) was given early access to the full Getting Started with AI report, here’s a collection of their thoughts about what’s inside:
The goal of our research was to find the critical processes, strategies, and philosophies that allow some established companies to adopt AI successfully and relatively quickly – while their competitors waste time and money on misguided projects (or painful indecision).
30 Day 100% Money-Back Guarantee
We’ve spent months assessing past interviews, turning insights into frameworks, and speaking with our subscribers to determine the best fit for their needs – and we wanted to find a way to shoulder the risk for this product release, so that any nontechnical professional can feel comfortable making the purchase. Here’s the guarantee:
Get your copy of the report, and if you don’t genuinely believe that report will help you capture more AI opportunity for your career or business (and won’t pay for itself 20 times over), then simply send us an email to firstname.lastname@example.org, and we’ll provide you with a full refund of your purchase, no questions asked.
Purchase your copy of “Getting Started with AI – Best-Practices for AI Adoption“.