Real Solutions for Artificial Intelligence: A MaaxaLabs Framework for AI Adoption and Governance
This holistic, research-based adoption and governance framework takes clients from disorganized, risky AI use to holistic, proactive, and aligned cross-organizational governance
MaaxaLabs’ Strategic AI Framework (SAiAF) offers leaders a dynamic toolkit that allows them to get proactive about AI strategy, early on. The framework documents AI adoption across an organization, systematically assesses values and risks throughout its AI journey, sets a strategic value statement, and identifies and resources opportunities for responsible governance and innovation.
AI adoption is speeding forward with the widespread introduction of large language models (LLMs), predictive and generative tools, operational tooling and other imported and/or custom AI applications throughout modern organizations. Businesses and teams have taken on a series of documented risks in this fast-breaking environment and are calling for a strategic AI framework they can trust: the toolkit they need to harness controlled, aligned, and holistic strategy for AI responsibility.
A Clear Business Problem for Many Organizations
When it comes to te\he ways AI use cases affect a constellation of stakeholders, we essentially have to break down traditional "silos" and look holistically across the entire organization and the AI product adoption/development journey to ensure alignment, understanding, a shared vision and vocabulary for proper security, and governance and risk management from the use of these revolutionary tools.
Businesses have experienced a series of challenges in the race to AI capability and efficiency:
Uncontrolled adoption: Multiple AI initiatives and uses across an organization leads to a sense of disorganization and reactivity
Misalignment: Cross-organizational confusion, lack of visibility
Performance stumbles: AI not performing as expected, insufficient data pipelines, disruption to business units and processes, model management
Risk: unexpected snafus and liabilities that can lead to cost, legal issues, bad PR, and more
The complexity of the organizational AI adoption and governance journey means that mid-sized business leaders struggle to find alignment and oversight of simultaneous processes across business units. Solutions for large enterprises involve long-term vendor lock-in; smaller orgs have more cross-functional visibility into AI tool use. Current consulting solutions address parts of the problem for various decision-makers across an organization but lack the intergenerational, interdisciplinary, interdepartmental, democratized, and holistic governance that is part and parcel of the value of cloud transformation.
SAiAF Addresses AI Fundamentals
MaaxaLabs turned to the lab to identify a series of specific needs on the part of mid-sized businesses through a series of case studies and a state-of-the-industry survey. From this discovery, they synthesized a groundbreaking end-to-end journey map that captures what matters to leaders: AI stakeholders and users, points of risk and misalignment, and opportunities for action that promise both immediate and strategic, measurable impact toward AI responsibility.
In turn, we have developed and refined a five-part toolkit - an Assessment | Workshop | Action Items | Playbooks | and Governance Tooling designed to raise the AI bar across an organization, and identify risks and opportunities proactively. We put these tools into action using a consulting process that applies and adapts the framework to specific customer contexts.
The SAiAF solves the business problems outlined above:
Uncontrolled adoption: identifies a strategic AI values statement based in organizational needs and prioritizes action items to drive strategic adoption
Cross-organizational confusion: produces an end-to-end AI journey map that is the basis for strategic alignment, decision-making and communication
Adoption stumbles: identifies potential performance disruptions and risks due to data, reliability, or other issues across an organization; identifies governance players and duties
Risk: identifies legal, security, customer trust, and other governance risks
The SAiAF Gets the Job Done
When a client calls us for solutions to AI adoption and governance challenges, we begin with our assessment process to grasp, through quantitative and qualitative approaches, the unique problems and risks their AI journey involves. We synthesize them and place them in our framework to identify the job to be done.
We then work with stakeholders to craft a strategic AI values statement that sets goals for AI governance and guidelines for action.
Using our patented framework, an end-to-end AI adoption and governance journey map, we workshop with cross-functional stakeholders to identify AI value, risk, and opportunities according to each distinct phase of AI processes. In doing so, we drive alignment on problems to be solved, mapping them carefully to the values statement. We then categorize and prioritize opportunities using LUMA innovation methods, delivering immediate action items orgs can take to raise their AI governance bar.
We follow up with a series of deliverables, a completed end-to-end journey map and recommendations, and a custom package of trainings, playbooks, assessments, and other tools to support sustained AI responsibility and value.
