In the first and second part of this three-part series, we looked at definitions and use cases of generative AI. We’ll now explore the approach IBM Consulting takes when embarking upon AI projects.
As business leaders investigate the best way to apply generative AI to their enterprise at scale, they need to consider trusted vendors and partners with expertise in data, machine learning and AI, data and AI governance, and proven capabilities of scaling applied AI within enterprises across industries and geographies.
IBM Consulting has capabilities in Foundation Models delivery at scale.
IBM Consulting brings industry expertise to understand the regulatory constraints and how to derive value with AI by augmenting specific workflows.
The mission of IBM Consulting is to drive business transformation with hybrid cloud and AI in a way that is valuable and responsible. We formally stood up our AI Ethics Board in 2018 to ensure that AI systems created at IBM are developed and deployed ethically. The board is comprised of senior leaders from research, business units, human resources, diversity and inclusion, legal, government and regulatory affairs, procurement, and communications, who have the authority to direct and enforce AI-related initiatives and decisions.
In the same year, IBM published its own principles of trust and transparency and offers them as a roadmap to others working with and implementing artificial intelligence. They focus on the following:
The purpose of AI is to augment human intelligence;
Data and insights belong to their creator; and
New technology, including AI systems, must be transparent and explainable.
These are the values by which we approach any work involving artificial intelligence: to enhance—not replace—human intelligence; to deliver client success without the requirement that clients relinquish rights to their data—nor the insights derived from that data—even when it is stored or processed by IBM; to provide clarity about who trains our AI systems, what data was used in that training and, most importantly, what went into an algorithm’s conclusions or recommendations. These principals are further supported by our defined pillars of trust, which we have dedicated time and resources to research, implement and disseminate:
Explainability: How an AI model arrives at a decision should be able to be understood
Fairness: AI models should treat all groups equitably
Robustness: AI systems should be able to withstand attacks to the training data
Transparency: All relevant aspects of an AI system should be available to the public for evaluation
Privacy: The data used in AI systems should be secure, and when that data belongs to an individual, the individual should understand how it is being used
Generative AI and large language models (LLMs) introduce new hazards into the field of AI and we do not claim to have all the answers to the questions that these new solutions introduce. IBM understands that driving trust and transparency in artificial intelligence is not a technological challenge, it is a socio-technological challenge.
80% of efforts in artificial intelligence get stuck in proof of concept for reasons ranging from misalignment to business strategy, to mistrust in the model’s results. IBM brings together vast transformation experience, industry expertise, proprietary and partner technologies and IBM Research to work with clients wherever they are on their AI journey. With this combination of skills and partnerships, IBM Consulting is uniquely suited to help businesses build the strategy and capabilities to operationalize and scale trusted AI to achieve their goals.
Currently, IBM is one of few in the market that both provides AI solutions and has a consulting practice dedicated to helping clients with the safe and responsible use of that AI. IBM Consulting helps clients establish the organizational culture needed to safe-handle AI, build multi-disciplinary and diverse teams and think through risks and unintended effects. We work with businesses to identify low-risk uses cases, to assess, educate, and communicate across the organization and to stand up their own internal AI ethics board.
IBM embraces an open ecosystem approach, working with IBM technology as well as a diverse set of ecosystem partners including AWS, Microsoft Azure, Google Cloud, Salesforce, and others, designing intelligence and productivity across mission critical workflows and systems. IBM Garage methodology co-creates, co-executes, and co-operates with enterprise teams to quickly ideate, pilot, test and scale projects. In the co-innovation phases, we employ ethics-driven exercises to ensure that our intentions match our actions.
IBM can help companies put AI into action today to re-imagine workflows with AI, to automate end-to-end enterprise processes, to replace mundane tasks to achieve productivity gains with AI-driven decision making, personalize employee and customer interactions, and more. Our AI services include:
Analytics and AI to build, train and deploy AI and ML models for your business. We will work together to integrate bespoke models into your operations, continually revise and optimize them over time.
AI and Automation Advisory to integrate best of breed AI and Automation solutions for full stack observation and orchestration, driving highly automated and predictive IT Operations across business processes, applications and hybrid clouds.
Full-Service Automation to leverage IBM’s full suite of technology and services platforms that enables straight through “touchless” processing with minimal human involvement.
Senior Partner - Global AI & Analytics Leader, IBM Consulting
Manish Goyal is a VP & Senior Partner and the Global Leader of IBM Consulting’s Artificial Intelligence & Analytics practice. In this role, Manishâ...