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Generating AI ROI – Best Practices and Frameworks

Most of the questions of our executive readers at Emerj.com ask can be put into two categories:

“What’s possible with AI in my sector?”

and

“Where is the ROI of AI in my sector?”

Applying AI is challenging, mistakes are costly, and seeing a return on investment (ROI) from AI projects is something that every business leader and consultant is looking for.

ROI is being measured incorrectly, however – and because of this – most companies are setting themselves up for a failed first initiative.

Most AI Projects Don’t Achieve an ROI of Any Kind – and it Has Little to Do with Data or Data Science Talent

It’s true that some AI projects fail because the company didn’t have adequate data, or because it lacks the right data science talent.

However – many projects fail because the measurements of success were established incorrectly.

Most companies set out to measure AI ROI in one way:

  • Short-term financial return (an amount of percent of money saved or made)

While this approach can be helpful if measurements are established well from the start, it is woefully incomplete.

Artificial intelligence requires a firm understanding of strategic advantage, of how AI capabilities and improvements in data infrastructure, teams, and skills can be improved.

Without a tie to long-term advantage, many attempts at AI ROI turn into what we call “toy applications” – individual instances of AI that exist as experiments but add neither financial value of significant learning and skill to the company who spends money on them.

Leaders who are deciding on AI projects and initiatives want to:

  • Understand the ROI potential of an AI project before sinking money into developing it
  • Have tangible, measurable benchmarks to hold their AI projects accountable
  • Leverage each AI application towards long-term AI agility and AI advantage (in addition to short-term measurable results)

And that’s exactly what the new Generating AI ROI report provides.

How Nontechnical Leaders Can Set Up AI Initiatives for Success

Most leaders don’t know how to establish expectations, measurements, and frameworks for AI ROI becuase they simply haven’t had the chance to try.

AI is new to many sectors, and most pilot projects are barely getting started – so actual experience with reliably rolling out AI is a skill set held by a limited few categories of experts around the world.

Our Generating AI ROI report draws from over 50 interviews with such AI experts from across countries and geographies. These experts fall into three broad categories:

  • 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.

The objective of our ROI research was to determine the frameworks and steps involved in predicting and ensuring AI ROI in the short-term and the long-term.

Ultimately, we aim to give leaders the ability to see real traction with AI projects, while most of their competitors waste time and money on chasing the wrong measurements, without a grounded strategy for delivering AI value.

If you’re to have the best chance of a short-term financial ROI from an AI project – this report will be remarkably helpful.

If you’re looking to leverage AI for a long-term strategic advantage – using each new AI initiative as momentum for winning in the marketplace – then this report will be absolutely essential.

A Structured Guide to Delivering Financial and Strategic ROI with AI for Business Leaders

Generating AI ROI - Best Practices and Frameworks preview

The ROI report provides a set of frameworks to set an AI project up for success – breaking down the complex process of AI ROI into “strategic” and “measurable” components.

Our readers are business leaders, innovation and strategy leaders, and cutting-edge consultants who want maximum business impact with minimum fluff – and that’s how this report is structured.

This compact 28-page report is broken down into four key chapters:

  1. Effectively Framing AI ROI – Discover the most critical hurdles to near-term ROI, and best practices for deciding on AI projects based on their ROI potential.
  2. Achieving Strategic ROI – Position an AI project to deliver long-term results in line with core business priorities. In this chapter, you’ll learn three strategic ROI exercises to align AI initiatives to an AI advantage now – and into the future.
  3. Achieving Measurable ROI – Use the six categories of measurable ROI as anchor points for any AI project – and set yourself up to overcome many of the early hurdles that AI initiatives face.
  4. Making ROI Happen – The Critical Success Factor – Team collaboration and alignment steps for nontechnical executives, including a walk-through of key team collaboration points across the six phases of AI deployment.

Purchase your copy of Generating AI ROI – Best Practices and Frameworks.

Contact:
Email: reports@emerj.com
Phone: 1-617-945-8567