Artificial intelligence is transforming industries, and it is providing 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 managers and leaders are championing genuinely impactful AI solutions that will drive their business and career forward – most managers and 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, begun 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.
If you’re anything like the organizational leaders 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 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
Whether you’re a consultant or an internal team leader, moving forward toward AI adoption with confidence is critical. Leaders are often hesitant to suggest AI initiatives due to a lack of understanding, and an unwillingness to champion a losing project – losing money and reputation at the same time.
That’s exactly what 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.
How Companies with No Data Science Skills Can Win with AI – Critical Insights from Three Years of Research
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 companies who have very little (or zero) prior AI experience.
For this reason, I drew upon three primary sources of expertise for this report:
- AI Leadership at Top Tech Firms: I’ve interviewed AI leaders and experts at Facebook, AirBnb, Google Deepmind, and other tech firms. I 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 sectors for AI case studies and adoption into established existing businesses.
As many of you know, at Emerj we run the largest B2B AI podcast online, “AI in Industry”, and have done so for many years now – giving us extraordinary access to AI leaders and 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.
In fact, one of our Research Advisors for our latest AI in banking report was the former Head of Artificial Intelligence at HSBC, one of the largest banks in the world (a 170-year-old company spending millions on artificial intelligence, and learning critical AI lessons along the way that any business can learn from).
The goal of our research was to find the critical processes, strategies, and philosophies that allows some established companies to adopt AI successfully and relatively quickly – while their competitors waste time and money on misguided projects (or painful indecision).
A Step-by-Step Guide to Reducing Risk, Improving ROI, and Getting Started with an AI Initiative – With No Previous Data Science or AI Experience
My goal was not to write a book, or tell a long and colorful story – but to put together the maximum value in minimal words and graphics.
The intended audience is business leaders, people with very little time on their hands, and tremendous responsibilities to juggle. For this reason, I’ve kept this report remarkably concise.
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.
Purchase your copy of “Getting Started with AI – Best-Practices for AI Adoption.”