Our AI Impact

 for the health of people



 Our AI Impact

 for the health of planet



 Our AI Impact

 for the health of business



“The work [with Synaptiq] is unprecedented in its scale and potential impact,” Mortenson Center’s Managing Director Laura MacDonald MacDonald said. “It ties together our center’s strengths in impact evaluation and sensor deployment to generate evidence that informs development tools, policy, and practice.” 
Read the Case Study ⇢ 


    ⇲ Implement & Scale
    A startup in digital health trained a risk model to open up a robust, precise, and scalable processing pipeline so providers could move faster, and patients could move with confidence after spinal surgery. 
    Read the Case Study ⇢ 


      Thwart errors, relieve in-take form exhaustion, and build a more accurate data picture for patients in chronic pain? Those who prefer the natural albeit comprehensive path to health and wellness said: sign me up. 
      Read the Case Study ⇢ 


        Using a dynamic machine vision solution for detecting plaques in the carotid artery and providing care teams with rapid answers, saves lives with early disease detection and monitoring. 
        Read the Case Study ⇢ 


          man-wong-aSERflF331A-unsplash (1)-1
          This global law firm needed to be fast, adaptive, and provide unrivaled client service under pressure, intelligent automation did just that plus it made time for what matters most: meaningful human interactions. 
          Read the Case Study ⇢ 



            Mushrooms, Goats, and Machine Learning: What do they all have in common? You may never know unless you get started exploring the fundamentals of Machine Learning with Dr. Tim Oates, Synaptiq's Chief Data Scientist. You can read and visualize his new book in Python, tinker with inputs, and practice machine learning techniques for free. 

            Start Chapter 1 Now ⇢ 


              How Should My Company Prioritize AIQ™ Capabilities?





                Start With Your AIQ Score

                  5 min read

                  Three Ways to Failure-Proof Your AI Roadmap

                  Featured Image

                  Manage Risk & Achieve Rewards

                  Artificial intelligence (AI) adoption is accelerating, but trends are misleading.

                  IBM reports that 35 percent of companies use AI in 2022, up from 31 percent in 2022. Gartner predicts that AI software revenue will total $62.5 billion in 2022,  an increase of 21.3 percent from 2021.

                  What are these trends hiding? The majority of AI initiatives end in failure. 

                  Many organizations achieve a (very) positive return on investment from AI. Others fall short because their leaders don’t understand the challenges unique to AI. Together, we’ll navigate these challenges to ensure that your AI roadmap is failure-proofed to achieve your goals. 

                  The following tips have helped Synaptiq clients across industries succeed in their AI endeavors:

                  Inquire About AI Roadmapping for Your Business

                  I. Set an Accurate Budget

                  How much will your AI roadmap cost to execute? 

                  If an AI initiative is “uncharted territory” for your organization, answering this question may pose a challenge. We recommend that you begin by setting achievable business goals and establishing specific, quantifiable benchmarks for success. For example, does “solving” your problem mean…

                  • …achieving ___% prediction accuracy to save ___% on expenses?
                  • …analyzing ___ with ___% accuracy by a target date?

                  If you have multiple goals, AI may be able to accomplish some for a reasonable cost, while others may require an alternative solution. We can refer to the former as “viable” use cases and the latter as “nonviable” use cases. Viability heavily depends on your data and your team’s ability to support a certain use case. If you don’t have that support, the use case is nonviable. 

                  Organize your viable use cases in order of priority. Estimate the cost of executing an AI initiative to address each viable use case. Your budget is the sum of those costs.

                  II. Convince In-House Skeptics

                  Skepticism can shred an AI roadmap even before the journey begins. Here’s a term that every business leader should keep top of mind: cultural readiness. “Culture” is the shared beliefs, values, and assumptions that distinguish a team from a group of strangers. Your team is “culturally ready” for a change when their shared beliefs, values, and assumptions align with it.

                  For example, imagine two teams: Team A and Team B. Team A recruits for flexibility and rewards members for innovating. By contrast, Team B recruits for risk adversity and neither expects nor encourages members to innovate. Team B prefers “the ‘traditional’ way of doing things.”

                  Tasked with AI adoption, Team A will rise to the challenge. Team B will rebel against it. 

                  An AI roadmap that disregards cultural readiness will fail when team members inevitably (i) resist execution and (ii) resist using the new technology post-execution. 

                  Unused technology is a waste of investment, even if it works. Therefore, a failure-proof AI roadmap should include a detailed plan to improve cultural readiness (if necessary).

                  III. Conduct Feasibility Studies

                  Finally, we suggest your AI roadmap incorporate a feasibility study: a low-cost, limited commitment “pilot” AI initiative execution. A feasibility study will fortify your roadmap by exposing unforeseen challenges and checking your costs, benefits, etc. estimates. Many AI adoptions fail before implementation because expectation doesn’t match reality. Feasibility studies are not optional (if you want to succeed).


                  humankind of ai


                  About Synaptiq

                  Synaptiq is an AI and data science consultancy based in Portland, Oregon. We collaborate with our clients to develop human-centered products and solutions. We uphold a strong commitment to ethics and innovation. 

                  Contact us if you have a problem to solve, a process to refine, or a question to ask.

                  You can learn more about our story through our past projects, blog, or podcast

                  Additional Reading:

                  Too Much Data, Too Little Time: A Business Case for Dimensionality Reduction

                  Introduction to Dimensionality Reduction

                  High-Dimensional Data

                  Imagine a spreadsheet with one hundred columns and...

                  BETTER Customer Review Sentiment Analysis: A Business Case for N-grams

                  Sentiment analysis is a useful tool for organizations aiming to understand customer preferences, gauge public...

                  Smart and Safe Innovation: Synthetic Data for Proof-of-Concept Projects

                  In the ever-evolving landscape of technology, innovation and experimentation are key drivers of success. However, the...