Ruchir Nath is a leader at Dell Technologies, with experience in leading & steering global teams.
With the advent of artificial intelligence (AI) and machine learning (ML), and corporations’ significant investments in hardware and employee training, sales operations/strategy and planning functions are poised for a major transformation. These technologies promise to significantly enhance productivity and decision-making capabilities.
At the highest level, sales operations encompass several critical pillars, each intertwined with the others. However, due to human limitations, time constraints and the need for parallel execution of these activities, decisions often fail to consider the full picture. Here’s how AI/ML can revolutionize each pillar.
Planning
Traditionally, financial planning involves analyzing historical data, market projections, high-level sales productivity KPIs, attrition rates and product mix. This process typically goes through several iterations and involves interlocking with corporate FP&A, business unit finance and sales finance. AI/ML can streamline this by accessing all historical actuals and market data, predicting at a rep level (e.g., risk of leaving, better attrition predictions) and analyzing existing GTM and rep coverage models.
These technologies can process vast amounts of data in parallel, providing well-thought-out plans with clear assumptions. They can also offer scenario analysis, allowing finance and sales ops leaders to tweak variables and see the impact, thus avoiding the “black box” issue that hinders adoption.
Go-To-Market (GTM)
GTM is one of the most complex problems due to the sheer volume of variables, including account sets (large corporations, mid-size, small and medium businesses), reps (tenure, type: inside, outside, specialty, location) and territory and district planning. The goal is to design the most optimal GTM strategy that can be executed rather than a perfect one. AI/ML can connect all these data points more effectively and quickly, providing scenarios and options that can be reviewed and adjusted as needed.
Run The Business
Executive reviews typically provide performance updates and analytics to identify hot spots or issues and suggest actions to ensure targets are met. AI/ML can analyze recent data, conversions, account patterns, sales activity, attrition and GTM changes to offer a comprehensive understanding of underlying issues and potential solutions. This can shift the focus of reviews from being 70% to 80% backward-looking to 70% to 80% forward-looking, highlighting risks and opportunities.
Business Partnership/Financial Controllership
This role encompasses executing all of the above activities in the field, enabling sales calls/outlooks, pricing, rep payout, deal management, pipeline inspection and driving programs/plays with the sales team. AI/ML can enhance these functions by providing real-time insights and predictive analytics, ensuring that decisions are data-driven and aligned with overall business objectives.
For instance, AI/ML can improve predictive analytics on deal management and opportunities based on quotas, account sets and outlier deals. This can help create data-driven guidelines for rep pay reviews and predict potential waterfall deals. Additionally, many CRM companies like Salesforce and Oracle are already integrating AI/ML into their tools, which could further boost seller productivity.
Challenges Organizations Will Face With AI/ML Implementation
Adopting AI/ML technologies is a significant transformation, often complicated by the need for both cross-functional and function-specific solutions. Challenges include vision/executive sponsorship, change management, talent, data quality/democratization, explainable and reliable outputs and training.
To manage transformation projects effectively, setting vision and goals at the top isn’t enough. These must cascade down to the lowest level, ensuring each level understands how the vision and end goals relate to their specific roles. Leaders should not just reiterate the top-level vision but explain its importance and how it impacts each role and what is required of them.
Leaders at every level should build informal relationships with frontline workers or those two to three levels below to gain honest insights into the transformation’s progress, risks and roadblocks. This is crucial as formal reports may mask issues. Access to honest feedback from the doers at lower levels makes a significant difference.
Conduct multiple training sessions at various levels to ensure comprehensive understanding and adoption. Repeated training is necessary, as initial sessions may not reach everyone or be fully retained. The training has to be curated, and the impact explained according to the level of organization it’s being given to. Patience and perseverance are key, as change takes time.
The Future Of AI/ML In Sales Operations
In my opinion, this is just the beginning. Currently, AI/ML is being applied to individual functions such as sales ops, corporate FP&A, supply chain, product engineering and marketing. The real magic will happen when all these functions are connected, providing end-to-end predictive analytics across the company. This integration will allow every decision maker to see the impact of a change or decision on other functions.
For example, AI solutions for CRM can make reps more productive in managing their accounts, enabling them to close deals faster and with higher conversions. This information can be used by AI models in supply chain management to predict upcoming trends and optimize supply chain operations. Similarly, insights on product performance can be shared with pricing teams and product line managers in real time, allowing them to adjust pricing, discounting or create new competitive strategies based on current data.
Final Thoughts
I am personally excited about the potential of this technology. If used correctly, AI/ML can drive incredible productivity and enable decision making at unprecedented speeds. However, this will also require new business processes, management cadences and skill sets.
Future professionals will need to have a broad understanding of multiple functions, such as finance, supply chain and product management, rather than expertise in just one area. Those with varied skills will be better suited for this transformation, and those with experience in a single domain will be critical as an SME in helping data scientists implement and test the models based on their expertise in the domain.
In conclusion, AI/ML has the potential to significantly enhance the efficiency and effectiveness of sales operations. By leveraging these technologies, organizations can make more informed decisions, optimize their GTM strategies and drive better business outcomes. The key to successful adoption lies in ensuring transparency and understanding of the assumptions used by AI/ML models, thus overcoming the traditional barriers to adoption.
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