Welcome
Where Are You In The Great AI Race?
The starting gun has sounded, and leaders are already scaling AI throughout their organizations and reaping the business benefits. In this crucial race for competitive differentiation and business success, are you leading the pack or fighting to keep up? Take our short self-assessment to find out how you compare to your peers and receive actionable steps you can take to advance your AI and ML readiness.
The assessment will yield customized results and recommendations based on your responses and should take no more than 2 minutes to complete.
Questions
Roughly how many people at your organization have the data science skills and expertise to build predictive, ML, or AI models (deep learning, natural language processing [NLP]/understanding, speech, computer vision, etc.)?
Questions
To what degree are the following parts of your organization investing in predictive analytics, ML, and AI capabilities? (Select one per row.)
Questions
How challenging is it to put AI/ML models into production? Models that should be put into production are:
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Roughly what share of your AI/ML models put into production are monitored regularly for data drift or decay in model performance?
Questions
Roughly what share of your AI/ML models put into production are regularly retrained to ensure they are accurate?
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What are your plans for investing in the following data and analytics technologies? (Select one per row.)
Results Overview
Results Overview
Your maturity result:
Beginner
Your score means you are most likely just beginning to scale AI and ML at your organization. Now is the time to make AI and ML a priority for your data and analytics investments: 80% of firms that are advanced with their AI/ML readiness say that AI and ML will be the most important factor in their business competitiveness in three years. While your data science team is likely small, here are some tactical recommendations to drive business value and get the most out of your initial AI and ML investments.
- Create a data science center of excellence. A center of excellence helps to democratize AI and ML in your organization and standardizes data practices to help get more high-quality models into production. Fifty-one percent of your intermediate and 71% of your advanced competitors have already implemented or are expanding their COE, while just 19% of your beginner peers have done so. Creating a COE is the fastest way to get your AI/ML practice off the ground.
- Encourage cooperation between data science, IT, and the business. Critical to success moving AI into the business is to foster collaboration and trust between your data science team building and implementing AI/ML models and the users trying to take those data insights and implement changes in the business. Nearly half of your intermediate competitors and two-thirds of advanced firms are undertaking initiatives to encourage cooperation between these often-siloed groups.
- Democratize AI and ML with automated machine learning and easy-to-use platforms. Most of your beginner peers have already made initial investments into machine learning and customer analytics platforms, so if you have not, that should be your first step. The next step many are taking is investing in automation platforms for ML. Automation is especially important to smaller data science teams because it allows data workers to easily create and implement new models and increases the productivity of your limited data science resources.
- Invest in technology to close the insights-to-action gap. The stage of the AI to ML lifecycle where your beginner peers encounter by far the most challenges is turning the insights from AL/ML initiatives into actionable changes to the business. To help close this insights-to-action gap, your more advanced competitors are increasing investment in new technologies like decision management and optimization and simulation solutions to find the best combination of business actions based on inputs from AI and ML models.
Intermediate
Your score means you are well underway with scaling AI and ML to drive business value at your organization. You are likely still growing your data science talent, so here are some tactical recommendations to drive business value and get the most out of your initial AI and ML investments.
- Focus on expanding business use cases. As you scale AI and ML into different areas of the business, elevate your use cases from those focused on driving efficiency to those driving business value. Your more advanced competitors are focused on use cases that improve the customer experience they can deliver, improve strategic decision making, and develop new revenue streams.
- Invest in technology to close the insights-to-action gap. To help close the insights-to-action gap, your more advanced competitors are increasing investment in new technologies like decision management and optimization and simulation solutions to find the best combination of business actions based on inputs from AI and ML models.
- Invest in ModelOps solutions to overcome model development and maintenance challenges. Your AI/ML projects may be underperforming due to challenges maintaining those models to ensure their accuracy. Your intermediate peers say model development and maintenance is the most difficult phase of the AI/ML lifecycle. ML operations solutions are designed to help both accelerate the deployment of AI and ML models and monitor them in production, making it easier to retrain them when conditions change.
- Expand your data collaboration initiatives. Advanced AI/ML firms further expand and democratize AI by creating data science centers of excellence and undertaking initiatives designed to foster collaboration both within your analytics teams and across the business and IT. Sixty-nine percent of your advanced competitors have already implemented or are expanding their COE, so if you do not have one, this is the time to act.
Advanced
Congratulations, your score means that your AI and ML practice is thriving today. You are ahead of the curve on scaling AI and ML through your organization and driving business value with your initiatives. This is no time to rest on your laurels, however. Your advanced peers believe AI and ML is the most important factor to being competitive in the future and are investing with this mindset: 60% of your peers say they will invest at least $5M in AI/ML platforms in the next year. To continue to grow your practice effectively and improve your business results you should:
- Expand AI’s influence and boost productivity with automation. Tools like autoAI can help turn data workers into de facto data scientists with capabilities that automate feature engineering, model selection, parameter tuning, and model deployment. Your advanced peers see automation for AI and ML as by far the most valuable features of AI/ML platforms. These features not only allow data workers to quickly and easily develop AI/ML models but lets your precious data science resources work more productively and work on higher-value use cases.
- Drive trust in AI with governance and transparency. Drive trust and reduce risk while also improving business outcomes by investing in solutions for tracking model lineage, monitoring your models for data drift, and declining accuracy, as well as new techniques for providing explainability, bias detection, and mitigation features. This is another top feature for advanced firms to get the most value from their AI/ML platforms.
- Invest in ModelOps solutions to manage models at scale. Implement platforms with ModelOps capabilities that enable you to take models developed across a wide range of ML tools and frameworks, rapidly deploy them across hybrid environments (on-premises, private cloud, and multiple public clouds), retrain, and redeploy models as part of a continuous improvement process.
View your detailed results
Next Steps:
Thank you for your answers about your AI/ML readiness.
Based on what you indicated, you are in the initial stages of your journey to AI. IBM recommends you consider the following steps to evolve into a more predictive, digitally automated enterprise.
Map and simplify your journey to AI with the IBM AI Ladder, an eBook that guides you in how to collect, organize, and analyze data to yield insights with AI and infuse AI-enhanced capabilities throughout your business.
Take a key primary step in a successful journey to AI: Make your data AI-ready. Modernize your data estate wherever your data lives by using an open, extensible data and AI platform that runs on any cloud. Discover why there’s no AI without IA — a solid information architecture that provides the foundation for artificial intelligence. Learn how IBM Cloud Pak for Data, on Red Hat OpenShift, delivers the architecture that accelerates your journey to AI.
Get a video overview of the journey to AI and see a short video on how Cloud Pak for Data makes your data AI-ready, wherever your data and models reside. Watch how Watson Studio automates the AI lifecycle.
Engage an IBM expert in a free consultation to learn how IBM can help you get where you need to be with AI.
Next Steps:
Thank you for your answers about your AI/ML readiness.
Based on what you indicated, you are in an intermediate stage on the journey to AI. IBM recommends you consider the following steps to evolve into a more predictive, digitally automated enterprise.
The IBM Data Science Elite team has helped hundreds of organizations deploy AI and deliver business value using it. They’ve distilled best practices learned from these engagements into a free eBook. Download Agile AI: A Practical Guide to Building AI Applications and Teams.
As you build your teams, boost your organization’s AI and ML expertise with this playlist of on-demand webinars. Each is rich in demos and guidance to achieve specific results.
As you champion the use of AI in your organization, you may want to strengthen the business case for moving ahead by quantifying the potential value of your AI investments.
As much as 80% of data scientist time is spent on low-value data prep and model development tasks. Learn why AutoAI capabilities in Watson Studio won the Best Innovation in Intelligent Automation Award because they automate these tasks and free your AI teams to do higher-value work. See a video overview.
Engage an expert: Schedule a one-on-one consultation with IBM experts who have worked with thousands of clients to build winning data, analytics, and AI strategies.
Next Steps:
Thank you for your answers about your AI/ML readiness.
Congratulations. Based on what you indicated, you are in an advanced stage of the journey to AI. IBM recommends you consider the following steps to further evolve into a more predictive, digitally automated enterprise.
Learn how high-growth leaders in AI are demonstrating groundbreaking results, and get the details on what you should look for in a data and AI platform in the IBM CxO Guide to accelerating growth at scale with modern AI.
As much as 80% of data scientist time is spent on low-value data prep and model development tasks. Learn why AutoAI capabilities in Watson Studio won the Best Innovation in Intelligent Automation Award because they automate these tasks and free your AI teams to do higher-value work.
Build AI governance. Trust and transparency in AI are critical. Before you can scale AI, you need to be able to track, monitor, and manage AI models in production. See why IBM Watson OpenScale won a Firestarter award from 451 Research for its ability to detect and correct AI model bias and drift and explain outcomes during runtime. Its interface enables business users to better understand their models, and it frees data scientists to work on higher-value tasks. Learn more with this Watson OpenScale video and webpage.
Explore the benefits of combining predictive analytics (who is most likely to buy?) and prescriptive analytics (what is the optimum amount of marketing budget to spend to motivate their decision?). Learn how technology can help make critical decisions involving thousands of variables and millions of alternatives. Read a technical validation from ESG that concludes, “IBM’s analytics portfolio has been designed to support all organizations’ analytics needs, including descriptive, predictive, and prescriptive solutions.”
Engage an expert: Schedule a one-on-one consultation with IBM experts who have worked with thousands of clients to build winning data, analytics, and AI strategies.
Methodology, Disclaimers and Disclosures
Methodology, Disclaimers and Disclosures
Methodology
Methodology
In this study, Forrester conducted an online survey of 316 firms in the US, the UK, France, and Germany with $250M or greater annual revenue to evaluate the current state and challenges of artificial intelligence and machine learning. Survey participants included business and IT decision makers responsible for AI, ML analytics, or automation at their organizations. The study was completed in August 2019.
Disclaimers
Although great care has been taken to ensure the accuracy and completeness of this assessment, IBM and Forrester are unable to accept any legal responsibility for any actions taken on the basis of the information contained herein.
Disclosures
This interactive tool is commissioned by IBM and delivered by Forrester Consulting.