As AI continues to reshape industries and redefine competitive advantages, the ability to scale AI initiatives effectively has become a critical differentiator. Despite significant investments and efforts, many AI projects struggle to move beyond the experimental phase and into production. This high rate of difficulty in scaling underscores the complexity of enterprise-wide AI implementation and the need for a comprehensive strategy to overcome the hurdles that stand in the way of success.
There are seven critical challenges that businesses must conquer to make AI at scale a reality. From aligning AI initiatives with core business strategies to addressing the AI talent shortage, these challenges represent the key battlegrounds where the future of enterprise AI will be decided.
By understanding and addressing these critical factors, organizations can dramatically increase their chances of successfully scaling AI and unlocking its full potential across their operations.
Failure to integrate business strategies
AI POCs often fail to align with the strategic business goals of the company. While these projects may be innovative and interesting, they frequently do not receive the necessary attention and funding because they are not connected to the company’s strategic agenda.
Strategy should always come first. To successfully earn stakeholder alignment, development of any AI initiative must be tied to a business problem that needs to be solved. Executive sponsorship is an overlooked component of scaling AI. If you cannot gain champions for initiatives in the business, you’ll find yourself stuck in a perpetual loop of proof-of-concept projects that never get off the ground. Otherwise, POCs will never get to the scale stage, they wind up becoming science projects that are no more than interesting widgets.
Another lens is what business problems are unique to the business, versus what are generally common to businesses of similar scale. If the problem is not unique, there needs to be a competitive advantage to solving it versus waiting for an off-the-shelf solution. But where the business problem is unique and provides significant business advantage, investing more aggressively can create unique first mover advantages.
Doing AI for the sake of keeping up with the Joneses is not enough. Always begin with the business case first, efforts should provide meaningful opportunity for new revenue or reduce costs.
Lack of strong data foundations
The age-old adage of “junk-in, junk-out” still lives on when deploying AI at scale. Effective AI requires a solid data foundation, but often the necessary data is trapped in siloed systems, making it difficult to support AI use cases at scale. In fact, 41% of data leaders grapple with siloed operating models, hampering data accessibility and collaboration1.
These silos can be broken down with thoughtful data governance, which is critical for scaling AI initiatives beyond proof-of-concept projects. By establishing clear policies, procedures, and responsibilities for data management, data governance ensures that data is handled consistently and securely across the organization, enabling teams to work together effectively and access the data they need to support AI initiatives.
Governance ensures that the data used to train and feed AI models is accurate, consistent, secure, and compliant with relevant regulations. Without proper data governance, AI initiatives can face numerous challenges, such as:
- Poor data quality leading to inaccurate predictions and flawed decision-making.
- Inconsistent data across different systems hindering the integration of AI models into existing workflows.
- Inadequate data security measures, exposing sensitive information, and breaching privacy regulations.
- Organizational barriers to deployment by not having approved architectural patterns for AI.
Effective data governance provides a framework for managing data throughout its lifecycle, from acquisition and storage to processing and disposal. It also enables organizations to maintain data lineage, track data provenance, and ensure data integrity, which are essential for auditing and compliance purposes.
Moreover, data governance fosters collaboration between stakeholders, including data owners, data stewards, and data consumers. It promotes a shared understanding of data definitions, quality standards, and usage guidelines, enabling teams to work together effectively.
By fortifying their data governance, organizations can ensure reliable, high-quality data inputs for AI systems, enabling successful scaling from proof-of-concept to production deployments and unlocking the transformative value of AI across their operations.
Technical challenges in enterprise AI
Scaling AI within an enterprise has technical challenges not seen in more traditional computing deployment. There are three key areas where technical challenges can put the brakes on your AI projects; development phases, scaling and computing costs, and differing modalities.
Development phases
AI lives in the phases of algorithm experimentation design, model training, and model inference. These are not at all similar to the traditional development, testing, and production phases typical to enterprise systems. However, control techniques such as continuous integration and deployment can still be applied.
Scaling and computing costs
Scaling of production models is very different than traditional application scaling. Not all models are financially viable for all use cases. Generative AI techniques, for example, are quite literally computationally constrained by the availability of computing power – something that we have not had significant concerns about in decades.
Differing modalities
New modalities to manage present new quality challenges. The most challenging is managing quality in probabilistic model outputs– ensuring that the quality level is consistent over time in production use.
Secondary, but also challenging, is determining when models are good enough and are subject to diminishing returns. These modalities are a stark difference from traditional software and analytical queries where they either work performantly or don’t and can be tested consistently throughout the lifecycle.
Speed of innovation versus the pace of advancement
The rapid pace of innovation complicates decision-making. With constant advancements and solutions based on current large language models there is fear that solutions quickly become obsolete as newer, more powerful models emerge. The key question is: how can you develop a solution that can stand the test of time and embrace advancement in an environment characterized by such rapid change?
Speed of innovation is a critical factor in a company’s ability to successfully scale their AI efforts. Rapid iteration and experimentation are crucial for developing and refining AI models and applications, allowing companies to quickly prototype, test, and iterate on AI solutions. This agility enables organizations to adapt to changing business needs, market conditions, and customer preferences, ensuring that their AI solutions remain relevant and valuable.
Moreover, the AI field is rapidly evolving, with new techniques, frameworks, and best practices emerging regularly. Companies that can swiftly adopt and integrate the latest AI innovations into their workflows and pipelines gain a competitive advantage in scaling AI capabilities. Walking the line of “good enough” performance using current models versus investing in techniques such as model fine-tuning which represents more sunk cost in a specific model are important decisions to get right for keeping up with innovation and getting business value today.
Faster innovation cycles also enable companies to identify and address issues or limitations in their AI systems more efficiently, facilitating smoother scaling and deployment across the organization.
Driving change management
Implementing AI requires significant changes in roles and processes, often leading to organizational resistance. For example, Blend’s recent work with a client on hyper-personalization necessitated a complete overhaul of their content creation process, impacting various roles within the organization.
Change management is a critical factor in successfully scaling AI initiatives within large enterprises. Data leaders face the challenge of driving organizational change and fostering a data-driven culture. A recent study revealed that 62% of data leaders report difficulty in changing organizational behaviors and attitudes toward data-driven decision-making4.
A primary obstacle is the presence of siloed operating models, which hinder data accessibility and collaboration. To overcome these change management hurdles, data leaders must act as change agents, leading the transformation towards a data-driven culture. This involves managing change, fostering data and AI literacy throughout the organization, and focusing on high-impact, proven value initiatives.
AI talent: The scramble and the shortage
The rapid adoption of AI technologies has created significant opportunities for businesses, but scaling AI efforts faces a critical talent challenge. With 70% of companies struggling with AI implementation due to talent shortages, leading to average project delays of 7 months, addressing this issue is paramount3.
The solution lies in prioritizing upskilling and reskilling through comprehensive, tailored AI training programs. These programs should be designed to meet the specific needs of different roles within the organization, from data scientists to business analysts. Partnering with experts can significantly enhance these training efforts, providing industry-specific insights and cutting-edge knowledge.
The urgency is clear: 67% of employees feel they lack adequate training opportunities2. By implementing role-specific, hands-on training programs, companies can rapidly close skill gaps and boost employee confidence in AI technologies.
By investing in tailored AI training, companies not only address the immediate talent shortage but also create a culture of continuous learning and innovation, essential for long-term success in the AI-driven business landscape.
Trust is the ultimate enabler
Trust manifests itself as a challenge in AI in three main ways:
Fear, uncertainty, and doubt
There are a lot of claims on how large the impact of this AI wave will be, which is creating fear of the impact it will have on jobs and quality of work, uncertainty on outcomes, and doubt that it can be as game changing as it sounds.
Trust in AI effectiveness
The nature of AI is probabilistic results – not repeatable deterministic, and as such, results will not be entirely consistent. Use cases with high human interaction are more subject to the known effectiveness gaps, creating potential frustration for users.
Safety and bias
AI can easily have biases and create concerns around enterprise risk. Whether concerns about IP ownership or copyright protection, or how bias may surface in solutions– there are important considerations and decisions needed on adoption. These need to be clear choices and well communicated.
In the race to implement AI, data leaders face a critical challenge: 40% struggle with employee adoption1. The key to overcoming this hurdle? Trust.
To successfully scale AI, organizations must build trust through three crucial lenses:
- Job security: Emphasize AI’s role in augmenting human work, not replacing it. Show how AI handles routine tasks, freeing employees for higher-value contributions.
- Data integrity: Ensure and communicate the accuracy, timeliness, and unbiased nature of data feeding AI systems. Transparency builds confidence.
- Platform security: Address cybersecurity concerns with regular audits and clear privacy measures, protecting sensitive information.
Additionally, trust in AI reliability is vital. Encourage a balance between leveraging AI insights and applying human judgment.
Implementing AI requires the support of the entire workforce. Data leaders must lead change management efforts, cultivating a data-driven culture that addresses concerns and highlights AI’s benefits. Proving out smaller, high-impact use cases can demonstrate value and secure stakeholder buy-in at all levels.
Crucially, responsible AI practices must be at the forefront. By prioritizing ethical AI use and addressing potential risks, data leaders can architect a data-driven future that unlocks AI’s full potential across the enterprise.
The path to scale
The journey to AI at scale is paved with seven critical challenges:
- Integrating AI with business strategies
- Building strong data foundations
- Overcoming technical complexities
- Balancing innovation speed with practicality
- Driving effective change management
- Addressing the AI talent shortage
- Building trust in AI systems
Tackling these challenges head-on is crucial for transforming AI initiatives from isolated experiments into powerful, enterprise-wide capabilities that drive real business value.
The path forward requires a strategic approach focused on:
- Aligning AI with business goals: Integrate AI initiatives seamlessly into your business strategy, ensuring every project drives tangible value and secures stakeholder support.
- Building a robust data and tech foundation: Invest in scalable data infrastructure and flexible architectures that can evolve with AI advancements, fueling innovation across your organization.
- Cultivating AI-ready talent and culture: Develop internal AI capabilities through targeted training programs, while fostering a culture of continuous learning and data-driven decision-making.
- Ensuring responsible AI practices: Implement transparent and ethical AI systems that build trust, mitigate risks, and align with your organization’s values and compliance requirements.
While this journey is complex, you do not have to navigate it alone. Blend’s team of expert data scientists, engineers, and strategists is here to guide you. Blend has helped Fortune 500 companies successfully scale their AI initiatives and is ready to do the same for you.
Blend’s expertise in strategic AI integration can help your organization identify and prioritize AI projects that directly support your business objectives, ensuring maximum ROI and stakeholder buy-in. Learn more about Blend.
Sources
- Evanta, “Top 3 Goals & Challenges for CDAOs in 2024”
- https://www.bcg.com/publications/2023/how-to-attract-develop-retain-ai-talent
- https://www.insightpartners.com/ideas/four-key-ai-challenges-and-how-the-talent-shortage-impacts-them-all/
- AWS, “CDO Agenda 2024”
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