Artificial intelligence often requires data and data scientists, in order to deliver value.
But most companies leave out the fact that it also requires a process.
AI companies in Silicon Valley not only have the advantage of data science talent – but they also understand the AI lifecycle and can execute on AI projects quickly because of it.
These AI deployment frameworks and processes can be used by any business, but most companies ignore them – at their own peril.
Consultants and professional services firms with the right understanding of AI deployment best-practices can help their clients reduce risk and reduce time-to-value with AI projects. The consultants and firms who understand these fundamentals will set themselves up to become invaluable partners to enterprises who don’t understand these critical best-practices.
AI Deployment is Done Wrong Over 90% of the Time, and the Costs are Gigantic
Companies to decide to adopt artificial intelligence are often eager to get started – but without previous experience managing AI projects – they are left to learn painful lessons at every step of the process. Most of the consultants hired to help have no more understanding of AI deployment than the companies that hire them.
Among the most common reasons for failed AI deployments include:
- Lack of a clear step-by-step or phase-by-phase approach to managing AI success from proof of concept to full integration.
- Data scientists don’t know when to bring in subject matter experts to assess project results and ensure that the project leads to meaningful business results.
- Executive leadership simplifies deployment as if the AI system is just another IT tool – leading to projects that are cancelled when the lengthy reality of AI deployment becomes known.
Without expensive trial-and-error experience, or a data science and leadership team with robust experience in achieving enterprise AI ROI, companies are left to learn as they go – unprepared for how to start, or how to progress.
Deploying AI Successfully Requires Expertise That Most Companies Don’t Have
Our research across leading AI startups, tech giants, and existing enterprises successfully deploying AI has lead us to identify seven unique steps in the data science life cycle, and three unique “phases of deployment.”
Companies who know these steps are better prepared to:
- Estimate the timelines and financial resources needed to deploy any AI application or capability.
- Assemble the right mix of technical and nontechnical talent to make an AI initiative a success.
- Achieve success in every step of the AI lifecycle, from setting realistic ROI goals – all the way to integrating the AI system live within the existing tech architecture.
To assemble the steps and frameworks in this report, we dozens of experts across three experience categories:
- AI Leadership at Top Tech Firms.
- PhD AI Consultants with Startup and Enterprise Experience.
- AI Leadership in Older, Established Companies.
By determining the common success factors and best practices across these three groups – we have determined a succinct blueprint for AI deployment.
Using simple instructions and representative use-cases within businesses like yours, the AI Deployment Roadmap is designed to take the guesswork out of each step in the AI deployment journey for consultants or IT service professionals.
A Step-by-Step Guide for Deploying Artificial Intelligence, and Achieving an ROI
The objective of this guide is to provide leaders with a maximum amount of practical value, in a minimal amount of space.
This compact 32-page report is broken down into five key chapters:
- Prerequisites to AI Deployment – Reduce risk and give your firm a foundation for success by applying the four steps to take before deploying AI.
- The Living Process of AI Adoption – A quick-start summary of how business functions change before and after AI. Having these proper expectations ahead of time allows a company to adapt and take advantage of AI, instead of being caught off guard.
- The 3 Phases of AI Deployment – Apply this three-phase approach to deployment and literally double your chance of achieving an ROI. Most firms skip phase two, and never realize why their deployment isn’t working.
- The 7 Steps of the Data Science Lifecycle – A jargon-free, business leader-friendly breakdown of how to iterate with data and business processes to achieve a result (plus a full breakdown of the team members involved at each phase).
- Putting the Steps and Phases in Action – Three keys to post-deployment success with AI projects – including how to retain AI skills and processes to make each deployment easier.
Nontechnical leaders need to not only choose the right AI projects, but they also need to roll out AI in a way that will give them the best chance of near-term and long-term ROI.
That’s exactly what consulting and professional services leaders get with this practical, succinct “AI Deployment Roadmap” guide.
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 this 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 email@example.com, and we’ll provide you with a full refund of your purchase, no questions asked.
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