Artificial intelligence gives brands the power to drive productivity, profitability, and performance. The early adopters will have a distinct, and potentially insurmountable, competitive advantage.
But AI in marketing is a crowded and confusing space, with lots of hype and buzzwords. It’s hard for marketers to find the AI technologies they can trust to move their brands, and careers, forward.
We’re here to help.
We launched the Marketing Artificial Intelligence Institute in November 2016 with the vision to make AI more actionable and approachable for modern marketers.
Since that time, we’ve published more than 200 blog posts designed to help marketers turn AI into their competitive advantage. As part of the content strategy, we’ve written spotlights on 40+ AI-powered companies with more than $1 billion in combined funding.
But that’s just the start. We’re tracking more than 750 sales and marketing AI companies with combined funding north of $4 billion. In 2019, we’re also hosting the Marketing Artificial Intelligence Conference (MAICON), an event for practitioners and leaders seeking to drive the next frontier of digital marketing transformation within their organizations.
While AI marketing technologies are potentially years away from self-running, self-improving autonomous systems, many AI tools available today can still have massive impact on your business.
You can turn AI into your competitive advantage with strategic consulting from the Marketing Artificial Intelligence Institute.
We offer two standard consulting models for planning, piloting, and scaling marketing AI within organizations: the Problem-Based Model and the Use Case Model. See below for details on both models.
1) The Problem-Based Model
In the problem-based model, the client has a known pain point, a challenge that they believe may be solved more efficiently, and at scale, with AI.
For example: Organic traffic is flat the last 12 months despite a 2x increase in blog posts and $100,000 in additional marketing budget dedicated to content.
Our team of marketing strategists, data scientists, and machine learning engineers follow a fact-based, hypothesis-driven methodology to solve the problem.
The 10 steps in the problem-based consulting model are broken into two phases: Discovery and Planning. Phase 1 Discovery defines and validates the problem. Phase 2 establishes the strategic plan to resolve it.
The end deliverable is a final report with key findings and recommendations, and an implementation plan that outlines recommended tools, and details the projected roadmap, timelines, and costs.
2) The Use-Case Model
The use-case model is ideal for identifying AI pilot projects, or to drive efficiency and performance of existing activities that are known to consistently require significant time and money.
In steps 1 - 4 we narrow the focus down to a maximum of 10 potential use cases by considering two primary factors:
A single use case is selected in step 5, and then steps 6 - 10 determine the action plan moving forward for that use case. If more than one use case will be pursued, steps 6 - 10 are repeated for each use case.
The end deliverable is a final report with key findings and recommendations, and an implementation plan that outlines recommended tools, and details the projected roadmap, timelines, and costs for a single use case.