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Early gains in data and AI are nice, but scaling the business requires a different approach.
Positives are beginning to emerge as organizations take advantage of data transformation and AI capabilities: some 56% of respondents to a recent McKinsey study reported successful adoption of AI in at least one function, an increase from 50% in 2020.
That’s great news, isn’t it? Well, yes, but now comes the hardest part: shifting to a product management mindset that will effectively scale those enterprise-level functional quick wins, while establishing what Forrester calls the knowledge-based business.
As experience confirms, this is easier said than done.
Signs your AI data and operations need a new approach
1. AI data and results do not cross organizational boundaries
Most organizations started their data and AI journeys organically in functional areas, and they continue to operate that way. Greater maturity requires more cross-sectional data.
Mature data and AI products tend to use data outside of functional domains to gain the best insights. Also, reusable components such as customer propensity scores or product-related data pipelines tend to become more prevalent across teams and even at the enterprise level.
A global SaaS organization reported that their marketing department had three models in production, providing significant value for lead generation, but not deploying these models or functionality from the models for sales use cases. After some follow-up discussions, the applicability of these models in sales became clear and, alas, created a missed opportunity to maximize these strengths.
2. You have a “build versus maintain” dilemma and an excessive rotation towards prototypes
Highly skilled technical resources tend to prioritize “new build” over “maintenance,” and they often lack enterprise-level prioritization mechanisms that will maximize value to the organization over time. time. Strong data operations and model operations processes can help overcome this challenge, although building this muscle can take time. New resources with a different focus may be needed to balance both ends of the spectrum between prototypes and AI.
3. Redundant and/or heavily overlapping AI models have proliferated across the enterprise
While data and model catalogs go a long way toward creating greater visibility into what exists today, these tools don’t go far enough to help reconcile and establish reusable components that benefit the whole world. ‘business.
A standard process that takes into account and streamlines larger business needs is a common problem for organizations that are starting to see results from their investments in data and AI.
4. There is a lack of clarity around corporate ownership of AI data and products
Although data governance boards and individuals are focused on data quality and putting in place much-needed controls, confusion remains around ownership of results and strategies for investing in those results.
The generally broader reach of CIOs and CDOs means that these leaders are well positioned to drive change in the field, but lack the skills in their teams and/or framework to really take the bull by the horns.
Product Management: Bridging the Gap Between Operations and Business Priorities
Forrester defines a data product as “a component that ingests and provides data used by an analytics solution for decisions and actions”. This encompasses the results of data operations and AI model operations practices.
Organizations that embrace a product stewardship mindset while applying proven product stewardship principles to these valuable assets will enjoy a greater return on their investments, maximize the technical resources at their disposal, and quickly outpace their peers.
To clarify, the principles of managing products effectively…
- Provide a strategic vision of the company. Shared services functions are quite common in medium and large enterprises; however, the pivot to product management emphasizes the results of these services while providing much-needed capability to manage information requests. The goal is to ensure that data and AI services achieve maximum business alignment through the product concept.
- Connect teams in ways that transcend organizational boundaries. The cross-functional mindset generated by products quickly leads to asset inventories and reusability. These, in turn, benefit the entire enterprise, not just individual functional groups, while enabling more advanced outputs. Often these connections are not natural ways of working without a unifying framework such as a product.
- Facilitate a consistent and standard prioritization mechanism. Many organizations today experience wildly different levels of insight-driven-ness maturity across functional areas. Companies that succeed in aligning these elements to form a advanced knowledge-based company are 8.5 times more likely to report year-over-year revenue growth of at least 20%. Even if limited resources are available, product management can facilitate decisions to scale up certain products that may distribute the wealth of knowledge more evenly or focus on specific areas that require more attention.
Activate a product management policy
To take full advantage of the benefits of a product management approach on top of data and model operations, follow these steps.
1. Use the Boston Consulting Group Growth Share Matrix
First introduced in 1968 by Bruce D. Henderson, this analytical tool has been an important planning tool in brand marketing, product management, strategic management and portfolio analysis in top companies for decades. decades.
Plotting AI data and products on this matrix informs results for service teams and facilitates good discussions with management.
Source: corporate finance institute
A technology customer added a review of each functional group’s growth share matrix as part of their quarterly business reviews. This led to critical “dot-connect” conversations that helped everyone align on enterprise-level priorities and resource management.
2. Invest in a product catalog with supporting roadmaps
Once the growth share view has become available, the next logical step is to ensure that the products are viewable and their near-term investment plans are available via roadmaps.
Simple, engaging roadmaps provide perspective on the timeline and bring products to life in actionable ways. They should become essential inputs to annual and quarterly planning exercises.
3. Establish a product management training plan
Many local university business schools offer product management training courses of all shapes and sizes. Instilling such a mindset takes time and commitment at all levels of the organization. A formal training program signals management’s commitment to the approach and equips employees with the skills they need to be successful with the framework.
4. Use the internal product suite as an incubator for external data products
In the digital age, many organizations are finding new business opportunities in the internal products they create and use internally. Don’t let opportunities to monetize data and AI products pass you by while you run the business headlong. Innovative CIOs and CDOs are often in the best position to drive these types of transformations, which can help the business grow and establish sustainable competitive advantages.
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As the use of AI in business becomes more common, the requirements for scaling the technology become more evident. To avoid a plateau full of expensive prototypes and initiatives that don’t reach their full potential, embrace the fundamentals of product management.
After all, the fundamental ideas behind product management have served countless successful brands well for decades, and will continue to do so for decades.
More resources on product management
How to Maximize Your B2B Digital Marketing Strategy with PIM
The Challenges Product Experience Management (PXM) Can Solve
What sales professionals expect from product management [Infographic]